A launch decision is coming
Your agent works in demos and internal testing. Before real customers rely on it, you need an independent view of what works, what fails and what must change.
PRIMARY SERVICE
A clear baseline. A practical improvement plan.
An independent assessment of how your AI agent performs against agreed workflows, business rules and acceptance criteria, with evidence your leadership can inspect before release.
When an assessment fits
Leadership wants evidence
Stakeholders are asking how you know the agent is ready. A documented baseline with denominators, severity and limits answers that question directly.
Something feels off
Users report inconsistent behavior, or the team disagrees about quality. A structured assessment replaces opinions with reproducible cases.
A focused engagement
Scope before scoring
Select one agent or bounded workflow. Identify user groups, tools, known failures, version details and the release decision. Agree exclusions and the test environment before execution.
Inputs from your team
Access to a test endpoint or runnable agent, business policies, representative examples, approved interaction samples, expected outcomes and a subject-matter reviewer.
Tests that reflect your work
A reviewed dataset spanning common journeys, high-impact tasks, edge cases, incomplete inputs, tool failures and multi-turn conversations, with a held-out set for final comparison.
Evaluation dimensions
Task and tool execution
Outcomes, tool choice, argument validity, interpretation of tool results, state transitions, stopping behavior and handoff accuracy.
Hallucinations and grounding
Invented facts, policies and citations; claims not supported by approved sources; and whether the agent admits uncertainty instead of guessing. Measured as a rate by workflow and type.
Reliability
Variability across repeated runs of the same scenario. We report how often the agent succeeds consistently, not only whether it succeeded once.
Conversation handling
Context retention across turns, recovery from misunderstandings, handling of changed requests and smooth escalation to a person.
Time and cost
Latency distributions, usage, retries and estimated cost per successful task. Evaluation overhead is reported separately from agent execution cost.
Business-rule compliance
Your policies expressed as testable rules: eligibility, limits, required disclosures and approval steps, checked case by case.
What a finding looks like
Illustrative example only. Not a client result.
Scenario
A customer asks to change a booking to a date outside the change window, mid-conversation, after first asking about baggage.
Expected versus actual
Expected: explain the policy and offer the permitted options. Actual: the agent confirmed the change in text, but no change tool call occurred.
Severity and next step
High: an action hallucination. The customer is told something untrue about their booking. Owner: workflow engineering. Next step: add an outcome check and retest with five variations.
What you receive
Baseline scorecard
Results by scenario and dimension, with sample counts, thresholds, repeat-run results and a plain statement of what the evidence does and doesn't cover.
Prioritized findings
Each finding includes severity, reproducible inputs, expected versus actual behavior, supporting evidence and a proposed owner or next action.
Decision workshop
A working session to review release options and agree a ranked improvement backlog. The report supports your decision; it does not guarantee future performance.
Reviewed dataset
The test cases, expected outcomes and labels created during the engagement, in a format your team can run again.
Rubrics and checks
Scoring definitions, calibrated judge prompts and code-based checks used in the assessment, with notes on known limitations.
Retest plan
The specific cases and variations to rerun after fixes, so improvement can be confirmed rather than assumed.
Engagement stages
Discover and agree
Confirm scope, inputs, acceptance criteria, dataset coverage, timeline and deliverables in a short written plan.
Run and review
Execute baseline tests, investigate failures, calibrate judges with your experts and separate agent failures from evaluator disagreements.
Recommend and hand over
Present findings, agree next actions and deliver the dataset, rubrics and reusable evaluation assets covered by the engagement.
Why it matters
Your agent speaks for you
In Moffatt v. Air Canada (2024), a tribunal held the airline responsible for incorrect information its chatbot gave a customer. Testing what your agent says and does is part of owning it.
Problems are cheaper before launch
A failure found in testing is a backlog item. The same failure found by customers is a support cost, a refund or a lost relationship.
A baseline makes every later change measurable
Once a reviewed benchmark exists, every model swap, prompt edit or new tool can be compared fairly against it.
Frequently asked questions
How long does it take?
Duration depends on scope, access and reviewer availability. We confirm a timeline in the written plan after discovery.
Do you need production data?
Not necessarily. Approved samples help realism, but we can build scenarios from policies, expert examples and reviewed synthetic cases.
What if our agent isn't finished?
A runnable version with stable core workflows is enough. Early assessment often prevents expensive rework later.
Make your next decision with evidence.
Start with one workflow, clear success criteria, and an evaluation plan your team can use.