Evaluation set design
Build a versioned set of normal cases, edge cases, policy cases, adversarial inputs, and known failures. The set represents the decisions that matter, not just examples that are easy to score.
For teams that need to know what an agent gets right, where it fails, and who takes over.
Evaluation and guardrails
AI agent evaluation and guardrails built around consequences.
AI agent evaluation and guardrails for task quality, tool use, safety, regression testing, human escalation, and controlled production releases.
Definition
Reliability starts with naming the failure you cannot accept.
Agent evaluation measures whether the whole system performs the intended task, not whether one model response sounds plausible. The evaluation must include retrieved evidence, tool selection, arguments, state transitions, final actions, escalation behavior, and the operational result.
Guardrails then use that knowledge to reduce exposure. Some are deterministic, such as schema validation, permission limits, rate limits, and approval requirements. Others are model based, such as policy classification or evidence checks. Strong systems use several layers and assume that each layer can fail.
Use the least autonomous system that can improve the outcome.
Agentic behavior earns its place when the workflow needs interpretation and tool use. Conventional software should handle everything deterministic.
Capability
What the engagement covers.
The model is one layer. The useful product is the full system that connects context, decisions, actions, control, and ownership.
Task specific measurement
Define exact checks for extraction, classification, retrieval, citation, tool use, action correctness, escalation, latency, and cost. Human review is used where automated scoring would hide nuance.
Layered guardrails
Apply input validation, scoped tools, structured outputs, policy checks, confidence or evidence thresholds, approval gates, rate limits, and safe failure behavior according to the consequence of each action.
Regression and live review
Run evaluations when models, prompts, tools, policies, or data change. Sample live outcomes, turn incidents into new test cases, and prevent the same failure from returning silently.
Method
Test behavior before debating confidence.
The evaluation program links technical behavior to the decision and action the operation actually cares about.
Define the contract
Specify allowed inputs, expected outputs, permitted tools, policy constraints, escalation conditions, and unacceptable outcomes. Ambiguous requirements create ambiguous evaluations.
Assemble the cases
Sample real work where permitted, remove unnecessary sensitive information, and add important edge cases. Cases are tagged by scenario so average scores do not bury weak segments.
Score the system
Use deterministic assertions, model based graders where appropriate, and calibrated human review. Inspect traces to distinguish model errors from retrieval, integration, or workflow errors.
Control the release
Set thresholds, approval policies, monitoring, and rollback criteria. Production incidents update the evaluation set and inform which guardrail needs to change.
Boundaries
Guardrails reduce risk. They do not make it disappear.
A good engagement makes the stop conditions visible. We would rather reject a weak automation case than hide its economics or risk behind an impressive interface.
- A second model is not an independent safety layer if it shares the same blind spot.
- A confidence score is not meaningful unless it is calibrated against real task outcomes.
- Prompt instructions cannot replace permissions and deterministic validation.
- Human review does not work when reviewers lack evidence, time, or clear authority.
Related services
Build the missing layer.
Use the focused pages below to inspect strategy, workflow, implementation, and evaluation.
FAQ
Questions, answered plainly.
Specific answers beat vague reassurance. If your question depends on the workflow, we will say so.
How do you evaluate an AI agent?
Evaluate the end to end task across representative and high risk cases. Check evidence retrieval, reasoning outputs where exposed, tool choice, tool arguments, state, policy compliance, escalation, final action, latency, and cost. Use automated checks plus calibrated human review.
What guardrails should an AI agent have?
Guardrails depend on the action, but common layers include input validation, least privilege tools, structured outputs, policy checks, evidence requirements, rate limits, approval gates, idempotency controls, audit logs, and safe fallback behavior.
Are model based evaluations reliable?
They can be useful for scalable comparison, but they need task specific rubrics, validation against human judgment, and monitoring for bias or drift. Deterministic assertions should be used wherever the expected behavior can be checked directly.
How often should agent evaluations run?
Run regression evaluations before releases that change models, prompts, tools, policies, retrieval, or workflow logic. Continue sampling live outcomes because production data and operating conditions change even when the code does not.
Start with the workflow
Bring us the process that keeps breaking.
We will map the work, identify the right automation boundary, and tell you plainly whether an agent belongs there.
Discuss the workflow