AI agents powered by large language models (LLMs) are rapidly transitioning from impressive demos to practical applications. Deploying these agents in high-stakes environments demands careful evaluation due to their inherent unpredictability. How can you confidently determine if your GenAI agent is ready for production? This guide outlines essential evaluation questions and industry-proven best practices to ensure your agents are reliable and ready for deployment.

Has the agent been properly tested?

Given the vast variety of inputs LLMs handle, your testing must be comprehensive:

1. Core use cases: Simulate typical real-world interactions your agent will face, like product inquiries or troubleshooting. For example, if your agent serves as a customer support chatbot, ensure your tests cover typical scenarios like product inquiries, account issues, and service troubleshooting.
2. Edge cases and out-of-scope inputs: Go beyond standard scenarios. Include unusual phrasing, complex prompts, and off-topic questions to ensure the agent handles them gracefully.
3. Adversarial inputs: Perform targeted red-team testing to expose vulnerabilities, including nonsensical queries or malicious prompts.

Define clear success criteria, such as accuracy, correct tool invocation, and policy compliance. Regularly integrate real-world user feedback into your testing process.

Is the agent using tools correctly?

Evaluating tool usage is crucial when determining an AI agent’s readiness for production:

1. Supported query types: Clearly outline and test primary agent functions extensively.
2. Edge cases and out-of-scope inputs: Test complex or unusual queries beyond typical scenarios to verify graceful handling.
3. Stress and adversarial inputs: Challenge your agent with nonsensical queries, attempts to access restricted information, and known adversarial patterns to uncover potential vulnerabilities.

Regularly update test scenarios based on real user interactions to continuously enhance reliability and reduce regressions. Clearly define evaluation criteria such as factual accuracy and appropriate tool use, moving from manual reviews to structured automated evaluations as your agent matures.

Effective testing is an iterative process. Regularly update test cases based on real-world user interactions, continuously expanding your evaluation datasets as novel scenarios or unexpected behaviors surface in production.

Verifying tool usage & action Sequences

AI agents gain their strength not only from flexible language understanding but also from their capability to select and invoke appropriate tools to perform practical actions. Verify your agent consistently selects appropriate tools, parameters, and execution order:

1. Define ideal tool sequences: Clearly outline the expected tools and sequence of actions for each test scenario. During evaluation, compare the agent’s actual tool usage against these predefined expectations.
2. Validate tool parameters and outcomes: Confirm tools receive correct and complete parameters, ensuring outputs are appropriately used. For example, confirm that a weather API receives the correct location and date, or that a database query uses precise search terms.
3. Allow flexibility: Given that AI agents might solve a problem through various valid paths, evaluations should accommodate reasonable flexibility. Recognize valid alternative methods for problem-solving, validating strict sequences only when necessary.
4. Identify errors and omissions: Monitor instances where the agent neglects necessary tools, selects inappropriate ones, or mismanages errors returned by tools. Establish clear fallback procedures to handle disruptions gracefully. A useful metric here is Tool Error Handling, tracking the agent’s ability to appropriately manage unexpected tool failures.
5. Optimize for efficiency: Ensure the agent’s tool use is efficient, identifying and rectifying unnecessary or redundant steps. For example, if the agent resorts to web searches for straightforward calculations rather than utilizing a built-in calculator, this indicates an area needing refinement.

Monitoring, anomaly detection, and rollback in production.

Continuous monitoring ensures your AI agent stays reliable in real-world scenarios:

1. Comprehensive logging: Log detailed queries, intermediate steps, and internal logic for easy debugging.
2. Define metrics for normal operation: Establish clear baselines of healthy performance, such as response latency, typical tool usage per query, response length distributions, and quality evaluation scores, to promptly detect anomalies.
3. Implement real-time safeguards: Implement proactive validation and interventions, routing complex or risky queries to human support.
4. Rollback and fallback plans: Prepare strategies to quickly revert to stable versions or fallback systems, minimizing user impact.
5. Evaluate rollback appropriateness: Recognize situations where rollback isn’t the optimal solution, especially if user behavior shifts significantly. Instead, swiftly advance forward with updates and adjustments.

Conclusion

Deploying LLM-powered agents involves systematically addressing key evaluation aspects, such as comprehensive testing, meticulous tool-use verification, and robust monitoring. Embracing these best practices ensures your AI agent reliably delivers safe and valuable outcomes, effectively managing uncertainty and minimizing risks in real-world applications.