10 articles about ai-agents - lessons from building and scaling real software.
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.
When an agent fails, harness engineering gives you four levers (Context, Tools, Loop, Governance) to find which one broke in under a minute.
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.
What 'supported by a fleet of agents' means in practice: which tasks automate, which don't, and where the ROI breaks down. Evidence from Stripe, Coinbase, Ramp, and Shopify.
Most AI agent governance advice targets boards, not builders. Three failure patterns, real TypeScript examples, and what a CTO should do Monday morning.
How I used autoresearch to run 65 autonomous prompt optimization iterations on a production LLM agent, cutting it 28% while retaining 98% output quality.
AI agents produce better output when the codebase is ready for them. Here are the four dimensions of codebase readiness that account for most of the gap.
AI agents are stateless by default. Here's how memory systems actually work, covering the storage patterns, lifecycle triggers, and architecture behind agents that remember you.