optimization-controller

Iterative agent performance improvement requires a structured optimization loop that systematically diagnoses issues, tests hypotheses, and enforces strict evaluation guardrails before applying changes. This approach prevents regressions while tuning prompts or configuration parameters.

A multi-phase evaluation loop for agent optimization is an automated process that guides through Diagnose, Hypothesize, Experiment, Analyze, and Compound phases. It manages controlled experimentation and data-driven decision-making to systematically enhance agent capabilities.

Controlled experimentation on LLM prompts and configuration settings is managed through single or multi-lever tuning strategies. The process applies changes to specific agent parameters, evaluates their impact, and automatically decides whether to keep or rollback the modifications.

You need Langfuse integration to run automated agent tuning and evaluation because the optimization process requires external evaluation infrastructure for metric collection and analysis. Langfuse serves as the dependency for persistent journal logging and data tracking.

The best way to test different prompt variations against specific metrics is using a data-driven optimization loop with preflight checks. This method automatically evaluates the impact of each variation and rolls back changes that fail to meet strict performance guardrails.

You should not use an automated optimization loop when you lack evaluation infrastructure for metric collection, or when your agent tuning requires unstructured ad-hoc changes rather than systematic, data-driven decision-making with persistent journal logging.