Vibe Coding: Definition, Trends, and Impact in Modern Software Development

Look at vibe coding through Architects Lens

What Is “Vibe Coding”?

“Vibe coding” refers to a new approach to software development where the programmer leans heavily on AI code generation, essentially coding by describing what they want and letting an AI produce the source code . Instead of manually writing every line, the developer provides prompts or instructions (even via voice) to a large language model (LLM) specialized for coding, and the LLM generates the code to implement those ideas . In this paradigm, one “surrenders to the flow,” focusing on high-level intentions while the AI does the heavy lifting of actual coding . The term vibe coding was popularized by AI researcher Andrej Karpathy in early 2025 to describe how advanced coding assistants allow developers to “fully give in to the vibes… and forget that the code even exists” . It’s not a formal methodology like Agile; rather, it’s a slang term for an informal, conversational style of programming with AI – a cultural phenomenon born out of the recent leaps in code-generating AI tools .

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Example of an AI assistant (ChatGPT) generating code based on a natural-language prompt. The user requests a JavaScript function to shuffle a deck of cards, and the AI produces a complete solution with explanations. This illustrates the essence of vibe coding: describe the desired outcome, and let the AI handle the implementation.

Under vibe coding, the developer’s role shifts from writing syntax to guiding and refining the AI’s output . Instead of crafting every algorithm by hand, you might say, “I need a web form with a sidebar, decrease the padding by half”, and accept the changes the AI suggests . Errors are handled by simply feeding the error messages back into the AI and asking it to fix them . In Andrej Karpathy’s words, “I just see stuff, say stuff, run stuff, and copy-paste stuff, and it mostly works” – it doesn’t feel like traditional coding at all . Essentially, vibe coding turns programming into a collaborative dialogue between human and machine. This approach has been made viable only recently, as modern code-generating AIs (like OpenAI’s Codex/ChatGPT, Anthropic’s Claude with coding abilities, or tools like Replit’s AI) have become sophisticated enough to produce substantial blocks of correct code from a simple prompt .

Origins and Recent Trends

Vibe coding emerged from the confluence of AI advancements and developer experimentation. Karpathy (OpenAI co-founder and former Tesla AI lead) jokingly coined the term around February 2025 , after observing that new AI pair-programmers were “getting too good” and enabled a very hands-off style of coding . What started somewhat tongue-in-cheek caught on rapidly in developer communities. Within weeks, the concept went viral: major tech media covered it , Merriam-Webster added “vibe coding” as a trending term, and forums like Reddit lit up with debates on this practice . Y Combinator even released a 30-minute video explainer titled “Vibe Coding Is the Future,” indicating how seriously the startup world is taking this trend .

Enthusiasm for vibe coding is fueled by eye-opening early results. In Y Combinator’s Winter 2025 cohort, fully 25% of startups had codebases that were ~95% AI-generated, an astonishing adoption of AI-driven development . Replit (an online IDE) reports that “75% of Replit customers never write a single line of code”, a statistic CEO Amjad Masad shared to illustrate that many users rely entirely on high-level prompts or AI assistance instead . In other words, a majority of a popular coding platform’s users are effectively vibe coding – building apps by instructing the computer what to do in plain language. Tech entrepreneurs are embracing the approach: for example, one startup founder said that with vibe coding, “if you have an idea, you’re only a few prompts away from a product.” This captures the current vibe (pun intended) in Silicon Valley: turning ideas into working software faster than ever before by partnering with AI.

Developer social media is rife with “vibe coding” anecdotes. Some programmers have even adopted this style in personal projects for years. One developer wrote that since 2023 he has “AI handle about 90% of the code” for projects, using GPT-4 and custom tools to feed entire project context to the AI . He successfully launched small web apps and bots with minimal manual coding, essentially acting as a project manager gluing together AI-generated pieces. Teams are experimenting as well – for instance, Menlo Park Lab, a generative AI startup, is “all in on vibe coding” as a core development practice . Even large enterprises are paying attention. The trend has spawned solutions like TurinTech’s “Artemis”, an AI platform to optimize and clean up AI-written code, backed by $20M in funding to address the inefficiencies that vibe coding can introduce . Early adopters of such tools reportedly include big banks and blue-chip companies looking to harness AI-generated code without its downsides . This flurry of activity shows that vibe coding has moved from a fringe experiment to a mainstream discussion in the software industry in a very short time.

How Vibe Coding Works in Practice

In practical terms, vibe coding means working with AI as a co-developer throughout the software creation process. A typical vibe coding workflow might look like this:

  • Describe the Goal: The developer starts by describing the feature or problem in natural language. This could be done via text prompts or even voice commands. For example: “Build a simple TODO app with user login and a task list.” The key is that the prompt is specific about what is needed, but the developer does not manually write the solution – they delegate to the AI.
  • AI Generates Code: An AI coding assistant (such as ChatGPT, GitHub Copilot, Replit’s Ghostwriter/Agent, or Cursor) takes the prompt and produces code (or config, or other artifacts) that attempts to fulfill the request. The AI might create multiple files, functions, or classes as required. At this stage, the human acts more like a requester or tester than an author.
  • Review and Refinement: The initial AI output is rarely perfect. The developer examines what was generated (at least at a high level or by running it) to see if it meets the need. They might refine the prompt or provide additional instructions to adjust the result . For example, if the UI isn’t quite right, the developer might say, “Now make the login form green and add a remember-me checkbox.” The AI will tweak the code accordingly. This iterative prompt-response loop continues until the software behaves as desired.
  • Testing and Fixing: The developer runs the code. If there are errors or bugs, instead of diving into the code logic directly, a vibe coder will often copy-paste error messages into the AI and ask for a fix . The AI debugs its own code or suggests workarounds. The human might also ask for improvements (e.g., “Optimize this function” or “Simplify this code”). Essentially, the coder and AI pair-program the bug fixes.
  • Deployment and Cleanup: Once the application works, the developer may do a final review or light cleanup. In ideal vibe coding, the motto is “Accept All changes, don’t read diffs” – meaning the coder trusts the AI’s changes without painstaking verification. In practice, for anything non-trivial, an experienced developer will at least sanity-check the critical parts before deploying. The code can then be deployed or handed off, with the knowledge that much of it was machine-generated.

Orchestrating the solution

This workflow highlights that vibe coding is highly conversational and iterative. It blurs the line between coding and talking. As IBM’s technical strategists describe it, it’s like taking inspiration and “convert[ing] it into something” tangible via AI . Developers with strong skills can leverage vibe coding to get a running prototype in hours, by focusing on what the software should do rather than how every line should be written. For instance, IBM’s engineers have used this approach to quickly prototype an app for financial planning just by formulating a good prompt and letting the AI build the first draft . The result: coding feels more like directing or orchestrating the solution, and less like grinding through boilerplate.

no real coding needed

However, it’s important to note that vibe coding in its purest form (as originally defined) implies a somewhat reckless abandon of strict oversight. Karpathy’s own example involved never manually searching through code or carefully reading AI-generated diffs, which he admits leads to code that “grows beyond my usual comprehension” in complexity . In professional settings, most developers won’t go that far – they will still do code reviews or add tests for AI-written code. In fact, as one AI engineer put it, if you are reviewing and testing all AI-produced code until you understand it, “that’s not vibe coding, it’s just software development (with AI assistance)” . This underscores that vibe coding as a “no real coding needed” ideal is mostly applied to rapid prototypes or low-stakes projects. In day-to-day team development, AI code generation is increasingly used, but usually under the umbrella of normal engineering rigor (design reviews, testing, etc.). Next, we’ll examine the key benefits and drawbacks of vibe coding that practitioners and observers have noted.

Benefits of Vibe Coding

Vibe coding has risen in popularity because it offers several compelling advantages, especially when used in the right context. Some of the notable pros include:

Speed and Productivity

Perhaps the biggest draw is the dramatic acceleration in development speed. Seasoned programmers have found that an LLM can produce code “an order of magnitude faster” than a human in many cases . Routine tasks that might take hours can be done in minutes by simply prompting the AI. This enables rapid prototyping and iteration. Teams can go from idea to a functional demo at unprecedented pace – as one developer noted, “if you have an idea, you’re only a few prompts away from a product.” By offloading grunt work to the AI, developers free up time to build more features or try more ideas in the same timeframe. For businesses, this faster time-to-market can be a significant competitive advantage.

Lower Barrier to Entry

Vibe coding opens the door for those with minimal coding experience to create software. Because the approach relies on describing what you want in plain language, even “amateur programmers” or people who aren’t professional developers can get results without deep knowledge of algorithms or syntax . As The New York Times quipped, with modern AI “just having an idea can be enough” to start programming . This democratization means domain experts or designers with ideas can prototype solutions themselves, rather than needing to hand off to a software engineer for every new concept. It can also accelerate onboarding of junior devs – they can produce useful code via AI while still learning the deeper concepts in parallel. Overall, vibe coding can make software development more inclusive and broaden who contributes to coding.

Focus on Higher-Level Design

Because the AI handles the repetitive boilerplate and intricate details, developers can spend more mental energy on high-level architecture, user experience, and problem-solving. An IBM AI advocate observed that developers can now concentrate on “solving real-world complex problems… designing efficient architecture… and fostering innovation rather than routine tasks” . In vibe coding mode, you think about what the software should do, not how to write every piece – which aligns programming more closely with the abstract thinking of an architect or product designer. This shift can increase developer satisfaction by reducing tedious work and allowing them to exercise creativity and big-picture thinking. It’s essentially a move toward a “problem-first” approach, where you let the implementation details emerge dynamically via AI .

Rapid Prototyping & Innovation

Vibe coding is particularly powerful for quickly experimenting with ideas. Because it’s so quick to get a working prototype, teams can cheaply test out features or even whole product concepts. This encourages innovation and risk-taking: you can try something, and if it doesn’t work, you haven’t lost much time. Industry observers note that this ability to “progress with a minimum viable product (MVP), cheaply experiment… and adapt based on feedback” reduces sunk costs and business risk . In other words, vibe coding can function like an innovation sandbox – enabling a “fail fast” mentality where the cost of failure is low. Enterprise architects value this because it means more ideas can be explored without lengthy development cycles or large teams. It also allows for quicker pivots since the initial investment in any single approach is smaller.

Developer Enjoyment and Inspiration

Many who have tried vibe coding describe it as fun and empowering. Karpathy – an expert programmer – said it was “quite amusing” to build a weekend project this way . Senior devs who don’t need AI still enjoy using it to “try out wild new ideas” at high speed, just to see what’s possible . Some liken the experience to having a tireless pair-programmer or an “AI intern” who can generate ideas that you might refine. This can boost developer morale and satisfaction, since they spend more time in creative exploration and less on plumbing code. One IBM engineer remarked that “vibe coding is a thing… you can take inspiration and convert it into something”, implying it can be a very stimulating way to build, turning imaginative prompts into tangible results . For seasoned engineers, it’s a refreshing change of pace; for beginners, it’s incredibly motivating to see immediate results, which can encourage them to learn more.

Drawbacks and Risks of Vibe Coding

Despite its promise, vibe coding also comes with significant challenges and caveats. Tech leaders and developers have been quick to point out the downsides that become apparent especially as projects grow. Key concerns include:

Code Quality and Correctness

AI-generated code is not guaranteed to be good code. Often the initial output is “basic and imperfect” – it may work for a simple case but lack the polish or efficiency a human engineer would aim for. Without careful review, vibe coding can produce solutions that are functionally correct but suboptimal or even flawed. For example, large language models might write an algorithm that is much less efficient than a well-informed human solution, or they might use outdated libraries and bad practices. One startup found that as you generate a lot of code via AI, you also generate “a lot of inefficiencies”, requiring later optimization to improve performance and resource usage . In critical systems, these inefficiencies or hidden bugs can be costly. Therefore, while vibe coding speeds up initial development, teams often must budget additional time for debugging, profiling, and refactoring the AI-produced code to meet production standards.

Maintainability and Technical Debt

Maintainability is a major worry with vibe-coded projects. What happens after the AI pumps out thousands of lines of code? If developers have not kept up with understanding that code, it can become a black box that is hard to maintain. Seasoned engineers warn that “LLMs are great for one-off tasks but not good at maintaining or extending projects” . An AI might introduce convoluted logic or inconsistent coding patterns that make the codebase difficult for humans to navigate later. Over-reliance on AI without refactoring can thus accumulate technical debt – messy, opaque code that “could become unmanageable during scaling or debugging” down the line . This is particularly problematic in a team setting: if one developer vibe-coded a feature and then leaves, the next maintainer might struggle to decipher how it works if no one fully understood it in the first place. In short, the ease of producing code is a double-edged sword – it’s easy to create a large system quickly, but that system might lack the clear structure and documentation that normally comes from a thoughtful design process . Enterprises must be mindful that quick gains in development speed could be offset by long-term maintenance costs if vibe coding is not disciplined.

Loss of Architecture and Skills

Because vibe coding bypasses a lot of manual effort, there’s a risk that developers (especially less experienced ones) won’t learn important software engineering principles. One expert noted that the “ease of use is a double-edged sword… beginners can make fast progress, but it might prevent them from learning about system architecture or performance.” In traditional development, struggling through designing modules or optimizing code teaches valuable lessons; an AI that magically handles it might leave a knowledge gap. From a team perspective, if junior developers rely too much on AI, they may not develop the deep expertise needed to make wise decisions when the AI falls short. Over time, an organization could lose engineering skills or have a false sense of competence. Moreover, the codebases generated might lack a coherent architecture. AI tends to solve locally what the prompt asks for, which might lead to a patchwork design unless a human constantly guides it. Large systems typically need a unifying vision (for scalability, modularity, etc.), and that is something vibe coding doesn’t inherently provide. As a result, teams might end up refactoring an AI-generated prototype significantly to impose a proper architecture after the fact .

Debugging and Trust Issues

While vibe coding makes it easy to get something working quickly, debugging those AI-written sections can be challenging. Developers remark that when an AI produces code you don’t fully understand, tracking down the cause of a bug feels like navigating someone else’s unfamiliar code – except that “someone else” might not have followed logical patterns a human would. The code can be correct in syntax but wrong in logic, or have subtle errors. IBM’s analysis pointed out that AI code can be “dynamic and lacks architectural structure,” making bugs hard to pinpoint . When an error arises, the vibe coding approach of feeding it back to the AI may fix it, but if it doesn’t, the human has to dive into code that they didn’t write. This can be frustrating and time-consuming, potentially eroding the productivity gains. There’s also an inherent trust issue: without reading through AI-generated code, can you be confident it’s doing the right thing (and only the right thing)? Professional developers are trained to be skeptical; many will not deploy code they haven’t reviewed. Simon Willison, an advocate for responsible AI coding, argues that if you don’t review what the LLM wrote, you’re taking a gamble – one he refuses to take for production code . In critical applications, blindly trusting AI output is obviously dangerous. Thus, for serious projects, vibe coding often needs to be tempered with additional verification steps, which reduces some of the speed benefit.

Security and Compliance Risks

Skipping code reviews and bypassing a deep understanding of code can lead to security vulnerabilities slipping through. This is a pointed concern in vibe coding. If the AI uses an insecure function or leaves input validation out, a human may not notice if they’re in “accept all” mode. One engineer cautioned that “security vulnerabilities may also slip through without proper code review” in the vibe coding process . Additionally, using AI tools raises issues of data privacy and licensing – prompts might send proprietary code to an external service, or the AI might generate code that is inadvertently copied from licensed sources. Enterprise IT leaders have to ensure that vibe coding practices comply with their security policies (for example, by using self-hosted or privacy-compliant AI models, and by instituting human review for any AI-generated code that goes into production). In summary, the convenience of vibe coding has to be balanced with traditional software governance: testing, security auditing, and compliance checks remain essential and might even need to be enhanced to catch AI-introduced flaws .

Limits to Usefulness on Complex Projects

Vibe coding works best for relatively self-contained tasks or well-trodden domains (like building a standard web CRUD app or using common frameworks). Its efficacy drops when faced with truly novel or complex software engineering problems. As observed in industry discussions, current LLMs “get lost in the requirements” when projects become large or highly intricate, and can “generate a lot of nonsense content” beyond a certain complexity . In other words, they might do a decent job on the first 70–80% of a typical app (the generic parts), but then struggle with the last mile that involves nuanced business logic, tricky integrations, or performance tuning . Andrej Karpathy himself noted that sometimes the AI “can’t fix a bug” or handle a particular request, and he resorted to applying “random changes” until the problem went away – clearly not a systematic approach you’d want in mission-critical code! Venture capitalist Andrew Chen summed up his experience by saying using the latest AI tools for vibe coding was “both brilliant, and enormously frustrating”, because “You can get the first 75% [of a project] trivially… Then try to make changes and iterate, and it’s like you…” hit a wall .

Teams adopting vibe coding report similar friction: the initial scaffolding is easy, but extending the system with new requirements can confuse the AI or require ever more complex prompts. Thus, vibe coding is not a silver bullet for all programming – for deep algorithmic work, highly optimized systems (e.g. a new database engine), or long-lived software that undergoes many changes, the traditional skills and thoughtful coding are still irreplaceable. In fact, the consensus in the developer community is that vibe coding is great for quick demos and drafts, but delivering maintainable, robust software products still requires human engineering expertise at the helm .

Impact on Teams and Development Culture

The rise of vibe coding is prompting a re-examination of developer roles, team workflows, and the culture of software development:

Changing Developer Roles

As vibe coding tools become commonplace, the role of a developer may shift more towards a curator or architect of AI-generated code. Developers might spend less time typing out boilerplate and more time specifying requirements, integrating components, and verifying outputs. In enterprise settings, we may see new norms where senior engineers act as “editors” of AI-produced code – guiding the AI with better prompts, then reviewing and refining the results for quality . Junior developers could ramp up faster by using AI to handle routine tasks, while they observe and learn from the suggestions. However, there’s also potential for a skill gap to widen: the best developers will be those who not only code well but can also harness AI effectively, knowing when to trust it and when to intervene. This could elevate the importance of software architecture and conceptual design skills over syntax trivia. Some have compared this to managing an “AI pair programmer” – the human must still provide vision and critical thinking.

Team Collaboration and Workflow

Vibe coding introduces new dynamics in collaboration. On one hand, non-engineering team members (like designers or product managers) might be able to prototype ideas themselves with AI, which can then be handed to engineers – this can improve collaboration by giving everyone a more direct creative tool. On the other hand, within a development team, if one person is vibe coding heavily, others need to be brought into the loop on what the AI produced. Code reviews become part detective work to ensure nothing was missed. Teams may establish guidelines, such as: “AI-generated code must be commented or explained by the prompter,” or mandatory peer review for any AI-written module, to maintain transparency. There’s also the aspect of version control and diff management – AI might introduce large changes that are hard to manually inspect. Some teams use tests as a communication mechanism: as in, “if the AI code passes all our tests and linters, we accept it.” In summary, collaboration can remain strong, but processes may adjust: think shorter development cycles (since AI produces code quickly) but possibly longer code review or testing phases to compensate. In large organizations, internal “AI coding” champions or centers of excellence might form to share best practices so that vibe coding is used consistently and safely across teams.

Developer Satisfaction and Culture

The cultural impact of vibe coding is nuanced. Many developers get genuinely excited by the possibilities – it feels like having superpowers or an ever-helpful assistant. This can boost morale, as engineers can accomplish more with less tedium. It also fosters a culture of experimentation: developers might be encouraged to spike out ideas with AI and show results, leading to a more innovative atmosphere. However, there can be negative feelings as well. Some engineers worry that reliance on AI could deskill the profession, turning coding into a commodity or reducing the artistry of it. There’s pride and enjoyment in crafting clean code; if one’s job shifts to just gluing AI outputs, not everyone will find that fulfilling. There can also be frustration when the AI doesn’t do what you want – a feeling of wrestling an unpredictable collaborator. Andrew Chen’s remark about the process being “enormously frustrating” beyond the initial success resonates with many who have tried to build something substantial with AI . In an enterprise context, leaders will need to manage these cultural factors: encouraging use of AI tools but also training developers to maintain their skills and not become overly dependent. Done right, vibe coding can improve developer happiness by removing drudgery; done poorly, it could alienate developers who feel their craftsmanship is being sidelined by “autocomplete on steroids.”

Quality Assurance and Governance

From an IT leadership perspective, vibe coding necessitates updated governance. Continuous integration pipelines may incorporate AI code scanners or additional static analysis to catch issues from AI contributions. Organizations might establish rules about where AI can be used (e.g., prototyping vs production code) and require documentation for AI-generated components – essentially treating the AI like an external contractor whose work needs review. Education is key: developers should be trained in prompt engineering (to get better results) and in reviewing AI code. Some companies even maintain an internal library of approved prompts or use in-house LLMs to keep sensitive code in a secure environment. As Gartner predicts, by 2028 75% of enterprise software engineers will use AI code assistants in their work , so clearly the industry is heading toward widespread adoption. The organizations that thrive will be those that integrate these tools in a way that maintains high standards. Notably, vibe coding does not eliminate the need for disciplines like testing, DevOps, threat modeling, etc. – if anything, those become even more important to verify the deluge of code AI can generate. Enterprise architects should ensure that adopting vibe coding doesn’t bypass the checkpoints that ensure systems are reliable, secure, and aligned with business requirements.

Few words at the end…

(yes, I know… it is lengthy… )

Vibe coding has quickly moved from a buzzword to a real influence on how software is built in 2025. It represents a shift toward more natural and rapid development, where telling the computer what you want takes precedence over hand-crafting how it’s done. This AI-powered coding style offers exhilarating speed and creative freedom, enabling even novices (or simply the time-constrained) to turn ideas into working software with unprecedented ease . For enterprise software leaders, vibe coding promises faster prototyping, increased productivity, and the ability to tackle more projects with the same resources – a compelling proposition in today’s competitive environment. It aligns with the trend of developers focusing on higher-order problems while automation handles routine implementation details .

However, along with these benefits come very real trade-offs. Without prudent controls, vibe coding can lead to bloated, fragile codebases and security or maintainability headaches down the road . The informality that makes it attractive for a quick win is exactly what can make it risky for long-term, collaborative software development. Therefore, the consensus among thought leaders is to approach vibe coding as a tool best used in moderation and with oversight. It’s excellent for hackathons, early-stage prototypes, and accelerating low-stakes tasks. In those scenarios, the ability to “just vibe” and let the AI fill in the blanks can significantly boost innovation and developer enthusiasm. But when it comes to mission-critical products, teams are finding that traditional software engineering rigor must still apply: AI-generated code should be tested, reviewed, and integrated into a well-thought-out architecture – essentially guided by experienced developers to ensure quality .

In summary, vibe coding today is less a strict methodology than a cultural shift in programming. It’s a reflection of how far AI assistance has come, altering the developer experience and workflow. Companies that adopt vibe coding practices stand to gain in agility, but they should do so deliberately: provide training, set clear guidelines (e.g. when to use vibe coding vs. when to write code manually), and leverage tools to mitigate its weaknesses (such as AI code validators and security scanners). By finding the right balance, organizations can harness the “vibes” to their advantage – speeding up development and empowering their teams – without getting lost in the flow. The code may practically write itself now, but the responsibility for delivering maintainable, robust software still firmly rests with us humans . With a healthy mix of excitement and caution, enterprise architects and senior developers can guide their teams through this new era of AI-assisted development, making the most of vibe coding while upholding the standards that professional software demands.

Sources: The information above is synthesized from recent discussions and analyses of vibe coding across industry publications and developer communities, including Ars Technica , Business Insider , New York Times , TechCrunch , IBM’s technical blogs , and first-hand developer accounts , among others. These sources reflect the state of vibe coding as of 2025, capturing both the enthusiasm and the critical lessons learned as this trend unfolds.

Owning Your AI Platform: A Strategic Advantage in the Generative AI Era

Are we content being users of someone else’s AI, or do we want to be owners of our AI destiny?

AI thinker Tweet

Generative AI has stormed into the enterprise mainstream. In the wake of breakthroughs like GPT-4, organizations are racing to infuse AI into products and processes. Yet amid this excitement, IT leaders face a strategic dilemma: should you rely on third-party AI services, or build and own your own AI platform? For many forward-thinking enterprises, owning your AI platform is emerging as a key competitive advantage. By retaining control of both public large language models (LLMs) and proprietary models in-house, organizations can innovate faster without compromising on security, compliance, or flexibility. This approach enables companies to harness powerful public AI services when needed and develop domain-specific AI models – all while keeping sensitive data under their own roof.

 
 

 

Long text ahead !

TL DR :

Owning your AI platform, such as Architech’s Archy, strategically positions enterprises to securely leverage both public and proprietary generative AI models without risking data breaches or vendor lock-in. Archy’s open, integration-friendly architecture allows businesses to seamlessly incorporate AI into existing workflows, significantly accelerating the transition from business requirements to actionable product backlogs—boosting productivity and shortening time-to-market. Unlike closed or SaaS-only AI solutions, Archy gives enterprises full data control, customization capabilities, predictable costs, and flexibility to adopt and evolve AI capabilities at their own pace, ensuring secure, compliant, and future-proof generative AI adoption tailored specifically to enterprise needs.

Owning an AI platform means treating AI infrastructure as a core enterprise asset, much like your cloud or data platforms. Rather than sending proprietary data off to a vendor’s black-box API, you bring the AI to your data on your terms . The result is a secure, open foundation that lets you adopt generative AI at your own pace and integrate it deeply into your business. This article explores why owning your AI platform delivers strategic benefits, how Architech’s open architecture exemplifies this approach, and how it accelerates product development from requirements to reality.

The Strategic Case for Owning Your AI Platform

Building an AI platform in-house or adopting an open platform like Archy (Architech’s enterprise AI assistant) requires upfront investment and vision. But the payoffs are substantial in several key areas:

1

Data Sovereignty and Security

Handing your data to a third-party AI service means entrusting them with your crown jewels – something many CIOs are rightly wary of. A subscription-based AI model leaves your data security in the hands of an external provider that may not prioritize it . In contrast, owning your platform grants full sovereignty over your data, with complete control over where data is stored and how it’s processed . You no longer have to simply trust a vendor to keep sensitive information safe – you maintain end-to-end oversight within your firewall. This ensures strict compliance with regulations like GDPR and HIPAA, since you can enforce all required safeguards internally. In short, AI ownership means no data leaves your trusted environment, eliminating the risk of leaks or unauthorized access .

2

Flexibility and LLM Choice

Another major advantage of owning the platform is freedom from vendor lock-in. Many AI SaaS offerings tie you to one model or cloud service – for example, a proprietary chatbot that only uses its creator’s LLM. This one-size-fits-all approach often fails to align with an organization’s specific needs . By contrast, when you control your AI environment, you have your choice of models and can even deploy multiple LLMs fit for different purposes . You’re free to experiment with the latest OpenAI GPT series, Google’s models, open-source LLMs like LLaMA, or a bespoke model fine-tuned on your proprietary data – whatever best suits the task . Vendor-provided solutions may lock you into a specific model, but an owned platform lets you mix and match or swap models as you see fit . Crucially, those models can also be fine-tuned to your industry’s terminology and datasets for superior results . This flexibility means you can adapt to new AI advancements on your timeline, rather than waiting on a provider’s roadmap or being stuck with yesterday’s tech .

3

Customized Capabilities and Integration

Off-the-shelf AI tools are generic by nature. They might perform adequately out of the box, but they aren’t tailored to your business processes. Owning the platform allows you to purpose-fit AI solutions to your objectives, increasing productivity across departments . You can develop custom AI workflows (for example, automating financial report generation or triaging support tickets) that align with your internal workflows and data. This ability to embed AI deeply into your operations is a huge efficiency gain – AI isn’t a separate tool, it becomes an integral part of your enterprise architecture. In addition, an open platform encourages integration with your existing systems. Rather than a siloed application, the AI services can connect to your data lakes, knowledge bases, and business applications via APIs and event streams. As we’ll see with Archy, an open architecture makes it much easier to weave AI into the fabric of your IT landscape, so teams can leverage AI within the tools they already use.

4

Predictable Cost and Control

From a financial perspective, owning your AI stack can also offer more predictable costs and better ROI in the long run. Pay-as-you-go cloud AI APIs often result in escalating, unpredictable expenses – costs per token or per call can skyrocket as usage grows . Enterprises have faced surprise bills when AI adoption expands faster than expected. By contrast, investing in an in-house or private platform means upfront known costs (infrastructure, licenses, maintenance) but fewer “metered” surprises. It’s easier to budget when you’re not subject to an external provider’s pricing changes or hidden fees . Additionally, you gain full control over scaling – you can optimize hardware and deployments for your workload without vendor-imposed limits or costs. For executive stakeholders, this cost predictability and transparency are important strategic advantages that free up budget for other initiatives.

Owning your AI platform gives you greater control, security, customization, and flexibility than outsourcing this core capability. As one industry analysis put it: “By owning your AI platform, you can have full sovereignty over your data” and shape the platform to meet your needs . It’s a proactive strategy to make AI a long-term, well-governed asset for the enterprise, rather than a quick fix you have limited say in.

Leveraging Public and Proprietary LLMs – Securely

One of the powerful aspects of a platform like Archy is the ability to get the best of both worlds: leverage public LLMs where they make sense, and develop proprietary AI models for your unique needs – all under a secure umbrella. Many organizations want to experiment with state-of-the-art public models (such as GPT-4 or Claude) given their impressive capabilities. At the same time, they have proprietary data and domain knowledge that could benefit from custom-trained models. Owning your platform lets you do both, without exposing your data to outside parties.

 
 

 

 

How is this possible? The key is architectural: instead of sending your data out to an external AI service, you bring the AI to your data. That might mean deploying open-source LLMs on your own cloud infrastructure, or using private endpoints for a hosted model so that the provider never sees your raw data . In Archy’s case, the platform can be deployed within your Azure cloud tenant, and it leverages Azure’s AI services (like Azure ML Studio or AI models you configure) in a way that keeps all data processing internal to your environment . Your prompts, documents, and knowledge bases remain behind your firewall; only the model’s outputs are returned to your applications. This approach mitigates data privacy and residency concerns. As one expert noted, “Data sent externally can violate policies like GDPR”, so avoiding sending sensitive data to external LLM APIs is critical . By containing AI workflows inside your secured cloud or on-premise setup, you eliminate that class of risk.

Security professionals also worry about the opaque nature of public AI services. There’s little transparency into how third-party LLM providers handle or store the data you send them – these algorithms are essentially black boxes . Furthermore, you have no control over where your data might travel or be replicated in the process of using a public API; in the worst case, “data could land on insecure servers” outside your oversight . An owned platform like Archy sidesteps these unknowns. You define the security measures (encryption, access control, monitoring) and retain complete visibility into data flows and model behavior. Archy, for instance, supports deploying in air-gapped or isolated environments and inherits your existing security policies – meaning it conforms to your enterprise’s security posture rather than introducing new vulnerabilities.

Critically, owning the platform doesn’t mean foregoing the benefits of public AI innovation. You can still tap into cutting-edge models – but you do so in a controlled, hybrid manner. For less sensitive tasks or publicly available data, you might call an external API via a secure gateway. For anything involving regulated or proprietary data, you use your in-house model or a fine-tuned version of an open model running locally. The platform orchestrates this seamlessly. Over time, you might even reduce reliance on third-party models as your proprietary LLMs become more capable, trained on your data. Many enterprises pursue this hybrid strategy initially: use a mix of vendor models and internal models, then gradually specialize. With Archy’s open architecture, such a hybrid approach is supported by design – you are free to plug in new models or switch providers as the landscape evolves. The result is AI agility with security, allowing you to leverage the best models available while rigorously protecting your data assets.

Open Architecture and Integration at Your Own Pace

Technology leaders know that big-bang platform replacements rarely succeed. One of the strengths of owning your AI platform is the ability to adopt and evolve AI capabilities at the pace that suits your organization. Architech’s design philosophy embraces open architecture and deep integration, ensuring that introducing AI augments your existing ecosystem rather than disrupts it. In practical terms, an open architecture means the AI platform can plug into your current tools, data sources, and workflows with minimal friction – so you can start with incremental use cases and gradually expand.

Archy

Archy demonstrates this with its integration capabilities. It is not a standalone app that forces you to migrate your data or abandon established tools. Instead, Archy acts as an AI layer that sits on top of and alongside your project management and product development tools. For example, Archy integrates natively with Atlassian Jira as a plugin . Product managers and teams can continue using Jira for tracking backlogs and issues, while Archy works within Jira to provide AI-driven assistance. This means zero context-switching – the AI comes to your workflow. Similarly, Archy connects with Confluent for real-time data streaming , allowing it to incorporate live data and events into its insights. It also supports custom API integrations , meaning if you have other internal systems (say a requirements database or a knowledge wiki), Archy can hook into those to gather input or push updates. The composable, open architecture makes it possible to integrate Archy with your existing infrastructure “at your own pace, ensuring smooth adoption.”

This flexible integration is key for enterprise readiness. You might choose to start by deploying Archy for a single team or project as a pilot – plugging it into that team’s Jira project and data sources. Because Archy runs in your environment (for instance, in your Azure cloud), you can limit the scope and safely trial its capabilities. Once it proves value, you can scale up to more teams or use cases, gradually weaving AI assistance into more product workflows. The open architecture supports this incremental rollout; you’re not forced into an all-or-nothing migration. Additionally, because Archy leverages your existing cloud services (like Azure AI and your security framework) , your IT team can manage it with familiar tools and governance processes. This greatly eases adoption – compliance officers, architects, and ops teams are on board because the solution extends what you already have, rather than introducing a completely foreign system.

Contrast this with many “closed” AI platforms where you must use their interface or cloud service in isolation. Those might require you to export or duplicate data into their system, or only work with a narrow set of tools. Such approaches can be rigid and force organizations onto the vendor’s terms and timeline. Architech purposely took a different route: by being integration-friendly, it allows enterprises to embrace AI on their own terms. Whether you want to start small or go enterprise-wide, the platform adapts. As one AI architecture principle states, “Composable, open architecture [lets you] integrate an AI fabric with your existing infrastructure at your own pace” – this ensures your AI adoption is smooth and aligned with your readiness. The bottom line is that an open AI platform becomes a natural extension of your enterprise architecture, not a disruptor. This positions you to continuously evolve your AI capabilities, swapping in new models or connecting new data sources as needed, without re-architecting from scratch.

Accelerating Product Development from Requirements to Reality

Perhaps the most exciting advantage of owning your AI platform is how it can directly accelerate the delivery of customer value. One high-impact use case across industries is using AI to supercharge the product development lifecycle – especially the early stages of requirements gathering, analysis, and backlog creation. Architech’s Archy assistant is purpose-built for this scenario: it dramatically shortens the journey from business idea to actionable product backlog. This capability is a game-changer for product managers and IT leaders striving to deliver features faster and more efficiently.

Consider the traditional process of going from vague business requirements to a concrete list of user stories and tasks. It often involves multiple lengthy workshops, countless email threads to clarify needs, and manual drafting of specification documents or Jira tickets. Weeks can be spent translating stakeholder wishes into well-defined epics and user stories for the development team. Archy compresses this timeline by using AI to automate much of that translation work. It can ingest high-level inputs – project charters, requirement documents, even conversation notes – and generate initial epics, user stories, and acceptance criteria that align with the business needs . In essence, Archy serves as an AI co-pilot to the product manager, turning natural language descriptions of goals into structured backlog items.

For example, a product manager could input a plain-language description like “We need a mobile app feature that allows customers to deposit checks by taking a photo, with real-time fraud detection”. Archy would analyze this and produce a set of epics and user stories such as “Mobile Check Deposit Feature” with user stories for capturing check images, verifying check details, integrating a fraud detection service, etc., each with draft acceptance criteria. It might suggest “As a banking customer, I want to deposit a check via the app so that I don’t have to visit a branch” as a user story, with acceptance criteria like “Given a clear check photo, the system recognizes the amount and payer correctly”. These are starting points that the product team can then review and refine. The AI essentially kickstarts the backlog creation, doing in minutes what might have taken days of meetings and documentation.

This acceleration has a direct impact on time-to-market. By quickly generating a detailed and comprehensive backlog aligned with the project goals, Archy allows development to begin earlier . Teams can focus their time on refining and executing the work, rather than on tedious initial writing. One source notes the time efficiency of AI-generated user stories – it “quickly generate[s] detailed user stories, allowing teams to focus on … backlog refinement” rather than starting from scratch. In practice, this means your developers and testers get clarity sooner, and stakeholders see working software faster.

Another benefit is improved quality and clarity of requirements. Archy’s AI doesn’t just speed up writing; it also brings consistency and thoroughness. It suggests standard formats (e.g., the classic As a [user]… I want… so that… form for user stories) and can ensure that acceptance criteria are present for each story. By analyzing lots of data, it can remind teams of edge cases or non-functional requirements they might overlook. For instance, Archy can propose acceptance criteria covering performance (“the system should process a check in under 5 seconds”) or edge cases (“the photo is blurry or the check is already deposited”) that make the backlog more robust. It’s like having an encyclopedic analyst on the team who has seen thousands of projects – the AI draws on that generalized experience to strengthen your requirements. The result is fewer gaps and ambiguities, which in turn leads to fewer misunderstandings down the line. (In fact, Archy’s backlog assistance capability learns from user feedback over time, getting even more attuned to a team’s domain and preferences with each use .)

It’s important to note that this AI-driven backlog creation is applicable across industries. Whether you’re in finance, healthcare, retail, or tech, the challenge of turning business needs into implementable user stories is universal. Archy’s model can be fine-tuned with industry-specific knowledge – for example, using a bank’s past project data to become fluent in banking terminology, or training on healthcare use cases to understand medical compliance needs. Owning the platform makes such fine-tuning feasible, since you can train the AI on your domain data securely. As mentioned earlier, having your own AI allows models to be customized to your terminology and workflows . This means the AI’s output becomes even more relevant and accurate for your field. A telco company could have Archy imbibe telecom-specific user story patterns; a manufacturing firm could feed Archy its repository of requirements for IoT systems. Over time, Archy becomes a knowledgeable assistant steeped in the context of your business.

From a strategic viewpoint, accelerating the requirements-to-backlog phase gives companies a huge competitive edge. It speeds up the delivery of customer value, because you’re spending less time in analysis paralysis and more time coding, testing, and iterating on actual features. In fast-moving markets, the winners are often those who can translate ideas into products quickest. AI-powered product management ensures that good ideas don’t languish in documents – they quickly become actionable plans. And because Archy integrates with tools like Jira, the output of this AI process flows directly into the execution pipeline . There’s no disconnect between planning and implementation; the AI-generated user stories can be immediately taken up by agile teams in their sprints.

To illustrate, imagine a cross-functional team at a large insurance company embarking on a new customer portal. Using Archy, the product leader gathers input from business stakeholders in a brainstorming meeting, then feeds the summarized needs into Archy. Within minutes, Archy produces a draft backlog: epics for user registration, policy lookup, claims submission, each broken down into user stories with criteria. The team reviews this draft the next day, tweaks a few stories, adds a couple of missed requirements – and by the end of the week, they have a complete, groomed backlog ready for development. In the past, this process might have taken 4–6 weeks of back-and-forth. Now, development can start in one week. Multiply that acceleration by many projects, and it translates to a drastically shortened time-to-value for the enterprise’s initiatives. The organization can deliver improvements to customers faster and respond more swiftly to new opportunities or regulatory changes.

Enterprise-Ready Flexibility and Governance

When comparing generative AI platforms, it’s crucial to assess their enterprise readiness – are they built to meet the complex needs of integration, security, and governance in a large organization? Many generative AI tools on the market today originated in the consumer or startup space; they may not check all the boxes an enterprise requires. Architech’s Archy platform, on the other hand, was designed from day one with enterprise concerns in mind. From deployment model to governance features, it aligns with what IT architects and CISO teams expect.

When comparing generative AI platforms, it’s crucial to assess their enterprise readiness – are they built to meet the complex needs of integration, security, and governance in a large organization? Many generative AI tools on the market today originated in the consumer or startup space; they may not check all the boxes an enterprise requires. Architech’s Archy platform, on the other hand, was designed from day one with enterprise concerns in mind. From deployment model to governance features, it aligns with what IT architects and CISO teams expect.

1

Security & Compliance

As discussed, Archy can be deployed as a self-hosted solution within your cloud environment, providing complete control over data access . All data processed by Archy stays within your network boundary, and you can enforce encryption, identity management (e.g. Azure AD integration), and any specific compliance controls you need. This is a stark contrast to many SaaS AI tools where you have limited visibility into their security measures. Archy also supports data isolation – teams or departments can operate in siloed workspaces if needed, preventing any inadvertent data mixing between projects . This is useful for maintaining Chinese walls or dealing with different data classification levels. Because it leverages your existing cloud’s security infrastructure, Archy effectively “inherits” compliance with standards your organization already meets. For example, if your Azure setup is HIPAA-compliant, Archy running in that same environment can be part of your compliance boundary. This alignment with enterprise compliance regimes means adopting Archy doesn’t introduce new headaches for risk management; it conforms to established policies and can be audited like any other internal system.

2

Integration & Extensibility

Enterprise IT environments are heterogeneous. Archy’s open architecture ensures it can interface with a variety of systems and data sources. In addition to Jira and Confluent, it can integrate with databases, message queues, and other APIs as required . It also provides an API of its own for custom extensions. This level of extensibility is important if you have a unique internal tool that you want Archy to pull data from or push recommendations to. Many other GenAI platforms offer only limited integration points (perhaps a plugin here or there), whereas Archy is positioning itself as a platform you can extend. This is closer to how big enterprise software like ERP systems or ITSM platforms operate – they provide rich integration capabilities so you can fit them into your environment. For AI to be truly transformative, it cannot live in a vacuum; Archy’s integration-minded design is a significant differentiator for enterprise use.

3

Governance & Control

With AI systems, governance includes monitoring usage, managing model versions, and controlling outcomes (preventing inappropriate content, etc.). Because Archy runs within your purview, you can log and monitor all AI interactions using your existing tools (e.g., Azure Monitor, Grafana dashboards) . This means you can trace how a requirement got turned into a user story or who accepted an AI-suggested change – invaluable for audit trails and understanding AI-driven decisions. Archy supports centralized monitoring and analytics so that administrators can oversee performance and usage patterns . If needed, you can establish guardrails – for instance, disallowing Archy from using certain data, or configuring it to require human approval before certain actions. Such fine-grained control is often not possible with off-the-shelf AI services. Moreover, owning the platform gives you control over the AI lifecycle: you decide when to upgrade models, apply patches, or retire capabilities. This avoids situations where a vendor might deprecate a feature you rely on or unilaterally change model behavior. In short, Archy’s model of “full control” means your team is in the driver’s seat for how AI operates within your business .

Finally, it’s worth noting that Archy is a new entrant in this space – it is currently in early adoption stages, with initial pilots rather than public case studies. This is not a drawback so much as a reflection of where the industry is as a whole. Generative AI for enterprises is a very recent development; in fact, studies have shown that over 60% of enterprise generative AI investments are still coming from innovation budgets, highlighting that we’re in the early stages of adoption . Early adopters of Archy are essentially partnering in exploring this frontier. The encouraging part is that Archy’s architecture and feature set were explicitly crafted to meet enterprise requirements, even before large-scale production deployments. It stands on solid, proven foundations (Azure cloud services, Atlassian ecosystem integration, etc.), which gives confidence that it can scale and comply with enterprise demands as usage grows. In other words, Archy has enterprise DNA – even as it continues to mature through real-world use, it was built with the right principles (flexibility, security, governance) from the ground up.

Embracing the AI Platform Advantage

As generative AI becomes a cornerstone of digital transformation, enterprises that own their AI platforms will be better positioned to lead. The strategic advantages are clear: you gain the agility to innovate without waiting on vendors, the security of keeping data in-house, the flexibility to choose and tailor models, and the seamless integration of AI into your existing operations. Instead of being bottlenecked by one-size-fits-all solutions or worrying about data exposure, you can focus on delivering value – like accelerating product development cycles and unlocking new insights – with confidence that your AI is working for you on your terms.

Architech’s Archy platform exemplifies this approach. It provides an open, extensible AI assistant that slots into your enterprise toolkit, enhancing it with generative AI superpowers while respecting the sovereignty of your data and processes. With Archy, product leaders can rapidly turn ideas into execution, and organizations can gradually scale AI adoption from pilot projects to enterprise-wide programs. All of this is achieved in a governed, secure manner befitting serious business applications.

While Archy and platforms like it are still in their nascent stages, the direction is set: enterprise AI will thrive on openness, integration, and ownership. The early adopters taking this path are essentially future-proofing their AI strategy. They will be able to incorporate the latest AI breakthroughs swiftly, because their foundation is adaptable. They will avoid the pain of vendor lock-in or compliance surprises, because they kept control from the start. And they will cultivate invaluable institutional AI knowledge, because their teams work directly with the models and data, not through a distant service.

In an era where every company is becoming an AI company, owning your AI platform is fast becoming not just a technical decision, but a strategic imperative. It’s about building AI capability as a core competency of your organization. Those who do so will have the freedom to innovate and the confidence to push AI to new frontiers, all while safeguarding the values and assets that make their business unique. In the long run, that is a defining competitive advantage. As you consider your enterprise’s AI journey, ask yourself: Are we content being users of someone else’s AI, or do we want to be owners of our AI destiny? The answer could determine who leads and who lags in the generative AI era. Embracing an open, owned AI platform like Archy might just be the key to unlocking sustained leadership and success in this next chapter of enterprise technology. 

Archy Now Available in the Microsoft Azure Marketplace

Microsoft Azure customers worldwide now gain access to Archy to take advantage of the scalability, reliability, and agility of Azure to drive application development and shape business strategies.

 

 

Nenad Crnčec

Nenad Crnčec

 

Zagreb, Croatia — March 15, 2025 — Architech, today announced the availability of Archy for Enterprises in the Microsoft Azure Marketplace , an online store providing applications and services for use on Azure. Architech  customers can now take advantage of the productive and trusted Azure cloud platform, with streamlined deployment and management.

 

Archy is an AI-driven assistant designed to enhance agile project management by automating product backlog management, task prioritization, and predictive analytics. By integrating seamlessly into enterprise project management tools, Archy reduces manual overhead, reduces lowers cost, enhances decision-making, and ensures project success.

 

“With the increasing complexity of digital transformation, businesses require AI-driven solutions to manage their workflows efficiently. By bringing Archy to Microsoft Azure Marketplace, we enable organizations to leverage the power of Azure while benefiting from automated insights, predictive analytics, and seamless project execution,” said Nenad Crnčec, CEO at Architech.

 

Jake Zborowski, General Manager, Microsoft Azure Platform at Microsoft Corp., said, “We welcome Archy to Azure Marketplace, where global customers can find, try, and buy from among thousands of partner solutions. Thanks to trusted partners like Architech, Azure Marketplace is part of a cloud marketplace landscape offering flexibility and economic value while transacting tens of billions of dollars a year in revenues.”

 

The Azure Marketplace is an online market for buying and selling cloud solutions certified to run on Azure. The Azure Marketplace helps connect companies seeking innovative, cloud-based solutions with partners who have developed solutions that are ready to use.

Learn more about Archy for Enterprises and get it now by visiting its page in the Azure Marketplace.

 

 

Event-driven architecture & real-time customer engagement

Revolutionizing Customer Engagement with Event-Driven Architecture

In a rapidly evolving digital landscape, customer engagement has become more critical than ever. Real-time interaction can make the difference between a satisfied customer and a lost opportunity.

This blog post will demonstrate how Event-Driven Architecture (EDA) can be leveraged to build a responsive communication platform that facilitates real-time customer engagement, as presented in our recent webinar hosted in collaboration with Confluent Inc. and Infobip.

Understanding Event-Driven Architecture

Event-Driven Architecture is a software design pattern in which decoupled applications can asynchronously publish and subscribe to events. This design allows systems to react to events in real-time, facilitating responsive and scalable solutions.

 

Key Characteristics of EDA

 

Asynchronous Communication: Components communicate through events without waiting for a response, enhancing system responsiveness.

Decoupling: Producers and consumers of events are independent, allowing for flexible scaling and maintenance.

Scalability: EDA can handle high volumes of events and data, making it suitable for large-scale applications.

 

EDA is a fundamental paradigm in software design that focuses on producing, detecting, consuming, and reacting to events. An event can be defined as a significant change in state, such as a user clicking a button, a sensor sending a temperature reading, or a financial transaction being completed. EDA’s core advantage is its asynchronous communication model, where components, or services, do not communicate directly but rather through events that are published to an event broker or bus.

This decoupling allows services to be independently developed, deployed, and scaled, significantly enhancing the flexibility and resilience of the system. Each event is essentially a message that contains information about a state change, which other components in the system can consume and react to appropriately. This pattern is particularly useful in scenarios requiring real-time processing and responsiveness, such as online financial transactions, real-time analytics, IoT applications, and complex event processing in distributed systems.

 

Components

 

In EDA, the architecture typically involves three main components: event producers, event consumers, and event brokers. Event producers are responsible for detecting changes in the state and publishing these events to the event broker. Event consumers subscribe to specific types of events and execute certain actions when those events are detected. The event broker acts as the intermediary that ensures reliable delivery of events from producers to consumers. This broker is often implemented using message-oriented middleware technologies like Apache Kafka, RabbitMQ, or Amazon Kinesis. 

One of the key benefits of this setup is the inherent scalability and fault tolerance it provides. Since producers and consumers are decoupled, each can scale independently based on demand. Furthermore, the event broker can replicate events across multiple nodes, ensuring high availability and resilience against failures. 

This architecture also supports eventual consistency, where systems are designed to be consistent in the long run, even if intermediate states might temporarily diverge. EDA’s inherent characteristics make it an ideal choice for building responsive, scalable, and maintainable systems in modern software engineering.

Commands and command processors

In the context of EDA, commands and command processors play crucial roles in the system’s operation and workflow management.

Commands are explicit requests to perform a specific action or change a state, typically initiated by a user or an external system. These commands encapsulate all the necessary information required to execute an action, ensuring that the intent and context are clear and unambiguous.

Command processors, on the other hand, are dedicated components responsible for handling these commands. When a command is issued, the command processor validates it, executes the necessary business logic, and then publishes events to the event bus or broker to notify other components about the change in state. This separation of concerns allows for greater modularity and scalability, as command processors can be independently developed, tested, and deployed.

By processing commands asynchronously and generating events, command processors facilitate a responsive and decoupled system architecture, enabling efficient handling of complex workflows and ensuring that different parts of the system remain loosely coupled yet highly cohesive.

Webinar

In our recent webinar, we delved into the intricacies of using Event-Driven Architecture (EDA) to enhance real-time customer engagement. A pivotal aspect of this architecture is the use of commands and command processors, which are essential for handling specific user requests and actions within the system. Commands, such as user registration or purchase initiation, encapsulate all necessary information for executing a particular task. These commands are processed by command processors, which validate and execute the necessary business logic. For instance, when a user signs up on our platform, the command processor handles the registration process, publishes relevant events to the event bus, and triggers subsequent workflows like sending a welcome email or updating the user engagement metrics.

The architecture we presented, in collaboration with Confluent Inc. and Infobip, exemplifies the power of EDA in creating a robust real-time communication platform. Our solution integrates seamlessly with various components, from the CPD Command Processor to the Infobip Adapter, ensuring that every event, from user actions to system notifications, is handled asynchronously and efficiently. This decoupling allows for independent scaling and maintenance of each component, ensuring the platform can handle high volumes of events and data without bottlenecks.

For example, during a marketing campaign, the command processors can manage numerous user interactions in real-time, triggering personalized messages through Infobip’s platform and ensuring immediate and relevant customer engagement. This architecture not only enhances the user experience by providing timely responses but also allows businesses to scale their operations seamlessly, adapting to growing demands and ensuring continuous engagement with their customers.

Core Components of the EDA Solution

The CPD Platform, as illustrated in the architecture diagrams, comprises several core components designed to facilitate real-time customer engagement through Event-Driven Architecture (EDA). 

Confluent

Central to this architecture is the CPD Cluster, which operates on Confluent Cloud, ensuring scalability and fault tolerance. This cluster serves as the backbone for the platform’s event processing capabilities, managing the flow of events between various components and ensuring reliable message delivery. 

Confluent Cloud, built on Apache Kafka, provides a fully managed platform that supports real-time data streaming and event processing at scale. Its architecture ensures high availability and fault tolerance, making it ideal for handling the large volumes of data generated by modern applications. Confluent Cloud offers several key benefits that enhance the capabilities of an EDA system:

  • Elastic Scalability: The platform can scale resources dynamically to meet varying demand, ensuring consistent performance during peak usage periods.
  • Data Durability and Reliability: With features like data replication and automatic failover, Confluent Cloud ensures that event data is preserved and accessible, even in the event of infrastructure failures.
  • Low Latency: Confluent Cloud’s architecture is optimised for low-latency data streaming, which is crucial for real-time applications that require immediate processing and response.

 

CPD – Communication Platform Demo

 

The CPD Platform component itself acts as the orchestrator, consuming actions from the Command Processor and generating events for other services. This modular setup allows for easy extension by adding new event types or integrating additional services without disrupting the existing infrastructure.

If needed, you can increase modularity by developing “CPD Platform” components for specific use case, or set of common use cases. This would go towards orchestrator pattern where you have one service ( one process ) which orchestrates services, commands and events around one use case. 

For instance, integrating a new customer feedback system would involve producing and consuming specific events related to feedback collection and analysis, seamlessly incorporating it into the platform’s workflow.

The CPD User View and CPD Infobip Adapter are pivotal components in delivering a responsive user experience. The User View component consumes events related to user interactions, ensuring the system’s state is updated in real-time and accurately reflects user activity. This is crucial for maintaining an up-to-date user interface and providing immediate feedback to users. 

Extending the User View involves subscribing to new event types or enhancing processing logic to handle additional data, ensuring the platform remains adaptable to evolving business needs. 

 

Infobip

 

Infobip Adapter, on the other hand, interfaces with the Infobip CPaaS, consuming events to send requests and publishing events upon completion of activities. This integration enables the platform to leverage Infobip’s robust communication capabilities for tasks such as sending notifications or processing user responses. Extending the Infobip Adapter can involve incorporating new communication channels or enhancing existing ones, ensuring that the platform can scale and adapt to provide comprehensive real-time customer engagement solutions.

Infobip’s Communication Platform as a Service (CPaaS) integrates various communication channels, enabling businesses to engage with customers through SMS, email, voice, and other messaging platforms. This integration allows for a unified communication strategy that can be tailored to the preferences and behaviours of individual customers. Key aspects of Infobip CPaaS include:

  • Omnichannel Engagement: Infobip CPaaS supports a wide range of communication channels, ensuring that businesses can reach their customers on their preferred platforms.
  • Scalability: The platform can handle high volumes of interactions, making it suitable for businesses with large customer bases or those experiencing rapid growth.
  • Analytics and Insights: Infobip provides tools for monitoring and analysing communication effectiveness, allowing businesses to optimise their engagement strategies based on real-time data.

Integration within a Comprehensive Environment

The broader architecture diagram illustrates how our core solution integrates within a larger ecosystem, interfacing with various internal and external systems.

In real-life, we have more complex environments and systems integrated in one use case. To enable and facilitate this kind of complexities, we use EDA as “glue” that connects all required components.

 

Real-World Applications and Benefits

 

The practical applications of this architecture are vast, particularly in scenarios requiring real-time customer engagement. For example, in financial services, such an architecture can provide immediate fraud detection and personalised financial advice based on real-time data analysis. In e-commerce, it can enhance customer experiences through real-time recommendations and notifications, increasing engagement and conversion rates.

 

Benefits of EDA in Customer Engagement

 

  • Immediate Response to User Actions: By processing events as they occur, the system can provide immediate feedback and interactions, essential for enhancing user satisfaction.
  • Scalable and Resilient: The platform can scale to accommodate growing user bases and data loads, ensuring consistent performance. Kafka’s built-in features for data replication and fault tolerance further enhance system reliability.
  • Integration with Multiple Channels: The ability to integrate seamlessly with various communication channels through platforms like Infobip CPaaS allows businesses to engage customers on their preferred platforms, creating a cohesive and unified customer experience.

 

Conclusion

 

Event-Driven Architecture, as exemplified by the CPD platform, offers a robust framework for building scalable, real-time communication systems. By leveraging the strengths of Confluent Cloud and Infobip, businesses can create highly responsive systems that not only meet the demands of modern customer engagement but also provide a flexible foundation for future growth and innovation. This architectural approach not only addresses current business needs but also positions organisations to adapt to the rapidly changing digital landscape, ensuring long-term success and customer satisfaction.

Architecture Observability

Enhancing Software Architecture with vFunction: Insights from Amir Rapson

I recently had the pleasure of moderating an incredible tech-talk session with Amir Rapson, CTO and Founder of vFunction. Tech-session was organised TBC Bank from Georgia. We delved deep into the nitty-gritty of architectural observability and its role in tackling technical debt. If you couldn’t join us, here are the highlights and key takeaways from our discussion.

Amir Rapson

Amir Rapson co-founded vFunction and serves as its CTO, where he leads its technology, product and engineering. Prior to founding vFunction in 2017, Amir was a GM and the VP R&D of WatchDox until its acquisition by Blackberry, where Amir served as a VP of R&D. Prior to WatchDox, Amir held R&D positions at CTERA Networks and at SofaWare(Acquired by Check Point). Amir has an MBA from the IDC Herzlia, and a BSc in Physics from Tel-Aviv University.

Amir Rapson

Understanding Architectural Observability and Technical Debt

Amir kicked off the session by emphasising the importance of architectural observability. It’s not just about keeping an eye on our code; it’s about truly understanding the architecture of our systems. This awareness helps us pinpoint and address technical debt early, keeping our software scalable and resilient.

One of the biggest eye-openers for me was how Amir linked technical debt directly to business outcomes. It’s easy to think of it as just a developer’s problem, but in reality, unchecked technical debt can slow down our engineering velocity and lead to more frequent outages, impacting the bottom line.

View
Architectural discovery and visualisation

 

Leverage AI-based architecture discovery and mapping to understand the architectural health of applications. Explore different visualizations and identify the most impactful areas of improvement in minutes.

Map
Dependency mapping

 

Discover complex and dynamic relationships among classes, transactions, files, beans, synchronization objects, sockets, stored procedures, and other resources highlighting areas for improvement.

Debt
Architectural technical debt analysis

 

Architectural technical debt highlights the compromises made during software design and development that affect the system’s core architecture.

Alert

Prioritisation and alerting

 

Incorporate a prioritized task list into every sprint to fix key technical debt issues, based on your unique goals for the domain, including application scalability, resiliency, engineering velocity and cloud readiness.

Monitor

Architectural drift monitoring

 

See what’s changed in your architecture since the last release — which domains were added, what dependencies were introduced — and configure automated alerts for new architectural events like new dependencies, domain changes, and cloud readiness issues. 

R&A

Remediation and automation

 

vFunction supports transformations for updated frameworks, automates code extraction for microservices creation, and generates the necessary APIs and client libraries for newly created microservices.

Integrate
Integration and exporting

 

Export architectural data and analysis results into any system for any purpose, as well as task lists for use in Jira and Azure DevOps. Simplify deployment in cloud ecosystems via licensing and marketplace integrations.

Refactoring: Beyond Service Extraction

We talked about the common challenge of extracting services from monolithic applications. Amir made it clear that it’s not enough to just pull out services. To do it right, you need to refactor the monolith to improve its internal structure, ensuring that the new services don’t end up with messy dependencies.

This approach to refactoring is crucial for achieving a modular architecture. It’s all about breaking down the monolith in a way that each piece can operate independently without creating a tangled web of dependencies.

vFunction platform

Architecture observability and technical debt management

Tools and Techniques for Better Architecture

Amir shared some fantastic insights on using tools like vFunction in conjunction with SonarQube. The integration of these tools can significantly enhance our ability to manage code quality and architectural dependencies. He explained the importance of combining dynamic and static analysis to get a full picture of our software architecture.

 
 

 

 

Dynamic analysis helps us understand the real-time interactions and method calls in our applications, while static analysis gives us a snapshot of dependencies and code structure. Using both, we can gain comprehensive insights and make informed decisions about refactoring and improvements.

 
 

 

 

Boosting Engineering Velocity and Business Confidence

One of the big topics we covered was how technical debt affects engineering velocity. Amir pointed out that exhaustive testing and regression testing due to technical debt can really slow us down. This resonated with me because it’s something many teams struggle with. He shared strategies to balance thorough testing with maintaining high velocity, such as reducing regression testing and improving deployment frequency.

 
 

 

 

We also discussed regaining business confidence after tackling technical debt. It’s not just about fixing the code; it’s about demonstrating improved reliability and reduced risk to the business. Amir emphasized the importance of showing tangible metrics to the business to rebuild trust and move towards quicker, independent deploys.

 
 

 

 

Good vs. Bad Architecture

We all know bad architecture when we see it—a complete mesh of services with no clear structure. Amir highlighted the characteristics of good architecture, like having minimal interdependencies and clear separation of concerns. He warned against the pitfalls of creating a service mesh, which can lead to a complex and hard-to-maintain system.

Instead, Amir advocated for layered architectures that maintain modularity and reduce complexity. This way, each layer has a specific role, and dependencies are clear and manageable.

 
 

 

 

Optimizing Database Interactions

We also touched on database usage and how vFunction can help optimize it. Amir explained how the tool provides insights into whether we should use relational or non-relational databases and when to implement caching strategies. These insights are invaluable for improving database performance and overall application efficiency.

Practical Implementation and Continuous Improvement

Integrating vFunction into the software development lifecycle was another key point. Amir stressed that vFunction should be used continuously to manage technical debt and maintain good architecture. He shared metrics that teams can track, like delivery times, recovery times, and the number of incidents, to measure the success of their efforts.

Final Thoughts

This tech-talk with Amir was a deep dive into the heart of software architecture. It reinforced the idea that managing technical debt and maintaining good architecture are ongoing processes that require continuous effort and the right tools. By integrating solutions like vFunction, we can achieve better business outcomes, improve engineering efficiency, and build scalable and resilient software systems.

Addressing technical debt isn’t just about cleaning up code; it’s about ensuring long-term success and fostering innovation. I’m excited to see how these insights and strategies will help us all navigate the complexities of modern software development.

Thanks for reading, and here’s to building better software together!

 
 

 

 

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