AI-assisted code is changing into a typical a part of many builders’ every day workflows, and AI-driven instruments are actually straight focusing on the broader software program growth lifecycle.
For occasion, at Amazon Web Services’s 2025 re:Invent in December, AWS launched a brand new class of autonomous, long-running ‘frontier agents’, together with a coding agent, a security agent, and a DevOps agent, every designed to work for hours or days on behalf of growth groups.
VP 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.
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 prolonged past simply human builders. Platforms now have to be constructed not just for people, with wealthy narrative documentation, guides, and tutorials – but in addition for machines.
AI brokers profit from structured, predictable APIs: clear endpoint definitions, constant naming, unambiguous parameter sorts, and machine-readable metadata.
If an API is simple for a human to learn however ambiguous for a device (e.g., inconsistent naming, lacking schema, edge-case behaviors omitted), the primary integration try from an AI-driven device might fail or misbehave.
This means API suppliers ought to deal with machine-readability as a first-class design aim, as a part of the ‘definition of done’ – not elective. In impact, documentation, SDKs, discovery fashions and metadata outputs must be optimized for each human and agent ingestion.
Industry analysis helps that this shift is already underway: whereas 89% of builders now use generative AI of their work, solely 24% of organizations at the moment design APIs with AI brokers in thoughts.
This hole suggests many platforms stay optimized solely for human customers – a misalignment which will price them relevance as agentic growth turns into extra frequent.
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 proceed to develop in recognition, platforms that adapt early to machine-readability will achieve a aggressive edge. Their APIs can be simpler for AI brokers to combine, extra predictable, and extra prone to be the primary profitable goal the agent tries – giving them early adoption benefit.
Teams that wait threat being bypassed, ignored, or inflicting friction that pushes builders (or their agentic copilots) elsewhere.
Over time, ‘vibe coding’ will merely grow to be ‘coding’. The software program growth lifecycle (SDLC) will more and more embody AI brokers as first-class individuals – and platform readiness for these brokers can be a key differentiator.
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