Community Spotlight

Clément Garrigue's path into software started from the outside. As a non-technical entrepreneur, he spent years looking for a CTO before reaching a blunt conclusion: in an economy where every asset is digital, anyone who can't code is locked out of building their own ideas. Later, advising large corporate innovation teams, he watched the word "innovation" generate more anxiety than action, precisely because execution still depended on technical skills most teams didn't have.

The thread that has run through everything since is a single question: not what we call these tools, but who gets to build with them, and who controls the stack they run on.

Around 2021, through the emerging no-code community in France, he saw how much of that gap the new tools could close. He joined a no-code agency, became Secretary General of the French union for no-code professionals, and founded NoCode Summit, which grew internationally across three editions before the AI wave reshaped the entire field.

Terminology still matters to him. He groups generative, agentic and voice AI with no-code, low-code and vibe coding under one umbrella, "abstraction technologies," since each hides the complexity of code rather than removing it, always complementary to coding. But he moves past labels quickly: what counts is what people build, and who gets to build it.

He traces a clear evolution in the tooling. Pioneering platforms like Bubble, Airtable, and Make opened the door, even if they were modular and rarely full-stack, often requiring several tools stitched together to ship a product. Today's generation goes further: more powerful, more scalable, and increasingly built for security and governance, letting business teams turn their own ideas into working software while IT shifts from bottleneck to enabler. The latest inflection, voice and agentic AI, pushes this into natural-language programming: from keyboard, to chat, to voice, and eventually to intent.

Garrigue is candid about the governance tension this creates. Early skeptics came around as the tools matured; enterprises that dismissed the category early were in the room a few years later. The pressing questions now are what these tools can really do for society and the planet, how to keep humans in the loop, and how to find meaningful use cases through the hype.

That conviction is what he is channeling into his next chapter: Build It Summit, a new event he is bringing to Paris on December 1-3, co-located with FOST Paris. Its focus is deliberately forward-looking, built around private, hands-on labs where companies bring real challenges across growth, resilience, digital sovereignty, and carbon footprint. He frames it as a shift in who gets to build, and ties it to Europe's push for independence from big tech, citing the EuroStack movement, EU Inc, and the Build in Europe campaign as sources of inspiration. That urgency only sharpened this weekend, when a leading US AI provider had to suspend its most capable model worldwide following a US government order, a reminder that infrastructure built elsewhere can be switched off by decisions no European builder controls. The goal, he says, is less a conference than a working room for the people actually building it.

Sivasai Nadella is a Specialist Software Engineer at Amgen, whose work focuses on designing enterprise systems that operate reliably in complex and automated environments. As organizations adopt AI-powered workflows, event-driven architectures, and real-time decision systems, he is exploring how APIs must evolve to support a new generation of enterprise automation.

Historically, APIs have functioned as integration mechanisms. Applications request information, systems respond, and business logic remains largely within the consuming application. That model works well when humans remain at the center of decision-making.

Autonomous systems introduce a different set of requirements.

An AI-powered workflow may need to respond to inventory changes, compliance alerts, supply chain disruptions, or customer events as they happen. Instead of waiting for requests, systems need to react to events, evaluate policies, and coordinate actions across multiple platforms without constant human intervention.

This is moving API design beyond traditional endpoint management. Event streams, workflow coordination, policy enforcement, and real-time decision boundaries are becoming important parts of the API landscape. The question is no longer whether systems can communicate, but whether they can communicate in ways that support reliable automation at enterprise scale.

Nadella examines how APIs are evolving from integration tools to coordination systems. Events provide awareness of changing conditions, while policies establish the guardrails that keep automated actions aligned with business objectives, security requirements, and operational controls.

The lesson for API teams is that autonomous systems require more than connectivity. They require APIs that can communicate context, enforce boundaries, and support workflows that operate continuously across distributed environments. As enterprises expand automation and AI adoption, those capabilities will become core foundations for reliable, governed, and scalable systems.

API Feed

Big Story

Most enterprises run on systems that have been built, changed, and patched over many years. These systems often sit within critical business workflows, contain valuable logic, and support processes the organization relies on every day. The challenge is that the same systems can become difficult to change. Every new integration takes longer, every feature carries more risk, and teams become cautious because they are not always sure what might break.

That is why replacing a legacy system all at once is usually risky. A full rewrite sounds attractive because it promises a cleaner future, but it often concentrates too much risk in a single large migration. If the new system is not ready or if a key dependency is overlooked, the impact can spread across the business. A safer approach is to separate the old system from the rest of the organization before trying to replace it.

APIs make that separation possible. Instead of allowing every application or team to connect directly to the legacy system, teams can place an API in front of it. The API becomes the stable access point. Other systems depend on the API rather than the older system behind it. Once that boundary exists, the team can improve, replace, or rebuild parts of the legacy system without forcing every downstream user to change at the same time.

This is the basic idea behind gradual modernization. New services are built around the legacy core, and traffic is slowly moved from the old system to the new one. For example, one customer workflow, reporting function, or transaction type can be moved first. If it works, more traffic can follow. If there is a problem, the team can route traffic back to the old path. The migration becomes a sequence of smaller, testable moves rather than one high-pressure cutover.

Teams also need to manage the difference between old and new data models. Legacy systems often use naming conventions, business rules, or database structures that no longer fit how the company wants to operate. A translation layer at the API boundary can convert old formats into cleaner structures for new services. This makes newer systems easier to maintain while still using the data and logic within the legacy environment.

The technical work still requires discipline. APIs need to remain backward-compatible so existing users are not suddenly broken. Teams need clear versioning, careful testing, and the ability to run old and new paths side by side for a period of time. Monitoring is also important. Teams need to see which systems are still using the legacy route, where errors occur, and whether the new path performs reliably under traffic.

In sectors like healthcare, banking, insurance, and government, this kind of controlled approach matters even more. Downtime, data loss, or inconsistent records can create serious operational and compliance problems. The goal is not to modernize as quickly as possible. It is to modernize without disrupting the systems the business relies on. APIs help by creating clear boundaries, gradual migration reduces the risk, and monitoring gives teams the confidence to keep moving.

Resources & Events

apidays Munich brings together API architects, platform engineers, and enterprise technology leaders for two days focused on API strategy, platform operations, and AI-driven enterprise systems. The 2026 program includes sessions on API lifecycle management, developer experience, event-driven architectures, and the operational challenges of managing APIs across distributed environments. Designed for teams scaling enterprise API programs, the event combines technical discussions, practitioner-led case studies, and platform engineering perspectives on how APIs are evolving alongside automation and AI-connected workflows. Details →

apidays India brings together API architects, platform engineers, developers, and enterprise technology leaders for two days focused on the next generation of API-driven systems. The 2026 program explores how APIs are evolving to support AI agents, autonomous workflows, and machine-driven ecosystems, alongside sessions on API monetization, security, governance, platform strategy, and AI-driven automation. Designed for teams building and operating modern digital platforms, the event combines technical deep dives, practitioner-led case studies, and real-world discussions on how APIs are being designed, secured, and managed in an AI-connected world. Details →

You can find a list of all Apidays events here

Apply to speak at Apidays Singapore, NY, London, Paris, and more here

Current San Francisco brings together data engineers, platform architects, AI practitioners, application developers, and technology leaders to explore the future of real-time data and event-driven systems. Sessions cover data streaming, event-driven architectures, AI infrastructure, governance, platform operations, and the technologies that enable organizations to move from batch processing to continuously operating systems. Details →

F5's 12th annual State of Application Strategy Report, based on responses from more than 1,100 global IT decision-makers, examines how production AI is reshaping app and API delivery. It finds that 78% of organizations now run AI inference themselves, a signal that enterprises are choosing control over convenience as AI becomes central to operations, while 88% report at least one AI-related operational or security challenge. The number-one strategy in place today, used by 55% of organizations, is managing authentication for AI services and API access, with observability ranking a close second. With organizations relying on an average of 7 AI models and 93% operating in hybrid multicloud environments, the report frames APIs as the connective tissue for AI inference and a growing locus of security and governance work. Read →

Insight of the Week

Enterprises have raced AI agents into production while governance lags behind. A recent Deloitte finding shows that only 21% of organizations have mature governance for autonomous AI agents, while 73% say they are concerned about AI security and data privacy risks. Organizations write principles and check the box, yet rarely build the technical infrastructure to make them enforceable at runtime, where agents actually make decisions and take action.

For the Commute

In this APIdays session, Nick Loh from GovTech Singapore explores how shared API infrastructure can help governments operate as connected systems rather than collections of independent agencies. Using Singapore’s APEX platform as a case study, he explains how centralized API governance, self-service access management, common authentication patterns, and cross-agency data sharing reduce the operational friction that typically slows public-sector integration efforts.

Stay tuned for bold ideas, fresh perspectives, and the next wave of API innovation

-The Apidays Team