How to Improve Your AI Agent: A Guide for Founders

Improving your Agent

1. Set Up an Evaluation Strategy from the Start

• Final Output Evaluation: Does the response solve the user’s problem?
• Component-Level Evaluation: Are individual modules working as expected? (e.g., did the retrieval tool fetch the correct data?)
• Trajectory Evaluation: Was the sequence of actions efficient and logical?

2. Evaluate the Final Agent Output

• Use success criteria: Define KPIs aligned with business needs, such as response relevance, accuracy, utility, or customer satisfaction (e.g., NPS scores).
• Collect feedback: Get explicit feedback from end-users or testers.
• Set up automated scoring mechanisms: You can have judge prompts to grade the final answer. You can use a judge prompt to check for specific dimensions like relevance, tone etc.

3. Evaluate Each Step of the Agent’s Workflow

• Evaluate modular components: Look at the outputs from individual steps, such as data retrieval, intent detection, or decision-making logic.
• Debugging trajectories: Trace how the agent arrived at its conclusion. If the reasoning path (or trajectory) is flawed, adjust the sequence of operations to make the agent more effective.

4. Human Oversight for Scalability and Reliability

• You can create a system where human reviewers randomly audit the agent’s outputs, rating them for correctness and usefulness.
• You could also build an Auditor Agent that can run checks and validates your agent logs

• This can be a simple tool that has access to google search and customer knowledge bases to check for incorrect suggestions and hallucinations

• Scalable review systems can rely on SMEs (subject matter experts) for domain-specific evaluations. The data gathered from these audits can pinpoint weak areas that need further improvement.

• In scenarios requiring high precision, agents can assist human operators. The agent makes recommendations, which the human approves or rejects.
• This approach creates a feedback loop—human corrections become valuable training data that improves the agent over time.

5. How to Use Evaluation Data for Continuous Improvement

• Prompt Optimization: Use the feedback and audit results to rewrite or auto-optimize prompts that guide the agent’s behavior. For automatic prompt optimization, use an open-source tool like AdalFlow.
• Few-Shot Learning: Incorporate the best examples from your data as few-shot examples to enhance your agent's performance for specific tasks.
• Component Tuning: If an individual step (like intent recognition) performs poorly, you can modify, replace, or retrain that module.
• LLM Fine-Tuning: When necessary, fine-tune the underlying LLM on domain-specific data to improve accuracy and relevance in your specific use case. This can be done periodically to ensure your agent stays up-to-date.
• Planning: If you are noticing your agents have bad trajectories meaning it doesn’t take the right action in the right order, you may want to look into planning. Many research methods show that having the LM come up with a high level plan initially dramatically boosts performance and improves trajectories. You can use your evaluation data to check if the trajectories have improved.

6. Aligning with Business Goals and Scaling Operations

• Onboarding more reviewers or SMEs to audit outputs at scale.
• Automating portions of the review process (e.g., auto-flagging anomalies).
• Deploying multiple agents across different functions or departments, leveraging the same optimization techniques to replicate success.

Final Thoughts

1. Set up an evaluation framework from the start.
2. Evaluate the final agent response, components, and trajectory separately.
3. Integrate human oversight, either through audits or human-in-the-loop workflows.
4. Use the evaluation data to fine-tune components, prompts, and the underlying LLM when necessary.