agentic-hyperparm

What problem does it solve?

It solves the problem of an agent’s performance, cost, and safety being miscalibrated because its behavioral “dial set” (retrieval, planning, verification, abstention, and sampling) is not tuned to the deployment context.

Core Features & Use Cases

• Layerwise behavioral tuning: Tunes retrieval, context management, reasoning depth, verification passes, abstention policy, then sampling in a deliberate order to avoid wasted search.
• Interaction-aware parameter planning: Models key interaction effects (e.g., retrieval depth vs context budget, temperature vs verification) so configurations are evaluated realistically.
• End-to-end episode evaluation: Uses multi-seed scoring and composite objectives (quality, cost, latency, safety) rather than cheap proxy metrics.
• Trial loop with persistence and validation: Defines how to materialize configs, run tune/holdout banks, persist trial artifacts, and detect tune/holdout overfitting.

Quick Start

Tune the agent’s behavior by running the skill’s layerwise protocol on your tune bank, selecting the best composite-scalar configuration, and then validating it once on a separate holdout bank.