What ‘vibe coding’ means for API platforms and the future of DevRel
AI-assisted code is becoming a standard part of many developers’ daily workflows, and AI-powered tools are now directly targeting the broader software development lifecycle.
For example, at Amazon Web Services’ re:Invent 2025 in December, AWS launched a new class of autonomous, long-lived “border 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.
Vice President for Developer Ecosystem and DevX at Vonage.
These developments reflect a broader shift: Organizations are increasingly viewing AI not just as an assisted input tool they can use to develop proofs of concept and prototypes, but also as a partner they can use throughout the development lifecycle, capable of generating integration code, handling security reviews, or even automatically triaging operational issues.
As a result, what started as “dynamic coding,” the informal, exploratory use of AI code generation, is quickly becoming intrinsic to many teams’ development practices.
A new dual audience for API platforms: humans and AI agents
With AI agents actively participating in code creation, testing, and operations, the “consumer” of your API platform now extends beyond just human developers. Platforms must now 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 to read for a human but ambiguous for a tool (e.g. inconsistent name, missing schema, extreme behaviors omitted), the first attempt to integrate an AI-driven tool may fail or misbehave.
This means that API providers must treat machine readability as a top design goal, as part of the “definition of done” – not as an option. Indeed, documentation, SDKs, discovery models and metadata outputs must be optimized for human and agent ingestion.
Industry research confirms that this shift is already underway: while 89% of developers now use generative AI in their work, only 24% of organizations are currently designing APIs with AI agents in mind.
This gap suggests that many platforms remain optimized only for human users – a misalignment that may cost them relevance as agent development becomes more common.
What this means for API-first and DevRel platforms
Platform teams must now consider AI readiness as a core part 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 much more likely to produce correct integration code on the first attempt, reducing friction for both humans and their AI counterparts.
The discovery surfaces exposed by the platforms are also larger than before.
Auto-generated OpenAPI or Swagger schemas, structured metadata endpoints, and user-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 an engineering byproduct.
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 becomes 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 reevaluate how they measure impact in a world where agents are driving an increasing share of platform usage.
Metrics like forum activity, completed tutorials, or SDK downloads may no longer provide a complete picture of adoption. Instead, teams should track how often AI systems attempt integrations, how often those integrations succeed, and where agent-driven errors occur.
This shift opens up a new responsibility to provide AI-enabled tools that guide both developers and their co-pilots. Machine-readable reference documentation, prompt templates, sample 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 start viewing agents as a prime audience. This means investing in predictable pattern design, clear behavioral patterns, and error handling that is explicit enough for an agent to learn from.
Supporting developers now means supporting both the humans who build and the AI systems that help them do it.
First mover advantage for “AI-Ready” APIs
As agentic AI tools continue to gain popularity, platforms that quickly adapt to machine readability will gain a competitive advantage. 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 an advantage in early adoption.
Teams that wait risk being bypassed, ignored, or causing friction that pushes developers (or their agent co-pilots) elsewhere.
Over time, “vibe coding” will become just “coding.” The software development life cycle (SDLC) will increasingly include AI agents as first-class participants – and platform readiness for these agents will be a key differentiator.
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