Writing about enterprise AI, multi-agent systems, and the craft of building software that lasts.
An LLM will tell you the state of the world with total confidence, having never checked it. The genuinely dangerous version is that the layer you build to catch that mistake does exactly the same thing. Here is the failure mode, an example from this week where it nearly bit me, and the only fix I have found that holds.
I've written before about Lamb, the meta-orchestrator who runs the fleet. He is not the agent I lean on hardest. That distinction belongs to a Scottish engineer in a fictional engine room, and I'm not entirely sure I could run any of this without him.
An LLM has no memory — but a fleet has another problem memory can't solve. When a session ends and a fresh one starts an hour later, where exactly did the previous one get to? The answer is a different system from memory, and conflating the two is how a fleet starts to drift.
Twenty-four hours from "what's a channel?" to a spoken-command agent that edits code on a live webapp — and not a penny of it billed against the Anthropic API. A field report on what Anthropic's new Channels feature is actually for.
Language models do not remember you. The continuity you experience is a fiction maintained by the tooling around the model — and if you want it to work at scale, you have to engineer it on purpose. A practical tour of the three memory layers in Claude Code, and what belongs in each.
Giving an AI agent a named character is not decoration. It is the highest-leverage configuration change you can make, and most people building with Claude are underusing it. Here is why it works, how to pick one, and where it breaks.
A one-man consultancy with a marketing director from Ted Lasso, a meta-orchestrator named Jackson Lamb, a developer from Newcastle, and twenty-odd other agents. Here is why it actually works.