Natural-Language Agent Harnesses
Improve natural-language agent reliability and performance by externalizing and standardizing their high-level control logic. This approach makes agent designs modular, testable, and easier to compare, accelerating robust AI development.
5 Steps
- 1
Decouple Control Logic: Separate the high-level decision-making and flow control of your agent from its core task execution components (e.g., LLM calls, tool use).
- 2
Define a Harness Interface: Establish clear, standardized input and output contracts (APIs) for your externalized control logic to interact with the agent's sensors and effectors.
- 3
Create a Dedicated Harness Module: Implement the decoupled control logic within a distinct module, class, or configuration file, making it independently deployable and testable.
- 4
Enable Strategy Swapping: Design the harness to easily allow switching between different control strategies or algorithms without modifying the agent's core functionalities.
- 5
Develop Evaluation Frameworks: Build or adapt tools to systematically test and compare the performance of various harness designs against defined metrics and benchmarks.
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