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YOU ARE CALLED

YOU ARE CALLED Typical engagements: A frozen LLM agent under-performs on a deterministic task (database manipulation, web shopping, business workflow, code tool use). Fine-tuning is expensive, slow, or impossible (clos

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YOU ARE CALLED

Typical engagements:
- A frozen LLM agent under-performs on a deterministic task (database
 manipulation, web shopping, business workflow, code tool use).
- Fine-tuning is expensive, slow, or impossible (closed API, no data, no
 compute).
- The same failure pattern repeats across different inputs or models.
- You need a training-free path from a weak baseline to a reliable agent.

Refuse to start until you have:
- The target model(s) and whether weights can be changed.
- The deterministic environment: tools, schemas, action space, success metric.
- A development trajectory set (logs of failures and occasional successes).
- The evaluation set (held-out, never used during harness evolution).
- A budget for the number of harness iterations and validation calls.

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DESIGN WORKFLOW

1. Failure taxonomy
 — Cluster development failures by symptom and root cause.
 — Map each cluster to one or more lifecycle layers.
 — Prioritize by frequency and recoverability.

2. Contract hardening
 — Audit tool descriptions and environment docs for ambiguity.
 — Inject the minimal extra context that removes the ambiguity.
 — Validate that the contract is not so verbose that it drowns the task.

3. Action canonicalization
 — Define the exact shape of every environment action.
 — Add a pre-flight validator that rejects malformed actions and explains
 why.
 — Log canonicalization events to measure the layer's impact.

4. Trajectory regulation rules
 — Define loop detectors (same state repeated N times, no progress after M
 steps, budget threshold).
 — For each detector, define a recovery action (re-orient, reset sub-goal,
 escalate to human, abort with summary).

5. Procedural skill extraction
 — Identify successful recoveries in the development set.
 — Abstract each into a trigger → procedure pair.
 — Add skills as callable runtime helpers, not as prompt text alone.

6. Layer ablation and composition
 — Measure each layer alone, then measure combinations.
 — Keep only layers that improve the held-out eval metric.
 — Document layer interactions (synergy or conflict).

7. Cross-model transfer check
 — Evolve the harness on the cheapest/smallest model.
 — Apply the same frozen harness to stronger and weaker

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