How Neatlogs works

Neatlogs helps teams answer four connected questions:

1. What happened?
2. Was the behavior acceptable?
3. Why did it happen, and what should change?
4. Did the change improve the intended behavior?

Most agent teams already attempt each step somewhere. Traces live in an observability tool, quality reviews live in spreadsheets, customer context lives in Slack, and proposed fixes lose their evidence when copied into a ticket. Neatlogs keeps those steps connected to the same production evidence.

The reliability loop

Observe
Production traces show what the agent did
    ↓
Judge
Detections, Evals, and people identify concerning behavior
    ↓
Organize
Related evidence becomes an incident the team can own
    ↓
Investigate
Failed and successful runs are compared to find a supported cause
    ↓
Act
A reviewed, focused handoff goes to the team's normal workflow
    ↓
Verify
Known failures, nearby behavior, and new production runs are checked
    ↓
Repeat when the behavior returns or the standard changes

This is a loop rather than a pipeline with a permanent finish. Agent behavior changes when prompts, models, tools, data, policies, and traffic change. A fix that works today still needs production evidence tomorrow.

Follow one failure through the loop

Suppose a refund assistant cites a policy that expired before the customer made the request.

1. Observe what happened

The trace records the request, retrieval query, returned documents, model input, tool calls, and final answer. It establishes the execution path and the evidence available to the agent.

The trace does not decide whether the answer was acceptable. It may show that an expired document was retrieved, but it does not know which business policy should have applied unless that context was captured or supplied.

2. Judge the behavior

A teammate flags the exact sentence and adds the expected behavior: use the policy that was active on the request date.

If the team can define a repeatable condition, a detection can label similar evidence. If the decision requires a quality standard, an Eval can collect structured human or AI verdicts. Customer and team reports can contribute context that telemetry cannot infer.

These signals are evidence, not root causes. A failed Eval or detection match says where a condition appeared. It does not prove why.

3. Organize related evidence

When the behavior represents a real problem, related runs and detection matches are grouped into one incident. Neatlogs raises many incidents itself from recurring errors, slow operations, and detection matches. You can also ask for one in investigate.

The incident gives the problem a stable place for priority, status, recurrence, investigations, and resolution. It prevents every observation from becoming an isolated ticket.

4. Investigate the cause

The investigation compares the failed run with successful refund runs. It finds the earliest meaningful difference: successful runs include the request date in the retrieval filter, while failed runs do not.

The report cites the supporting spans, records uncertainty, recommends a bounded action, and names cases that should be retested. If the available evidence cannot support a clear cause, the honest outcome is no clear diagnosis, not a fabricated explanation.

5. Review and hand off the change

A person opens the cited evidence, challenges the reasoning, and checks that the recommended action addresses the supported cause.

The focused handoff carries the problem, evidence, constraints, intended behavior, and tests into the team's workflow: a coding agent through Fix in, or a Linear or Jira ticket through Push to. Repository access, code review, approval, and deployment remain governed by the team's existing controls.

6. Verify the result

The team reruns the original failure, checks nearby cases that should remain unchanged, applies the relevant Evals, and watches new production traces for the same signal.

Passing one test is not enough. Verification should show that the intended behavior improved and that the change did not simply move the failure elsewhere. Neatlogs supplies the evidence for this check, but it does not mark a fix as verified: the team decides when to resolve the incident.

The objects in the loop

Each object answers a different question.

A fix brief is documentation shorthand for the focused handoff assembled from the incident and its investigation. In the product, this material appears as the incident's Recommended action and the context sent through Fix in, Copy context, or Push to, rather than as a separately named object.

How evidence enters Neatlogs

Production telemetry

Native and imported traces provide the execution record. Spans preserve prompts, model responses, tool calls, retrieval, timing, errors, and available metadata.

Machine-recognizable signals

Detections label known conditions. They make repeated behavior searchable and measurable without claiming that every match is a defect.

Structured quality judgment

Evals apply defined questions and verdicts to selected traces or spans. Human reviewers establish the business standard, and tested AI evaluators can apply it at larger scale.

Team and customer context

Flags, comments, reports, and connected collaboration sources explain why observed behavior matters and what was expected.

Explicitly granted external context

Investigations and advanced evaluators can use configured connections or tools when the task requires information beyond the trace. Access remains scoped to what has been granted.

Signals become incidents through judgment

Not every detection match, failed Eval, or customer report should become an incident.

Neatlogs's incident analysis groups recurring errors, slow operations, and detection matches, and a model screens out matches that do not indicate a real problem. People then decide which incidents to investigate, resolve, or archive. Ask for a new incident only when the evidence represents an actionable problem that Neatlogs has not already raised. Group observations when they describe the same behavior, and keep them separate when the impact, cause, or responsible team differs.

This judgment gate reduces noisy queues and keeps the incident focused on resolution rather than raw event volume.

What Neatlogs automates

Neatlogs can automate evidence collection, normalization, detection evaluation, incident grouping, Eval routing, AI judgments, investigation steps, report generation, handoff preparation, and monitoring.

Automation reduces repeated work. It does not remove the need to define standards, inspect uncertainty, or control changes.

What remains under human control

People remain responsible for:

• deciding what data may be captured and connected;
• defining acceptable behavior and business policy;
• reviewing uncertain or high-impact conclusions;
• granting access to external tools and repositories;
• deciding which incidents to investigate, resolve, or archive, and choosing handoff destinations;
• approving code, deployments, and operational changes;
• deciding what evidence is sufficient to call a fix verified.

Where to begin

Next step

Understand traces, spans, and sessions →