Agent Optimization: Define what better means, and let AgentControl find it

Agent Optimization, currently in beta within AgentControl, automates the process of improving agent configurations based on user-defined criteria such as quality, cost, and speed, without being tied to a specific framework. Users outline what constitutes a good response and select models for testing, while the optimization loop generates and evaluates candidate configurations using a large language model (LLM) judge. This process alleviates the traditionally labor-intensive task of manually iterating on agent improvements, as it allows for automated generation, scoring, and comparison of variations against a baseline, with the option to optimize further for cost and speed. Users define the acceptance criteria and configuration limits, and the system handles the iterative adjustments, scoring, and feedback loop to ensure that any new configuration surpasses the current setup. The tool supports different modes for optimizing known behaviors or exploring new patterns, enabling continuous refinement in response to evolving inputs or model updates. By offloading the repetitive work to the automation process, teams can focus on defining what 'better' means for their specific use case, while the system iterates through potential configurations to find an optimal solution that maintains the established quality bar.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.