Answer
What is an AI agent evaluation platform?
An AI agent evaluation platform continuously scores what your AI agents actually do in production against criteria you define. Kynesis is an AI Evaluation and Quality Intelligence Platform built for this: it evaluates AI conversations, answers, tool calls and workflows, and returns per-criterion scores with the evidence behind each one.
What gets evaluated in an AI agent
Answer accuracy
Whether the response is factually correct and grounded in the sources available to the agent.
Hallucination and fabrication
Claims the agent could not have supported from its context or tools.
Tool execution
Whether the agent chose the right action, called it correctly and handled failures.
Task completion
Whether the user actually got what they came for, end to end.
Policy adherence
Disclosures, prohibited statements, escalation rules and tone requirements.
Customer experience
Clarity, empathy and effort required from the user.
How evaluation runs
- 01
Connect traces or transcripts
Send AI conversations, answers and tool traces via the API, webhooks or bulk upload.
- 02
Define the scorecard
Criteria, weights, thresholds and critical-failure rules specific to your agent and domain.
- 03
Evaluate every run
Score all traffic, not a sample, so regressions surface quickly.
- 04
Inspect the evidence
Each score cites the part of the exchange it is based on, so disagreements can be resolved.
- 05
Aggregate failure modes
Group recurring failures into a taxonomy you can prioritise and fix.
Choosing a platform: what to check
Can you author the criteria?
Generic benchmarks rarely match your domain, policies or product.
Is every score explainable?
Without quoted evidence, scores cannot be trusted or acted on.
Does coverage scale?
Sampling hides intermittent failures, which is where agent risk lives.
Can humans override?
Reviewer override, calibration and disputes keep automated scoring honest.
Are patterns surfaced?
Individual scores are less valuable than the recurring failure modes behind them.
Limitations to plan for
Automated evaluation is only as good as the criteria behind it. Vague criteria produce vague scores, so the first iteration of a scorecard usually needs refinement against items your team has already reviewed manually.
Kynesis recommends a calibration pilot of 100–200 previously scored items, comparing agreement on the overall score, critical-failure recall and false-positive rate before rolling out to full volume. Because Kynesis returns the evidence used for each score, disagreements can be inspected criterion by criterion.