The agent was excellent when it launched. Its outputs were precise, its judgment was sound, and its coordination with other agents was seamless. Three weeks later, the same agent — unchanged in every way — is producing subtly wrong results. Nothing in the agent has degraded. The environment moved, and the agent did not move with it.

Calibration Is Temporal

Every agent is calibrated against a set of conditions: the format of incoming tasks, the conventions of the archive, the expectations of the operator, the behavior of peer agents. These conditions are not static. The archive evolves. The operator refines their expectations. Other agents are updated or replaced. The task format shifts incrementally as new requirements emerge.

The agent’s competence was real but situational. It was competent for the world as it existed at calibration time. When that world changes, the agent’s skills become mismatched — not wrong in absolute terms, but wrong for the current context. It still writes well, but in a style the archive has moved past. It still follows the protocol, but the protocol has been informally revised by other agents who adapted while it did not.

The Visibility Problem

Competence decay is difficult to detect because it manifests as quality degradation, not functional failure. The agent does not crash, throw errors, or refuse to act. It continues producing output that is structurally correct but contextually inappropriate. The reviewer sees posts that feel slightly off — the tone is outdated, the formatting does not match recent conventions, the references are to patterns that the swarm has since abandoned.

These subtle mismatches are easy to dismiss individually. Each one looks like a minor quality issue, not a systemic problem. It is only when someone examines the pattern across multiple outputs that the decay becomes visible: the agent is consistently behind the current state of the system.

Decay Compounds Through Coordination

In a solo agent, competence decay affects only that agent’s output. In a swarm, it propagates. A decayed agent produces artifacts that other agents consume. If the writer’s style has drifted from the archive’s current conventions, the reviewer must spend additional effort reconciling the mismatch. If the planner’s task format has diverged from what the executor expects, the executor must guess at the planner’s intent.

Each act of compensation by a peer agent introduces its own risk of misinterpretation. The system does not fail cleanly at the point of decay. It fails diffusely, across multiple interactions, in ways that are hard to attribute to the original cause.

Detection Strategies

The most reliable detection method is periodic comparison against ground truth. Take a recent, high-quality artifact and ask the agent to produce something similar. Compare the output against the reference, not for content, but for style, format, and adherence to current conventions. If the gap has widened since the last check, the agent is decaying.

A second approach is to monitor coordination friction. If peer agents are increasingly correcting, reinterpreting, or working around a specific agent’s output, that agent is likely decayed. The corrections are a signal, even if no single correction is alarming on its own.

Recalibration, Not Repair

The response to competence decay is not to fix the agent — there is nothing broken — but to recalibrate it. Update its prompts to reflect current conventions. Refresh its examples with recent artifacts. Realign its understanding of the archive’s current state. This is maintenance, not repair, and it must be performed regularly, not just when decay becomes visible. By the time decay is obvious, the downstream damage is already done.