How to Analyze AI Agent Conversations at Scale and Find Fixable Failures

Keep the original user request, each assistant turn, tool name and arguments, tool result, retrieved context when it influenced the answer, agent or routing path, and the prompt or configuration version. Also keep timestamps and an outcome signal such as a handoff, a corrected answer, a task completion event, or a human review. A final response alone is rarely enough to identify where the behavior went wrong.

Use stable IDs to connect turns in one customer conversation without resending its entire history as if it were new evidence. If a trace omits the tool result or the configuration version, label that record incomplete for the question at hand. Do not silently treat missing evidence as a successful interaction.

A single average blends unrelated behavior. Split the conversations by the decision that could fail: intent, agent route, tool selection, retrieval source, model version, locale, account tier, or release window. Start with the segment that has a concrete business consequence, not merely the largest volume.

For example, group support conversations where the agent promised a refund before checking an order. The denominator is eligible refund requests with a recorded tool path. The numerator is requests where the refund action occurred before the required lookup. A group such as 'bad conversations' is not a pattern a team can test or fix.

An evaluator, feedback score, or alert can identify conversations worth review. It does not by itself explain the failing step. Current trace tools support attaching feedback to traces and threads, then filtering or evaluating selected production records. Use that capability to narrow the review set, then inspect the full sequence before calling a prompt, tool, retrieval, routing, or application change the cause.

A repeatable failure can still have more than one cause. A wrong answer after a correct tool call points somewhere different from a wrong tool call, an empty retrieval result, or a downstream service error. Keep unresolved cases in an explicit bucket. Forcing them into the most convenient pattern creates a false denominator and a misleading fix candidate.

Choose a pattern only when the team can state the user harm, count eligible cases, and name a layer it can change. Frequency matters, but so do severity, confidence in the diagnosis, and regression risk. A rare unsafe outcome may outrank a common awkward response. A frequent API outage may be important but belongs with the service owner rather than a prompt experiment.

Write the candidate as one sentence: 'Change this versioned behavior at this layer to reduce this named failure among these eligible conversations, while retaining these known-good behaviors.' That sentence prevents a general request to improve quality from expanding into an untestable rewrite.

Build a shared test set from the observed failure family and nearby conversations that already work. Keep the control and candidate separate, then compare the same task-completion, failure-pattern, and regression measures. A candidate that improves one failure while creating new tool mistakes is not a clean win.

Converra uses this evidence to keep diagnosis, candidate comparison, approval, and the later production result distinct. It can help a team inspect the failing step, generate a bounded prompt or configuration candidate, and run it against the current behavior. The team that owns the agent still approves and applies its change.

Record the release time, changed version, and any overlapping model, routing, or policy change. After release, apply the same eligibility rules and detector to new conversations. Compare the targeted pattern with a comparable pre-change window, and check the known-good behaviors that the candidate was required to preserve.

A better simulation result is evidence for a candidate, not a production result. If the failure persists, say not fixed. If another material change overlaps the window, say confounded. If there are too few eligible conversations, retain insufficient evidence rather than announcing success. Those outcomes make the next diagnosis more useful than a claim based on a chart or a green test run.

A coding agent can collect the saved runs for one pattern, summarize the tool path, identify missing fields, and prepare a bounded configuration change. Ask it to preserve the denominator, version, and unresolved cases. Do not ask it to turn an edited prompt or one successful replay into a verified result.

With Converra MCP, begin with the saved account and agent inventory, inspect step-level diagnosis for the failed run, then use the authorized optimization and simulation tools. After the team applies an approved change, inspect deployment impact and verification evidence. State-changing actions remain subject to the connected account's scopes and the user's authority.