Designing REST APIs for the age of AI agents - Boyan Mihaylov - NDC Copenhagen 2026
The talk argues that REST APIs, originally designed for human developers, must fundamentally adapt to serve AI agents and LLMs as primary consumers. To ensure reliability and discoverability in an AI-driven world, API designers must focus on structured documentation (OpenAPI), robust error handling, maintaining consistency, implementing adaptive rate limiting, and considering new standards like the Model Context Protocol (MCP) for web integration.
Key takeaways
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AI Agents are a New Consumer
21:45
The rise of AI tools means that API consumers are shifting from human developers to autonomous agents. These agents will interact with APIs by generating requests and chaining calls, requiring the API to be machine-readable and reliable.
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Documentation is Critical for AI
26:45
The OpenAPI standard (JSON or YAML specification) is crucial. Beyond simply documenting endpoints, developers must add rich metadata about the API's purpose, constraints, and potential errors to minimize agent hallucination.
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Prioritize Error Handling
30:30
Instead of basic validation messages, provide detailed error information (e.g., specifying the problematic field and supported options) to allow AI agents to self-correct and retry requests effectively.
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Adopt Adaptive Rate Limiting
35:05
Traditional static rate limiting (e.g., fixed quotas per minute) is insufficient for unpredictable AI agent traffic. Implement adaptive strategies that analyze traffic patterns and adjust limits dynamically to maintain service availability.
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Consider Web MCP
40:05
For web-based services, the Model Context Protocol (MCP) is an emerging standard allowing a webpage itself to expose tools and workflows directly to AI agents, making the entire page functional rather than just relying on backend APIs.