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Why APIs must evolve for AI-driven development
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AI-assisted code is becoming a standard part of many developers’ daily workflows, and AI-driven tools are now directly targeting the broader software development lifecycle.
For instance, at Amazon Web Services’s 2025 re:Invent in December, AWS launched a new class of autonomous, long-running ‘frontier agents’, including a coding agent, a security agent, and a DevOps agent, each designed to work for hours or days on behalf of development teams.
Alex BarnettSocial Links NavigationVP for Developer Ecosystem and DevX at Vonage.
These developments reflect a broader shift: organizations are increasingly seeing AI not just as an assistive typing tool they can use to develop proof-of-concepts and prototypes, but as a partner they can use throughout the development life cycle, capable of generating integration code, handling security reviews, or even triaging operations issues automatically.
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As a result, what started as ‘vibe coding’, the informal, exploratory use of AI code generation, is rapidly becoming intrinsic to many teams’ development practices.
A new dual audience for API platforms: humans and AI agents
With AI agents actively participating in creating code, testing, and operations, the ‘consumer’ of your API platform is now extended beyond just human developers. Platforms now need to be built not only for humans, with rich narrative documentation, guides, and tutorials - but also for machines.
AI agents benefit from structured, predictable APIs: clear endpoint definitions, consistent naming, unambiguous parameter types, and machine-readable metadata.
If an API is easy for a human to read but ambiguous for a tool (e.g., inconsistent naming, missing schema, edge-case behaviors omitted), the first integration attempt from an AI-driven tool may fail or misbehave.
Are you a pro? Subscribe to our newsletterContact me with news and offers from other Future brandsReceive email from us on behalf of our trusted partners or sponsorsBy submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.This means API providers should treat machine-readability as a first-class design goal, as part of the ‘definition of done’ - not optional. In effect, documentation, SDKs, discovery models and metadata outputs should be optimized for both human and agent ingestion.
Industry research supports that this shift is already underway: while 89% of developers now use generative AI in their work, only 24% of organizations currently design APIs with AI agents in mind.
This gap suggests many platforms remain optimized solely for human users - a misalignment that may cost them relevance as agentic development becomes more common.
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What this means for API-first platforms and DevRel
Platform teams should now view AI readiness as a core element of API design. This means greater discipline around endpoint consistency, schema stability and naming conventions, supported by documentation and metadata that can be consumed programmatically.
When these foundations are in place, machine agents are far more likely to produce correct integration code on the first attempt, which reduces friction for both humans and their AI counterparts.
The discovery surfaces that platforms expose also matter more than before.
Auto-generated OpenAPI or Swagger schemas, structured metadata endpoints and machine-friendly SDKs give agents the clarity they need to understand available functionality and select the right paths through an API. In practice, this means treating metadata as a strategic asset rather than a by-product of engineering.
Teams should also anticipate that first impressions will increasingly be shaped by automated agents rather than human developers.
The moment an AI agent successfully returns a 200 OK is becoming as important as a developer reading a polished README, because it determines whether the agent continues to attempt deeper integration or quickly turns elsewhere.
For DevRel and developer experience teams
Developer Relations and DevX teams will need to reassess how they measure impact in a world where agents initiate a growing share of platform usage.
Metrics like forum activity, tutorial completions or SDK downloads may no longer offer a full picture of adoption. Instead, teams should track how often AI systems attempt integrations, how frequently those integrations succeed and where agent-driven errors occur.
This shift opens up a new responsibility to provide AI-friendly tooling that guides both developers and their copilots. Machine-readable reference documentation, prompt templates, example snippets designed for code generation and environments that help teams audit or refine AI-generated code will all become increasingly useful.
Above all, DevRel teams should begin to think of agents as a first-class audience. That means investing in predictable schema design, clear behavioral models and error handling that is explicit enough for an agent to learn from.
Supporting developers now means supporting both the humans doing the building and the AI systems helping them do it.
First-mover advantage for ‘AI-Ready’ APIs
As agentic AI tools continue to grow in popularity, platforms that adapt early to machine-readability will gain a competitive edge. Their APIs will be easier for AI agents to integrate, more predictable, and more likely to be the first successful target the agent tries - giving them early adoption advantage.
Teams that wait risk being bypassed, ignored, or causing friction that pushes developers (or their agentic copilots) elsewhere.
Over time, ‘vibe coding’ will simply become ‘coding’. The software development lifecycle (SDLC) will increasingly include AI agents as first-class participants - and platform readiness for those agents will be a key differentiator.
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TOPICS AI Alex BarnettSocial Links NavigationVP for Developer Ecosystem and DevX at Vonage.
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