Developers building agentic chatbots for complex products like Postgres AI Hybrid Manager can ensure reliability by implementing a tiered evaluation framework that tests routing, task completion, and multi-step trajectories.

Key Points

• Routing Evals: Use deterministic tests to verify that user prompts correctly trigger the intended agent skill or tool.
• Task Completion Rate (TCR): Employ LLM-as-a-judge patterns with specific rubrics to score complex, non-deterministic responses.
• Multi-step Testing: Maintain conversation state across turns to ensure the agent handles follow-up questions and context correctly.
• Trajectory Testing: Evaluate the entire path of an interaction, including tool selection, argument validity, and final output.
• Instrumentation: Utilize tools like Langfuse to capture trace-level telemetry, enabling developers to debug specific points of failure.
• Tiered Architecture: Adjust evaluation expectations based on model size to accommodate constraints in sovereign or air-gapped deployments.

Why it Matters

Moving beyond simple text-based testing is essential for agentic applications that perform real-world actions, such as database migrations or cluster management. By validating the entire execution path rather than just the final response, developers can ensure their AI tools remain reliable, secure, and functional across diverse deployment environments.