A useful monitoring pilot proves that your team can capture an agent run, identify a meaningful failure, investigate its cause, and validate a change. Set those acceptance criteria before comparing dashboards. Otherwise the product with the most polished demo can win without solving the problem you bought it for.
Use one real workflow in a sandbox and the same evidence contract for every candidate.
Choose a bounded pilot
Pick a workflow your team understands, with a clear owner and verifiable outcomes. A support agent that reads policy and requests refunds is easier to evaluate than “all AI quality across the company.” Include your real framework, custom tools, and asynchronous boundaries where relevant.
Write down what is in scope: environment, task type, traffic volume, content policy, and the failure types you need to investigate. Set a time limit for the pilot and define what decision it must support.
Download the pilot scorecard and adapt its rows to your application. Blank scores are intentional; this is a worksheet, not a prefilled ranking of vendors.
Use failures and healthy controls
Start with a duplicate successful lookup, an answer that contradicts a source, and an unsupported action-completion claim. Pair them with legitimate polling, a justified retry, and a correctly described pending action.
Our synthetic lab provides small examples, but your acceptance test should add application-specific cases. A vendor passing our simple fixtures does not establish production reliability.
Give every candidate equivalent inputs and record any differences. If one integration captures nested tools and another only sees final messages, compare the integration effort and evidence coverage before comparing detection results.
Evaluate five concrete tasks
| Task | Acceptance evidence |
|---|---|
| Capture | One complete run with parent-child relationships and expected IDs |
| Detect | Confirmed findings plus healthy controls and known misses |
| Investigate | A reviewer can reach the relevant message and tool result |
| Validate | A production example becomes a reproducible test with a baseline |
| Operate | Cost estimate, ingestion lag, export policy, and an assigned owner |
For each task, record setup time, custom code required, manual steps, and remaining limitations. Avoid collapsing everything into one weighted score until the team agrees which requirements are mandatory.
Make reviewers do the same investigation
Give an engineer an incident identifier and ask them to explain what happened. Time the work from that starting point. Save the evidence they used and the hypotheses they could not resolve.
Then ask a second reviewer to follow the explanation. If they cannot verify the finding without repeating the entire investigation, the evidence packaging needs work. A readable explanation should connect the user’s request, execution, result, and expected behavior.
Ask for a representative healthy example too. A platform that produces plausible failure narratives for every run may look impressive until someone checks its false-positive rate.
Verify operating requirements
Inspect raw exported payloads for sensitive fields, not just UI masking. Confirm retention, access control, deletion behavior, environment separation, and how failed ingestion becomes visible. Verify contractual requirements directly rather than inferring them from a logo or a pricing tier.
For Agnost, start with the published security guidance and framework setup guides. For other candidates, use their current documentation and a representative account configuration.
Estimate a month using your expected traces, spans, evaluation calls, content size, users, and retention. Include the cost of maintaining a self-hosted deployment if applicable.
End with a decision record
Document which requirements passed, which failed, and which remain untested. Identify the evidence behind the recommendation and the owner of any integration work. “Supported in documentation” and “verified in our pilot” should be separate statuses.
The LangSmith, Langfuse, Braintrust, and Agnost comparison helps form the shortlist. This pilot is how you turn that shortlist into a decision based on your application rather than someone else’s feature matrix.