Agent-Architecture

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.

When an agent fails, harness engineering gives you four levers (Context, Tools, Loop, Governance) to find which one broke in under a minute.

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.

Most agent failures aren't model failures. They're harness failures. Here's the 4-lever framework I use to diagnose what broke.

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.

How to build Karpathy's LLM knowledge base into a production AI agent using Claude Code plan mode, an Obsidian vault, and a local LLM.

Most AI agent governance advice targets boards, not builders. Three failure patterns, real TypeScript examples, and what a CTO should do Monday morning.

Logs tell you what happened. Traces tell you why. The three layers of agent observability, and where silent failures actually live.

Most teams evaluate AI agents by vibes. Here are the four layers of evals you actually need to ship agents with confidence.

What happens when you wire an AI agent directly into your Obsidian vault? Here's the setup I use to turn notes into real leverage.

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.

How do AI agents remember things between sessions? Every agent forgets everything when a conversation ends, so how do the best ones seem to know you?

OpenClaw agent architecture explained: How autonomous AI agents like ClawdBot create the illusion of sentience using just inputs, queues, and a loop.