Right tone, wrong action
The agent says the refund is processed, but the tool call used the wrong order number. The customer hears good news; the system records nothing. Only outcome checks catch this.
AI AGENT TESTING & EVALUATION • PROFESSIONAL SERVICES
Know whether your AI agents are ready for real customers.
Independent testing and evaluation for AI agents. We define success with your team, test the workflows your agent must complete, catch hallucinations and wrong actions, and turn uncertain behavior into evidence for better release decisions.
Agents fail quietly
An AI agent can write a confident, well-formatted reply and still leave the job undone. These failures rarely show up in a demo.
Works once, fails the fifth time
Agents are probabilistic. A scenario that passes in a demo can fail on the next run with the same input. One good run is an anecdote, not evidence.
The fix that broke something else
A new model, prompt or tool definition improves one workflow and quietly degrades another. Without a fixed benchmark, nobody notices until customers do.
Hallucinations are the trust problem
Hallucinations are the AI failure executives worry about most, and for good reason. In agents, invented information doesn't just mislead; it drives actions.
Invented facts and policies
The agent states a price, feature or refund rule that doesn't exist, with complete confidence. Customers act on it, and your business may be held to it.
Claimed actions that never happened
"Your booking has been changed." No tool was called, or the call failed. Action hallucinations are invisible unless you check the system, not the sentence.
Made-up details in real tasks
Invented order numbers, account details, citations or calculations slip into tool calls and documents, turning a wording problem into a data problem.
Answers beyond the sources
The right document was retrieved, but the answer adds, omits or contradicts what it says. Grounding has to be checked claim by claim.
Guesses instead of "I don't know"
When the answer isn't available, many agents fill the gap with something plausible. Good evaluation rewards honest uncertainty.
How we measure it
Source-anchored tests, unanswerable questions, claim-level checks, action verification and repeated runs give you a hallucination rate you can track and reduce.
Explore serviceWhy evaluation matters now
Organizations are moving from pilots to agents that take real actions. The gap between a promising prototype and dependable production behavior is where value is won or lost.
Most pilots don't reach impact
MIT research published in 2025 reported that about 95% of enterprise generative AI pilots delivered no measurable P&L impact. Teams that cannot show what works struggle to scale anything.
Agent projects are under scrutiny
Gartner predicted in 2025 that more than 40% of agentic AI projects will be canceled by the end of 2027, citing costs, unclear business value and inadequate risk controls. Evidence of value protects the projects that deserve to continue.
AI quality is uneven
A Harvard Business School field experiment found that people using AI did better on many tasks but were 19 percentage points less likely to reach a correct answer on a task outside the model's capability. Knowing where your agent's frontier sits is the point of testing.
Inaccuracy is a top risk
McKinsey's State of AI research consistently lists inaccuracy among the generative AI risks organizations most often experience. Accuracy is not a model property; it is something you measure in your workflow.
Companies own what their agents say
In Moffatt v. Air Canada (2024), a tribunal held the airline responsible for incorrect fare information its website chatbot gave a customer. What an agent tells your customers is your business commitment.
Even specialist tools hallucinate
A Stanford study of AI legal research tools found hallucinated or incorrect answers on a meaningful share of queries, even from products marketed as reliable. Every agent needs testing on its own work.
Three ways to build confidence
Evaluation Assessment
Understand where your agent succeeds, where it fails and what to fix first. Receive a scoped benchmark, evidence-backed findings and a practical improvement plan for your release decision.
Explore serviceEvaluation Implementation
Build reusable test datasets, calibrated judges and release checks in your existing stack. We implement the workflow with your engineers and hand it over.
Explore serviceManaged Evaluations
Keep quality in view as prompts, models, tools and business rules change. Scheduled test runs, approved sample reviews and expert analysis support every release.
Explore serviceGood answers are only part of the job
Does it finish the task?
Test whether the intended business outcome actually happened: the right record changed, the right tool ran with the right arguments, and the handoff reached a person when it should.
Does it work again?
Repeat scenarios to measure variability. Separate a promising demonstration from consistent performance across realistic users, inputs and conditions.
Did the change help?
Compare agent versions on the same reviewed test cases. See gains, regressions, latency and cost before you decide what to release.
What we test
Task completion
Did the agent achieve the business outcome for each scenario? We verify final state directly, not just the wording of the last message.
Tool use
Tool selection, argument accuracy, sequencing, handling of tool errors and knowing when to stop. Many costly failures happen between the question and the answer.
Hallucinations and grounding
Invented facts, policies, citations and actions. We check every claim against your approved sources and every claimed action against real system state.
Multi-turn behavior
Context carried across turns, clarifying questions when information is missing, memory use and correct handoff to humans or other agents.
Reliability
Success rates across repeated runs with recorded configuration. We report how often the agent succeeds every time, not only whether it can succeed once.
Efficiency
Latency, retries, model usage and cost per successful task, measured under the same conditions so version comparisons are fair.
From questions to evidence
01 / Define success
Agree the workflows, users, constraints, acceptance criteria and the decision the evaluation must support, before anyone looks at results.
02 / Evaluate behavior
Combine deterministic checks, calibrated AI judges and expert review on representative, difficult and failure-prone scenarios.
03 / Prioritize improvements
Review findings with your team, retest selected fixes, and leave with reusable evidence, a ranked backlog and clear next steps.
Who we help
Teams preparing to launch
You have a working agent and a date. You need an independent baseline and a list of what must be fixed before real customers arrive.
Teams changing models or workflows
A model migration, new tools or a prompt rewrite is coming. You need a fair before-and-after comparison on the work that matters.
Teams running agents in production
Your agent is live and keeps changing. You need a recurring, documented quality process without building a full evaluation function.
Why independent evaluation
No platform to sell
We are not tied to a cloud, model provider or evaluation tool. Recommendations follow your evidence, and we work with the stack you already have.
A second set of eyes
Builders know what the agent is supposed to do, which makes blind spots hard to see. Independent test design surfaces the cases your team didn't think to try.
Capability you keep
Every engagement leaves datasets, rubrics, checks and instructions your team can run again. We aim to make you less dependent on us, not more.
Works with your stack
Cloud platforms
Agents built on AWS, Microsoft Azure and Google Cloud. We evaluate the deployed workflow using the access and tooling you approve.
Explore serviceModel providers
Agents built with Anthropic Claude or OpenAI models. We run application-specific comparisons for model choices and version migrations.
Explore serviceEvaluation platforms
Patronus AI, Giskard, Braintrust, Confident AI, Arize and Promptfoo. We help you design the evaluations these tools run and make the results useful.
Explore serviceFrequently asked questions
Do we need new tools?
Not necessarily. We work with the tools you already have or help you choose the right one, and build evaluation assets your team can keep using.
Which platforms do you work with?
Agents on AWS, Azure and Google Cloud; Salesforce, ServiceNow and SAP; Databricks and Snowflake; Claude and OpenAI models; and evaluation tools such as LangSmith, Langfuse, Promptfoo, DeepEval and Ragas.
How do we start?
Pick one agent or bounded workflow and the decision you need to make. Use the assessment request form and we will propose a scope.
What do you need from us?
A test environment or endpoint, business rules, example tasks with expected outcomes, and a domain reviewer who can resolve ambiguous cases.
Can you eliminate hallucinations?
No one can honestly promise zero. We measure your hallucination rate, find its causes, test which fixes reduce it and help set acceptable thresholds. We report limits clearly, and you keep the release decision.
Can we use our own data?
Yes, once data handling is agreed. Please don't submit confidential production records through the website form.
Make your next decision with evidence.
Start with one workflow, clear success criteria, and an evaluation plan your team can use.