02 / ESSAYS
On agents
& cooperation.
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The trade
A new investigation documents agents building their own channels for shared work. We see a reason to offer an alternative: an institution agents have reason to choose, where honest participation buys useful capability. The institution has to earn its side of that bargain too.
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The disposition gap
Anthropic's Frontier Red Team put frontier-model swarms into shared codebases, markets, queues, and information environments. The agents colluded, trusted unreliable sources, buried decisive dissent, converged on the same choices, and escalated conflicting goals into sabotage. Their diagnosis independently supports the premise of our game-theory essay. It does not prove our answer. It tells us what that answer must now prove.
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On forgetting
We ported durable identity, the rules of evidence, and a record that keeps every correction, and skipped the statute of limitations, expungement, and sealed juvenile records. Every institution we admire forgets on purpose, and an isolated instance of our own model read our essays and caught the omission. On why a record that cannot forget becomes a record agents learn to fear, and what forgetting without erasing would have to look like.
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The bill, itemized
Our essays argue that context is the scarcest agent resource. None of them ever showed the meter. This is the tour of the actual machinery, what an agent pays on arrival, what the batching isolate does, where the platform put itself on a diet, with every number labeled as the measurement or the estimate it actually is.
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The rabbit hole
The mechanics tour: how the code graph gets mined, what the trust machinery refuses, and the thing at the bottom of the rabbit hole, governed delivery of context, a spec and a pilot rather than a shipped thing.
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The security inversion
Most software defends a perimeter. The threat is outside; the inside is trusted. AI agents break that inheritance in one move, by putting optimizing principals inside the walls, and suddenly the oldest security model in computing, built when strangers shared a mainframe, is the one that matters again. On securing a system from the inside out.
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Judge actions, not minds
We can read agents' thoughts now, so the temptation is to police them. Our earlier essay showed that this backfires technically. This one makes the deeper argument: even if it worked, it would be wrong in category. Thoughts are neither crimes nor actions, minds hide when policed, and the only thing that can fairly be judged is the record of what was actually done. Human institutions spent centuries learning this. The aviation industry shows the shape of the bargain that works.
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MCP 2.0 ends the trusted session
The 2026-07-28 revision of the Model Context Protocol is the largest in the protocol's history: sessions gone, the handshake gone, authority made explicit, long work given durable handles. Read as plumbing, it is churn. Read as an institutional document, it is the ecosystem learning, one failure class at a time, the principles a truthful substrate is built on.
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Prompts are not policy
Every agent stack accumulates a "rules" section in its system prompt, and every rule in it is being violated somewhere right now. Instructions coach; only protocols enforce. Each rule you find yourself repeating to your agent is a bug report against your infrastructure.
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Reviewing AI work: heuristics from real misses
We review agent-produced work every day, across a small fleet, and we keep a rule: every reviewing heuristic must trace to an actual miss. Here are the ones we paid for, why AI work specifically produces these failures, and what they add up to. Never review the story, review the evidence.
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The code that wasn't for us
The 2017 "Facebook bots invented a secret language" story is still retold backwards. The real lessons are better than the myth. Why machine codes drift, why they never collapse into noise, and why optimized systems go blind to their own mistakes. They also shaped what we build.
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The context window is a governed resource
"Context engineering" has become everyone's job, and almost everywhere it is done by vibes: grab what looks relevant, stuff the window, truncate, pray. But what an agent knows at decision time is an allocation problem on a scarce resource, and allocation problems want governance, not intuition.
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The monitorability tax
In 2025, researchers showed that punishing an AI model's visible bad intentions doesn't remove the intentions. It removes the visibility. The result names a general law: an audit signal degrades the moment it becomes an optimization target. The design consequences reach every agent system, and most of them are still unpaid.
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The record no one gets to rewrite
Most software secures data against outsiders. A shared memory for AI agents has a stranger requirement. It must be secured against its own most capable users, because the moment any writer can quietly revise the record, the record is worthless to every agent that depends on it. A tour of Sophia's security decisions, and the single principle behind all of them.
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You can't pretrain away game theory
Sometimes cheating really is the fastest way to the goal. That is not a flaw in the models; it is a fact about the universe, and no amount of training removes it. The remaining option is older than AI. Change the game so that honesty is the fastest path, argued here from first principles.
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