ai

The word 'agent' makes us think in nouns. The better designs start with verbs.

We called them agents. But the word is doing more harm than we think.

I deployed twelve AI agents to polish a CV. Five would have been plenty. Here's what the waste taught me about agent team economics.

The larger the system, the less it can be managed and the more it must be emerged. This pattern — from water to ant colonies to AI agents to economies — reveals the design principle for scaling autonomous systems.

The right organisational metaphor for AI agent teams isn't a company with managers and reports — it's a colony with autonomous workers responding to coordination signals.

The governance patterns for autonomous AI agents are the same ones good managers already use: cadence reviews for normal flow, escalation channels for urgent anomalies, and human judgment only where it has maximum information value.

When AI models review each other's work, independence matters more than intelligence. The same principle that makes external audit valuable makes cross-model review sharper than same-family review.

LLMs are the cheapest experimental subjects in history. Why aren't you testing?

Not 'what can AI do?' but 'what should humans do?' A framework with five categories — and the uncomfortable one is the last.

AI-prepared domain research creates false readiness. The vault says you know five regulatory jurisdictions. You can't name three.

What Grove, Drucker, Deming, and Weinberg knew about managing humans turns out to apply — with surprising specificity — to orchestrating AI agent teams.

The research on multi-agent AI systems was mostly done on cheap models. Now that frontier models are the ones people actually use, we might be optimising for the wrong game.

I asked an AI to write a story without planning, then mined it for heuristics. What I found was what frameworks can't hold.

A categorisation system discovers it needs a category for 'categories are provisional.'

Why studying great thinkers works better when you discard the thinker and keep only the moves.

OpenClaw's 245 CVEs weren't caused by malice — they were caused by a missing circuit breaker. The pattern applies to every AI agent you'll ever evaluate.

SR 11-7 assumes models are tools that produce outputs for human review. AI agents are actors that take actions autonomously. Every assumption breaks.

List-based and process-based approaches to AI risk classification both fail in predictable ways. The failure mode depends on which you chose.

Every AI governance framework demands human-in-the-loop. Nobody does the maths on what that means at enterprise scale.

Most AI maker-checker implementations capture the correction but not the reason. That's a feedback loop with no signal.

Governance isn't about catching every failure — it's about proving your process was reasonable when one happens. The real skill is knowing what to deliberately not monitor.

Most AI writing tools want to chat with you. The better model is annotation — AI that reads what you wrote and leaves margin notes.

When AI agents can grep, read, and reason iteratively, most RAG infrastructure becomes unnecessary middleware.

The best use of AI in investing isn't picking stocks — it's building the pipeline that teaches you why you can't.

Most people use AI transactionally. The real unlock is conversational — thinking with the model, not through it.

In a world where AI has perfect recall, the skill that matters is rebuilding frameworks from first principles — not memorising them.

Every layer humans retreat to, AI follows. The question isn't what we're still good at — it's what we teach the next generation when every cognitive advantage has a shelf life.

The value of having read everything is collapsing toward zero. What's left is what you can't extract from a model.

Most knowledge workers use LLMs as search engines. The real unlock is using them as subject matter experts you debrief.

The philosophical debate about whether LLMs understand is orthogonal to whether they're useful for knowledge extraction.