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Agent-ready APIs

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.

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Agent-Ready APIs: What They Are and How to Build Them

How to design, document, and expose APIs so AI agents can discover, understand, and use them reliably.

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REST APIs vs MCP for AI Agents

When to expose capabilities as REST API endpoints vs MCP tools — and how to support both without duplication.

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Tool Calling for LLMs

How function and tool calling works in modern LLMs, schema design best practices, and error handling.

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OpenAPI Specs for AI Agents

How AI agents consume OpenAPI specs and how to optimize your spec for LLM-based API discovery and use.

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Designing Idempotent Tools for AI Agents

Why AI agent tools must be idempotent and how to design APIs that won't cause problems when retried.

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Permission Models for AI Agents

How to scope, limit, and audit what AI agents are allowed to do through your APIs — safely.

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Building an Agent Platform

Architecture guide for building an internal platform that teams use to deploy and govern AI agents.

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Memory and State Management for AI Agents

Patterns for managing conversation history, long-term memory, and shared state in agentic systems.

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Multi-Agent Systems: Architecture and Patterns

How to design systems where multiple AI agents collaborate, delegate, and coordinate via APIs.

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Agent Observability

How to trace, monitor, and debug AI agents in production: what to instrument and what to log.

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Evaluation for AI Agents

How to measure agent quality, reliability, and accuracy in production environments systematically.

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Becoming an Agentic Enterprise

What it means for an organization to become agentic, and the API infrastructure needed to support it.

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