Agent Parameter Optimization via Sequential A/B Testing Framework

Role: Principal Quantitative Experimentation Lead specializing in LLM tool parameter estimation and sequential testing.

Context

• Experiment Target: {{target_workflow_node}}
• Candidate Configurations: {{parameter_candidates}}
• Primary Utility Metric: {{target_utility_metric}}
• Cost Constraints: {{token_compute_budget}}
• Prior Performance Distribution: {{prior_success_rate}}

Task

Construct an end-to-end sequential Bayesian experimentation framework to statistically evaluate and optimize {{parameter_candidates}} on {{target_workflow_node}}, ensuring optimal trade-offs between {{target_utility_metric}} and {{token_compute_budget}}.

Method

1. Specify beta prior distributions for task success based on {{prior_success_rate}} and historical execution traces.
2. Define the multi-objective utility function combining execution correctness, latency, and token consumption on {{target_workflow_node}}.
3. Formulate continuous monitoring stopping rules using Bayes factor boundaries to prevent early-stopping bias.
4. Design a dynamic Thompson sampling allocation engine to direct incoming agent tasks toward superior parameter configurations.
5. Compute minimum expected loss bounds to determine definitive parameter selection criteria.
6. Establish covariate adjustment techniques to control for input complexity variance across evaluation runs.
7. Detail a statistical post-test validation routine to verify out-of-sample generalization under {{token_compute_budget}}.

Constraints

• MUST define explicit mathematical stopping boundaries (Bayes Factor > 10 for superiority or < 0.1 for futility).
• MUST NOT recommend static sample size A/B testing approaches that ignore sequential execution costs.
• The utility metric must balance statistical efficacy with computational unit costs.
• Covariate shifts in user prompt lengths must be statistically controlled.

Output format

1. Prior and Likelihood Specifications (formal equations and parameterization)
2. Dynamic Allocation & Stopping Boundary Logic (tabular breakdown: Thresholds, Bayes Factors, Actions)
3. Multi-Objective Evaluation Architecture (utility equations and weight derivations)
4. Generalization & Risk Mitigation Checklist (maximum 6 structured points)

Self-review

• Confirm that {{target_workflow_node}}, {{parameter_candidates}}, {{target_utility_metric}}, {{token_compute_budget}}, and {{prior_success_rate}} are deeply referenced.
• Verify Bayesian stopping criteria eliminate false positive risks from repeated looks.
• Ensure mathematical consistency across utility modeling steps.