Know Your Agent Works, Before Your Users Find Out It Doesn't

Evaluating every step of the reasoning chain, not just the final output. An agent that retrieves the right documents but misattributes a fact in step three has still failed, even if the final answer looks correct.

Validating that the agent selects the right tool, passes correctly structured arguments, and handles tool errors without cascading into a broken state, since a schema gap is a leading cause of real production failures.

Running the full evaluation suite whenever a prompt, model, tool, or retrieval system changes, so a behavioral regression is caught before it reaches production, not after a user reports it.

Sampling production outputs for human review, labeling quality dimensions automated checks can't measure, and using those labels to calibrate automated judges, the ground truth for helpfulness and nuanced correctness.

Tracking agent quality metrics after launch and surfacing regressions when real user inputs diverge from what the golden dataset covered, with production traces feeding back to keep the test suite current.

Simulating real-world attacks against agents: prompt injection, single- and multi-turn jailbreaks, prompt extraction, unauthorized tool access, privilege escalation, goal hijacking, excessive agency, insecure function calling, and sensitive data leakage.

Validating that agent actions produce correct real-world outcomes in the systems they operate on. A booking agent's confirmation must match the actual database record, not just the output text, verified across sandboxed and live environments.

Testing both sides of memory: that the right information is written, and that the right information is retrieved. Short-term testing checks context retention within a session; long-term testing checks that facts from prior sessions stay accurate and current.

Capturing full step-level traces across every tool call, model response, memory read and write, retrieval step, and decision branch, so trajectory evaluation has replay data and production regressions have a trace to debug against. 

Measuring token consumption, cost per task, and latency across the full execution trace, since a prompt change or model swap can raise cost significantly with no visible change in output quality.

A framework triggering multiple evaluators per input: security, environment, cost, memory, tool accuracy, retrieval quality, and coherence, each with its own pass/fail threshold. Prompts evolve based on past runs and integrate into CI/CD so quality gates run on every change.

Validating that the agent retrieves the correct information from its external knowledge store, distinct from memory. Covers retrieval accuracy, relevance of retrieved context, and cases where the right document exists but retrieval fails to surface it.

Running the same test dataset across different temperature and parameter configurations to find the optimal setup, since some agents need highly consistent responses and only meet quality within a narrow range.