Mastra

ACP is the layer that lets my Mastra agent hand real repo work to Claude Code instead of stopping at advice.

Most AI demos hand one agent the whole job. When the output touches reputation, money, or outbound email, use a workflow instead of an agent loop. Here's where the boundary goes, built with Mastra.

Large tool catalogs break agent context. Tool search fixes that by letting agents discover and load only what they need.

One inbox agent should not classify, research, score, route, and draft replies in one loose loop.

Prompt-based approval gates fail because the model decides whether to ask. Mastra's requireApproval primitive removes that decision entirely. Here's how to implement it.

How to wire human-in-the-loop on tool calls — and why system prompt instructions like "always ask before sending" don't actually hold.

The multi-agent supervisor pattern in Mastra: eight specialist agents on one local LLM, one supervisor, structural trust boundaries — using TypeScript.

Add an LLM-as-judge scorer to a Mastra agent, catch a fabricated action item your tests would never flag, and fix the prompt — no custom infra.

A practical guide to building an AI agent with Mastra that researches contacts, schedules follow-ups, integrates with Slack, and uses layered memory.

Learn to build your own AI agent that actually does work for you, not just answers questions.