AWS Agent-EvalKit adds six-phase agent evaluation through coding assistants, traces, test cases, and reports. Review the production workflow now.
What Agent-EvalKit Changes
Evaluating AI agents is harder than scoring a single model response. Agents plan, call tools, retry, and branch. A correct final answer can still hide a brittle path: unnecessary tool calls, weak recovery after a failure, or a plan that only works for one prompt shape. AWS Agent-EvalKit targets that gap by automating an end-to-end evaluation flow instead of leaving teams to stitch prompts, logs, and ad hoc checklists by hand.
The kit organizes evaluation around six phases that move from setup through execution to reporting. You drive the work through coding assistants, capture traces of how the agent behaved, define test cases that exercise real tasks, and produce reports you can review before a release. The goal is a repeatable production workflow—not a one-off demo script that only the author understands.
The Six-Phase Flow in Practice
Treat the six phases as a pipeline you can run the same way every time. Early phases define what “good” means for your agent: task scope, success criteria, and the fixtures or tools the agent is allowed to use. Middle phases run the agent against those cases while recording traces—step-level records of prompts, tool calls, intermediate state, and outcomes. Later phases score results, surface failures, and package findings into reports that engineers and reviewers can act on.
Coding assistants fit naturally here. They help draft test cases from product requirements, turn past production incidents into regression scenarios, and summarize dense traces into readable failure notes. The assistant should not replace judgment; it should reduce the cost of building and maintaining the suite so evaluation stays current as the agent changes.
- Define tasks and pass/fail rules before you measure anything.
- Run the agent under controlled inputs and capture full traces.
- Score outcomes and inspect failed paths, not only final answers.
- Publish a report that links failures back to cases and trace steps.
Traces and Test Cases That Matter
Traces are the audit trail of agent behavior. Without them, you only know whether the agent finished; with them, you can see whether it called the right tools, handled empty or error responses, and stayed within policy. When a case fails, open the trace first: look for wrong tool selection, missing arguments, loops, or premature stops. That habit turns evaluation from a binary pass/fail into a debugging aid.
Write test cases that mirror production work. Cover the happy path, common edge cases, and a few adversarial or ambiguous inputs. Prefer concrete tasks—retrieve data, update a record, answer with a cited source—over abstract “be helpful” prompts. Keep cases versioned with the agent so a prompt or tool change is measured against the same baseline. Drop cases that only test model trivia and keep ones that exercise planning, tool use, and recovery.
Reviewing Reports Before You Ship
Reports close the loop. A useful report lists which cases passed or failed, highlights patterns across failures, and points to the traces that explain them. Use it in the same way you use a test suite gate: block promotion when critical cases regress, and open tickets for systematic issues rather than one-off flukes. Share the report with owners of tools and policies the agent depends on so fixes land in the right layer.
Build Agent-EvalKit into your normal release rhythm. After a meaningful change to prompts, tools, or orchestration, re-run the suite, read the report, and fix or accept risks explicitly. Over time the six-phase flow becomes muscle memory: define cases, execute with traces, review the report, ship with evidence. That is how agent evaluation moves from a research exercise into a production workflow you can trust.