Production-Ai

AI coding agents broke the old headcount-equals-output math. Small senior teams now outship larger orgs, and verification is the new bottleneck.

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.

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.

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.

How I used autoresearch to run 65 autonomous prompt optimization iterations on a production LLM agent, cutting it 28% while retaining 98% output quality.