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Use agentevals to score recorded OpenTelemetry traces from kagent against a version-controlled golden eval set, then use those checks as a CI gate. This catches specific changes in tool use or final responses; it does not rerun the agent, prove general correctness, or show how a newly built version behaves unless your pipeline also executes and captures that version.
What agentevals checks—and what it does not
kagent is a Kubernetes-native agent platform with OpenTelemetry tracing and structured logs documented for its 1.x ecosystem. agentevals evaluates agent behavior from existing OpenTelemetry traces, comparing them with golden eval sets or applying other evaluators. It supports Jaeger JSON and native OTLP trace formats, as well as custom evaluators and CI/CD thresholds. Its project describes the software as under active development, so confirm CLI options and metric semantics for the version you pin.
Scoring an old trace avoids re-executing the LLM calls represented in that trace. That makes it useful for repeatable checks of captured behavior, but it is not a test run of the current agent build. To test a new build end to end, add a controlled step that runs the agent and captures its traces before scoring them.
Capture traces for the behavior you want to test
Start with user-important tasks that exercise representative branches, tool calls, and failure cases. Capture them using the relevant kagent version and configuration for the regression suite. kagent’s 1.x observability guide describes an OpenTelemetry Collector and trace backends such as Tempo.
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Check sampling before concluding that a request produced no trace. The kagent 1.x OTel stack documentation accessed on 2026-10-04 says Agent Substrate keeps 1% of traces by default. For an evaluation setup, that guide shows otel.traces.samplingRatio=1.0; it cautions that this should be lowered again in production because the router then records every forwarded request. This is versioned configuration guidance, not a guarantee about every kagent release. See the kagent 1.x OTel stack documentation.
For testing, make sure sampling retains the runs you intend to score. Keep prompts, tool inputs, and outputs within your organization’s retention, access, and redaction policies; the cited technical documentation does not establish a universal policy.
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Build a golden eval set around expected behavior
An eval set holds reference examples for comparison. agentevals documents a format based on Google ADK’s EvalSet schema, intended to support version-controlled test suites. Its UI can also generate eval sets from golden sessions. Review the eval-set format.
Begin with a small set of high-value cases, then add examples when incidents, agent changes, or new task variants expose gaps. Make each expectation concrete enough to catch the failure you care about:
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- For tool selection, record the expected tool uses or trajectory.
- For final-answer behavior, include an expected response or criteria specific to the task.
- When product requirements change, review and update references so the suite does not keep treating a newly desired behavior as a regression.
Choose evaluators for the failure mode
Match each evaluator to the behavior it can observe. The agentevals README demonstrates tool_trajectory_avg_score against a golden eval set: a trace that calls the expected Helm listing tool passes the example, while one without a matching tool call fails. It also demonstrates response_match_score for comparing an expected final answer. The eval-set guide lists additional options, including LLM-judge and safety or hallucination evaluators; verify their names and semantics against your installed release. The agentevals README and eval-set documentation describe these capabilities.
No single score is a complete measure of agent quality. A trajectory check can flag a changed tool path without telling you whether the resulting answer is useful. Text matching can penalize valid paraphrases or fail to catch factual defects. For important tasks, combine deterministic checks with response-level review or a domain-specific evaluator, and inspect examples near threshold failures.
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Run the checks repeatably in CI
The project documents a CLI command of this form for scoring a recorded trace against an eval set:
agentevals run samples/helm.json
--eval-set samples/eval_set_helm.json
-m tool_trajectory_avg_scoreIt also documents multiple trace inputs, JSON output, and evaluator thresholds in configuration. A practical CI job can check out the trace fixtures, eval set, and configuration from version control, pin the agentevals version, run the same metrics on each change, and fail when an agreed threshold is missed. If the goal is to test a new agent build rather than fixtures alone, include an execution-and-capture stage to produce that build’s traces first. The project documents CI/CD quality-gating capability, but does not prescribe a provider-specific pipeline recipe.
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Custom evaluators can be written in Python, JavaScript or TypeScript, or another language able to read and write JSON through the documented stdin/stdout protocol. The custom-evaluator guide illustrates a threshold field and sample value; set thresholds from your task requirements and observed behavior rather than copying an example. See the custom-evaluator protocol.
Triage failures and maintain the baseline
When a gate fails, inspect the trace and decide whether it reflects a genuine regression, an intentional behavior change, a stale fixture, or missing instrumentation. If the change is intended, update the golden eval set in the same reviewed change as the agent update. Keep a review trail for expectation changes so a failing check is not silently removed without considering why it failed.
Compare evaluation approaches by the evidence they provide
There is no neutral comparative benchmark in the cited sources across agent-evaluation products. For your own choice, compare the evidence and operational needs rather than treating different scores as interchangeable:
- Recorded traces or fresh runs: Scoring imported traces checks captured behavior; rerunning the agent tests a new execution and requires an execution-and-capture step.
- Behavior dimension: Tool trajectory, final response, safety or hallucination checks, and task-specific business rules address different failure modes.
- Repeatability: Deterministic checks are different from model-based judgments or live calls whose responses may vary.
- Integration: Account for trace import, direct OpenTelemetry collection, custom evaluator work, and CI wiring.
- Operations: Decide whether local trace inspection is enough or whether your team needs persistent shared storage, retention controls, and access management.
Scores remain evidence for review, not proof of correctness. Their value depends on trace quality, eval-set coverage, evaluator behavior, and intentional thresholds; the documented workflow does not establish statistically calibrated significance testing or a general correctness guarantee.
Quick Recap
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