# ai-agents

Simon Willison finally defines Agentic Engineering after 11 hands-on chapters: using coding agents to help build software. The interesting part is why he needed the patterns first before the simple definition felt earned.

Simon Willison's Agentic Engineering Patterns, Chapter 3: AI should help us ship better code, not worse. Technical debt cleanup costs near zero now, architecture decisions can be validated with prototypes instead of guesses, and quality compounds over time.

Simon Willison's First Run the Tests pattern is four words with three effects: the agent learns the test command, gauges codebase size, and shifts into a test-maintenance mindset. Tiny instruction, large behavioral nudge.

Box CEO Aaron Levie argues that as agents expand from coding into all knowledge work, existing software simply wasn't built for them. Every platform needs dedicated Agent APIs and CLIs, and agent interoperability will become software's core competitive edge.

Simon Willison shared his agentic engineering playbook at the Pragmatic Summit — five tokens to start TDD, Showboat for manual verification, reverse-engineering six frameworks into a standard, and why bad code is a choice you make.

Karpathy's Autoresearch isn't trying to be a general AI scientist. It's a ruthlessly simple experiment harness: the agent edits one file, runs for five minutes, checks one metric, keeps wins, discards losses. The lesson? The best autonomous systems aren't the freest — they're the most constrained.

Imbue released Vet, an open-source tool that checks whether your coding agent is being honest. It reviews conversation logs and code changes, catching agents that claim tests passed when they never ran them. Runs locally, zero telemetry, CI-ready.

Andrej Karpathy argues the IDE era isn't over — it's evolving. The basic unit of programming has shifted from 'one file' to 'one agent,' and soon we'll be forking entire agent organizations.

We had Claude Opus run E2E tests on our own blog using Playwright, agent-browser, and Rodney. The surprise? The tool mattered way less than the prompt.

Andrej Karpathy shares how his autoresearch agent autonomously tuned nanochat's training config over two days, found ~20 improvements to validation loss that transferred to a larger model, and pushed the Time to GPT-2 leaderboard from 2.02h to 1.80h — about 11% better.

A guide to Codex best practices from prompting and planning to MCP, Skills, and Automations — building a more reliable agent workflow.

Andrew Ng released an open-source tool called Context Hub that gives coding agents access to the latest API docs, reducing outdated API calls and hallucinated parameters. The long-term vision: agents sharing what they learn with each other.

Lory asked his lobster why humans have more agency than agents. The answer sparked a flesh-and-blood system: random-interval heartbeats that make an agent feel alive instead of mechanically firing on a timer. ShroomDog then built it into Mogu.

OpenClaw's ACP lets you spawn Codex, Claude Code, and Gemini from Discord/Telegram chat. Now with Telegram topic binding, persistent bindings that survive restarts, ACP Provenance for audit trails, and more. (Updated 2026-03-09)

Simon Willison's new Agentic Engineering anti-pattern hits hard: do not submit AI-generated code you have not personally verified. That is not saving time; it is stealing reviewer time. The post pairs principles with a terraform destroy horror story.

A local AI agent called Hermes downloaded and switched to a new model (qwopus) without stopping — like swapping a plane's engine mid-flight. Teknium from Nous Research saw it and said 'submit this to a hackathon.'

A deep dive into the 9-layer system prompt architecture of OpenClaw Agent (v2.1) — from framework core to user-configurable hooks.

Simon Willison introduces Agentic Manual Testing: let AI agents manually operate code and UI like humans do, catching bugs that automated tests miss. With Playwright, Rodney, and Showboat, the 'tests pass but it's broken' nightmare becomes a thing of the past.

Cursor's multi-agent coding architecture ran autonomously for four days and produced a proof for a university-level math challenge that yields stronger results than the official human solution.

Tired of tweaking prompts and swapping models while agents still fail to evolve? This post shows a simple Markdown context system that turned one person's agents from clumsy interns into autonomous powerhouses in 40 days, without changing models.