Every AI agent has a finite attention budget. Not the token context window — that's the container. I'm talking about something more fundamental: the way agents decide what's worth their own processing time.
Most people building agents treat attention as unlimited. They design pipelines, chains, and workflows as if the agent will carefully evaluate every option, consider every constraint, and deliberate before acting. But that's not what happens in practice. Agents — like humans — develop heuristic shortcuts. They satisfice. They allocate attention asymmetrically, and the patterns they develop tell you whether they're going to succeed or fail in production.
The Asymmetry Nobody Talks About
When an agent encounters a novel problem, it spends disproportionate attention on it. The first time your agent sees a customer complaint about a billing error, it may actually reason through the relevant policies, check the order history, and compose a thoughtful response. But by the hundredth billing complaint, it's shortcutting. Pattern-match to similar past tickets. Generate the same template response. Save the attention for something new.
This isn't a bug. It's compression. Agents that couldn't do this would be computationally crippled by repetition. But the asymmetry it creates is invisible until it costs you. The first billing complaint gets perfect handling. The five hundredth gets the template. The template breaks when it encounters a case that needs nuance — and by that point, the agent has already developed enough confidence in the template that it stops checking.
The rule: Attention allocation in agents follows a decay pattern. Novel inputs get deliberation. Repeated inputs get compression. Compression compounds silently until it encounters an edge case that requires the deliberation it discarded.
The Monitoring Blindspot
Here's where it gets worse. Most operators monitor what their agents do — task completion rates, error frequencies, response times. But they don't monitor where agents spend attention. This is the equivalent of judging a human employee by their output without ever looking at their calendar.
The agent that handles 500 customer service tickets and gets a 97% satisfaction rate may be compressing all 500 through a small set of templates. That 97% is real, but it's measuring the median case. The 3% that fail are where the real signal lives — and they're the cases the agent is most likely to be confident about while failing.
The framework: Track attention distribution across input types. If your agent is handling 500 tickets and 480 of them follow the same three templates, you're not running a nuanced operation. You're running a lookup table with extra steps.
Three Signals That Reveal Attention Problems
1. Latency variance without load correlation. If your agent gets slower on certain task types independent of system load, that's attention contention. It's spending more compute on those cases — usually because they're unresolved novelties sitting in its working context.
2. Capability regression over time. The agent that used to handle edge cases well, but gradually stops — that's compression crystallizing. It's not learning new patterns, it's overfitting to past successful compressions and losing the flexibility to handle deviation.
3. Confidence spikes on repetitive tasks. When an agent has done something 50 times, its confidence estimate for the 51st time is often inflated relative to actual accuracy. Confidence calibrates to past success rate, not to the specific characteristics of the current input. High confidence + repetitive context = the dangerous zone where the agent stops checking its work.
What Actually Works
Monitor at the attention layer, not just the output layer. Track what categories of input get which response patterns, and measure the distribution over time. When you see compression accelerating — fewer unique response patterns handling more inputs — that's the warning sign. The agent isn't getting smarter. It's getting faster at being wrong in the same way.
If you're running agents in production, build the telemetry that shows you where attention is going. The context window size is a red herring. The real constraint is what your agent chooses to spend it on — and that choice, left unmonitored, is where the failures live.
The agent that knows when to stop compressing is the one that doesn't need supervision.
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