- Published on
Optimizing Tool Calls and Evaluating Agentic Systems
- Authors
- Name
- K B Rahul
- @kbrahul_
Efficiency and Evaluation in Agentic Systems
Once you have a working agent, the next step is optimization. Agentic workflows can quickly become slow and expensive due to multiple LLM calls.
Optimizing Tool Calls
Tool calling is the bridge between the LLM and the outside world. Making it efficient is crucial.
- Provide Specific Tools: Don't give an agent a generic
run_pythontool if a specificget_user_datatool will do. Specific tools have constrained outputs and reduce the cognitive load on the LLM. - Batching: If an agent needs to fetch data for 10 users, provide a tool that accepts a list of user IDs rather than forcing the agent to make 10 separate tool calls.
- Clear Descriptions: The LLM relies entirely on the tool's description and parameter schema. Ensure they are unambiguous. Ambiguity leads to errors and retries.
Evaluating Agentic Systems
Evaluating an agent is much harder than evaluating a standard NLP model. You are not just checking text similarity; you are evaluating a trajectory of actions.
1. Frameworks
Frameworks like AgentBench or WebArena provide standardized environments to test agents on various tasks (e.g., navigating a website, interacting with a database).
2. Custom Metrics
For domain-specific agents, you need custom metrics:
- Task Success Rate: Did the agent achieve the goal?
- Trajectory Efficiency: How many steps did it take? Did it use tools optimally?
- Error Recovery Rate: If a tool call failed, was the agent able to recover?
Evaluating agents requires a shift from static datasets to dynamic environments where the agent's actions have real consequences.