Designing APIs for AI agent consumption — MCP, tool calling, and agentic patterns.
View All TagsHow to evaluate AI agents: task-based evals, regression suites, and the difference between offline and online evaluation with a practical rollout plan.
Learn how to scope AI agent API permissions with agent identity, OAuth flows, delegated access, and audit trails with patterns for both interactive and autonomous agents.
How to trace AI agents end to end: what to log, how to connect agent, LLM and tool-call spans, and which metrics predict agent reliability.
A practical guide to agent platform architecture: orchestration patterns, task routing, memory, and observability for running AI agents safely at scale.
What is an agentic enterprise, and how do you actually become one? A practical roadmap covering the architecture, the stages, and what to get right first.
AI agents retry aggressively and don't know when it's safe. Learn the idempotency patterns that keep retries from becoming duplicate actions.
Learn how AI agent memory actually works: the four memory types, vector vs. graph storage, session management, and how to design a memory layer that scales.
How ScheduleMe, a supervisor-and-specialists calendar assistant, performed across six languages: architecture, results, failure modes, and lessons
REST and MCP solve different problems for AI agents. Compare discovery, state, and auth, and see how to govern both from one control plane.
OpenAI function calling and Anthropic tool use carry a model's tool calls, and MCP complements them. Compare schemas, execution flow, and when to use each.
What makes an API agent-ready: clear semantics, machine readable exposure, governance, and observability, plus current standards and how to implement each.
Learn how to write OpenAPI specs that AI agents can use, with clear descriptions, unique operationIds, complete schemas, and agent metadata extensions.