Quality engineering service
AI Agent Evaluation Services
Build production-relevant evals that show how an AI agent behaves across real tasks, tools, data, policies, and failure conditions before each release.
Service explained
What does an AI agent evaluation service measure?
An AI agent evaluation service measures whether an agent completes representative tasks correctly, safely, consistently, and within agreed operational limits. QA-CS begins with the full agent system: instructions, model, retrieval sources, memory, tools, permissions, user interface, business rules, and human escalation points. We define realistic scenarios and expected outcomes, then combine deterministic checks with rubric-based expert review where context matters. Depending on the use case, evaluation may cover task completion, answer quality, grounding, citation accuracy, tool selection, parameter correctness, side effects, policy compliance, refusal behaviour, recovery from errors, latency, and cost. Results are compared across relevant prompt, model, tool, or data changes so teams can see regressions as well as improvements. Every report records the evaluation conditions, scoring basis, observed failures, reviewer judgement, and known limitations. The purpose is not to claim that an agent is universally safe; it is to provide bounded, repeatable evidence for a named release decision.
Business value
What this service helps you achieve
Evaluate AI agents for task success, tool use, grounding, safety, reliability, latency, and cost with human-reviewed release evidence.
Detect regressions across model and workflow changes
Give release teams reviewable evidence and limitations
When to use this service
Recognise the need before risk becomes delay.
Evaluation is most useful when it reflects the work the agent must perform in production. We connect user intent, tool calls, retrieved context, policies, and downstream effects to observable acceptance criteria rather than relying on a generic benchmark alone.
- An AI agent is moving from prototype to production
- A prompt, model, tool, or knowledge source is changing
- Teams need evidence beyond demos and generic benchmarks
Our approach
Evidence at every stage.
Evaluation is most useful when it reflects the work the agent must perform in production. We connect user intent, tool calls, retrieved context, policies, and downstream effects to observable acceptance criteria rather than relying on a generic benchmark alone.
- 01Map users, tasks, tools, data, and consequences
- 02Define representative cases and acceptance criteria
- 03Execute automated checks and expert review
- 04Compare results, investigate failures, and guide release decisions
Production-relevant evals
Measure the jobs users expect the agent to complete.
Public benchmarks can inform model selection, but they rarely represent a company’s tools, terminology, access rules, customer journeys, and failure costs. We construct an evaluation set from high-value tasks, common journeys, difficult edge cases, historical defects where available, and plausible misuse. Each scenario states the expected outcome and the evidence required to score it.
- Single-turn and multi-turn task completion
- Tool choice, arguments, sequencing, and effects
- Retrieval relevance and grounded responses
- Policy, refusal, escalation, and recovery behaviour
Evaluation design
Use the right scorer for each kind of claim.
Exact checks are valuable for structured outputs, calculations, schemas, permissions, and tool calls. Rubrics and expert review are needed for nuanced quality, relevance, tone, completeness, and risk. Model-based judges may assist at scale, but they are calibrated against human-labelled examples and are not treated as independent proof of correctness.
- Deterministic assertions for objective outcomes
- Rubrics for contextual and qualitative behaviour
- Calibrated model-assisted scoring where suitable
- Human adjudication for ambiguity and high-impact failures
Continuous assurance
Keep evidence connected to every meaningful change.
Agent quality can shift when a prompt, model version, retrieval index, tool, policy, or dependency changes. We organise evals so the team can rerun the right coverage, compare results with a known baseline, inspect failures, and decide whether a change is acceptable. The evaluation suite becomes a maintained release control rather than a one-time demonstration.
- Versioned datasets and configuration records
- Baseline comparison and regression analysis
- Failure clusters and prioritised remediation
- Release criteria with documented exceptions
Deliverables
Clear outputs your team can use
Documentation is concise, traceable, and written for engineering, product, and business stakeholders.
- Evaluation strategy and risk taxonomy
- Versioned scenarios and scoring rubrics
- Execution results with failure analysis
- Human-reviewed release and improvement report
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Explore serviceFrequently asked questions
What teams usually ask
Reviewed by the QA-CS quality engineering teamContent updated . Scope and controls are confirmed for each engagement.
Which AI agents can be evaluated?
The method can be adapted to customer support, internal knowledge, coding, research, workflow, and tool-using agents. Scope depends on the agent’s architecture, permitted access, and consequences of error.
Do you use model-based judges?
They may support scale when appropriate, but scoring is calibrated against explicit criteria and human-labelled examples. High-impact or ambiguous results remain subject to expert review.
Can the evaluation compare models or prompts?
Yes. Controlled comparisons can show how relevant configurations affect task success, safety, latency, cost, and failure patterns under the same evaluation conditions.
Does passing an evaluation prove the agent is safe?
No. Evaluation provides bounded evidence for tested scenarios and conditions. Reports state coverage, assumptions, uncertainty, and residual risk so people can make an informed decision.
Start with clarity
Discuss ai agent evaluation services
Share the product, release, or operational challenge. We will help define the right next step.