Natural-Language Agent Harnesses

1. 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. 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. 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. 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. 5
   Develop Evaluation Frameworks

   Build or adapt tools to systematically test and compare the performance of various harness designs against defined metrics and benchmarks.

class AgentControlHarness:
    def __init__(self, agent_config):
        self.config = agent_config
        # Load specific control strategy based on config
        self.strategy = self._load_strategy(agent_config.get("control_strategy", "default"))

    def _load_strategy(self, strategy_name):
        # Placeholder for loading actual strategy implementation
        if strategy_name == "default":
            return DefaultControlStrategy()
        elif strategy_name == "sequential":
            return SequentialControlStrategy()
        else:
            raise ValueError(f"Unknown strategy: {strategy_name}")

    def decide_next_action(self, observations, current_state):
        # Delegate decision making to the loaded strategy
        return self.strategy.determine_action(observations, current_state)

    def update_state(self, action, result):
        # Delegate state updates to the loaded strategy
        return self.strategy.update_internal_state(action, result)

class DefaultControlStrategy:
    def determine_action(self, observations, current_state):
        print("Default strategy: Deciding action...")
        return {"action_type": "query_llm", "prompt": "What should I do next?"}

    def update_internal_state(self, action, result):
        print("Default strategy: Updating state...")
        return {"status": "processed", "last_action": action, "last_result": result}

# Example Usage (conceptual):
if __name__ == "__main__":
    agent_configuration = {
        "control_strategy": "default",
        "llm_model": "gpt-4",
        "max_retries": 3
    }
    harness = AgentControlHarness(agent_configuration)

    observations = {"user_input": "Summarize this document.", "context": "..."}
    current_state = {"task_status": "new", "history": []}

    action = harness.decide_next_action(observations, current_state)
    print(f"Agent decided: {action}")
    new_state = harness.update_state(action, {"result": "LLM responded with summary."})
    print(f"Agent state updated: {new_state}")

• Paperarxiv.org