How reliable are AI agents?
Why one successful demonstration is insufficient, and how to check quality using real enquiries.
An AI agent can misunderstand a request, choose the wrong tool or misinterpret a result. Reliability needs to be assessed for a specific process and set of cases. A universal accuracy percentage without a testing method tells a business owner little.
Bounded tasks, action checks, measurement and human handover support reliability. Define in advance when an agent must stop before an important operation.
Define a correct result
For classification, this means the right category. For support, an answer grounded in an up-to-date document. For CRM entry, correct fields without duplication. For booking, consistency with availability and terms.
Evaluate each task separately. A fast response is not proof of a correct action, and polished wording does not prove that data was saved. Distinguish model quality from technical service availability.
Build a representative test set
Use anonymised ordinary enquiries and difficult cases: spelling errors, different languages, contradictions, missing information and out-of-scope requests. Include unavailable tools and repeated events.
Describe the expected result before testing. Clarification or human handover may be the correct outcome. Refusing to answer is not automatically a failure.
Measure what matters
Track correctly completed tasks, incorrect actions, handovers, latency and cost. Testing 100 examples gives a result for those examples rather than a guarantee for all future requests.
Consider the severity of mistakes. A wrong category and a falsely confirmed payment have different consequences. Launch criteria should reflect the risk of the particular action.
Useful limits and checks
Provide only the tools needed. Validate formats and allowed values before changing a record. Retrieve prices, availability and statuses from the system of record rather than generating them from memory.
Use staff approval for sensitive actions. Limit steps, runtime and budget. When information is missing or a tool fails, provide a clear fallback.
Keep checking after launch
Review mistakes and add them to the test set. Re-test after changing the model, instructions or knowledge base. Avoid changing several components together when that would obscure the cause of a quality change.
A pilot may show that full autonomy is unsuitable. Drafting assistance can still be valuable if it reduces manual work while preserving staff control.
Why one quality percentage is misleading
In a hypothetical 100-case test, 96 correct outcomes and four failures say little about consequences. An inaccurate phrase and a wrong CRM action should not carry identical business weight.
Separate document answers, clarifications, handoffs and changes. The example describes evaluation design, not a promise of 96% production quality. Measure availability, answer quality and action correctness separately. A responsive service can answer incorrectly; a good answer can still fail to complete the operation.
Build a representative test set
Use anonymized real enquiries, typos, languages and incomplete conditions. Include missing knowledge: asking a question or handing off may be the correct outcome.
Define acceptable results and forbidden actions in advance. Align reviewers' criteria so personal preference is not confused with an actual error. Keep some cases independent of tuning instructions. Memorizing demonstration questions does not establish quality on new enquiries.
Limit the impact of errors
Start with reading and drafts, cap steps and spending, and validate tool parameters with ordinary rules. Sensitive changes should require human approval.
Provide a stop control and manual route. Missing information should trigger clarification, and unavailable CRM should trigger recoverable handling rather than invented data. Alert the team about significant recurring problems without treating every normal clarification as an incident. Staff need a clear next action.
Maintain quality after launch
Add anonymized failures to the test set. Repeat relevant checks when models, instructions, tools or documents change.
Review results by language, channel and task. Strong averages can hide weak scenarios. Distinguish stale knowledge, retrieval mistakes and incorrect actions. Agree pilot stop and expansion criteria. Reliability depends on concrete tests, limits and error handling within the business process.
What to check
- Define correctness for each task.
- Test ordinary cases, exceptions and outages.
- Separate accuracy, availability, cost and error severity.
- Re-test changes and preserve human handover.
Sources and documentation
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