This cluster covers what it takes to make your APIs consumable by AI agents — from MCP and tool calling to permission models, idempotency, and agent observability. As AI agents become a first-class API consumer, these guides give you the architecture patterns and design principles to build APIs that are ready for the agentic era.
How to design, document, and expose APIs so AI agents can discover, understand, and use them reliably.
When to expose capabilities as REST API endpoints vs MCP tools — and how to support both without duplication.
How function and tool calling works in modern LLMs, schema design best practices, and error handling.
How AI agents consume OpenAPI specs and how to optimize your spec for LLM-based API discovery and use.
Why AI agent tools must be idempotent and how to design APIs that won't cause problems when retried.
How to scope, limit, and audit what AI agents are allowed to do through your APIs — safely.
Architecture guide for building an internal platform that teams use to deploy and govern AI agents.
Patterns for managing conversation history, long-term memory, and shared state in agentic systems.
How to design systems where multiple AI agents collaborate, delegate, and coordinate via APIs.
How to trace, monitor, and debug AI agents in production: what to instrument and what to log.
How to measure agent quality, reliability, and accuracy in production environments systematically.
What it means for an organization to become agentic, and the API infrastructure needed to support it.
Make your APIs agent-ready with WSO2
WSO2 API Manager supports MCP gateway, tool discovery, and agent-scoped authorization so your APIs are accessible and governable by AI agents.