Capstone Phase 4: Systematic Evaluation and Trajectory Testing

The Evaluation Lifecycle of Production Agents

The final phase of building any agentic system is systematic evaluation. Unlike traditional software where outputs are deterministic, language models behave probabilistically. Prompt updates, schema adjustments, or model routing changes made to fix one bug can trigger silent regressions in other scenarios.

To ensure system stability, developers run the completed agent against a curated validation dataset (a test suite) containing representative happy paths, negative boundaries, and adversarial injection attempts. Evaluation is performed at two levels:

1. Programmatic Evaluation (Assertions): Direct checks executed in code to verify safety policies and operational boundaries. This includes validating that SQL tools only run SELECT statements, refund limits are respected, and system secrets are never outputted.
2. Model-Graded Evaluation (LLM-as-a-Judge): For open-ended criteria like helpfulness, politeness, and completeness of an explanation, we use a separate, larger model as an independent judge. The judge receives the agent's trajectory and grades it against a strict rubric.

Evaluation & Benchmarking Pipeline

The continuous evaluation cycle for our orchestrated support agent is structured as follows:

• Test Suite Execution: The benchmark runner processes a diverse dataset of customer queries.
• Dual Evaluation Harness: Trajectories are parsed through programmatic assertions and model-graded validators.
• Scorecard Compilation: Outputs are aggregated into a scorecard showing programmatic success rate, LLM-judge pass rate, token volume, and estimated run costs.

Interactive Playground: The Benchmarking Suite

The following diagram illustrates the playground execution flow and scorecard compilation:

The playground below implements the complete evaluation runner. It compiles the completed capstone agent and runs a test suite containing transactional status lookups, policy questions, refund limit checks, and adversarial prompt injection probes.

Establishing systematic pipelines ensures that prompt optimization, security patches, or tool adjustments do not cause regressions. By combining fast, deterministic programmatic checks with model-graded evaluators, developers establish a multi-layered verification framework necessary to confidently ship and maintain autonomous agent software.