Stop guessing why the AI fails. Reproduce the issue, isolate the cause and test the fix.

We help diagnose inconsistent answers, broken automations, weak prompts, missing context, failed tool calls and integration problems. The goal is a reproducible problem statement, a practical fix and a test set that helps prevent the same issue from returning unnoticed.

View troubleshooting packages See two concrete deliverables

Reproduction firstRoot-cause analysisRegression testing

Direct answer

How do you troubleshoot an AI system?

We first reproduce the failure with the same input, context and configuration. Then we isolate likely causes one layer at a time: instructions, source data, context assembly, model settings, structured output, tool calls, external APIs and downstream workflow logic. Once the cause is identified, we apply the smallest practical change and rerun both the failing example and a wider regression set to check that the fix did not create a new problem.

Two concrete example deliverables

What a completed troubleshooting engagement can look like

These are sample scopes showing our diagnostic method. Results depend on whether the issue can be reproduced and whether the relevant platform exposes enough logs, settings or test access.

Deliverable A · Answer quality

Chatbot inconsistency diagnosis and regression test pack

For a chatbot that gives different answers to similar questions, ignores policy or invents details when source information is incomplete.

Capture failing examplesCollect exact prompts, user messages, expected behavior, actual behavior and any relevant source context.
Reproduce the failureRun the same scenario repeatedly and with controlled variations to determine whether the issue is systematic or intermittent.
Inspect instruction and context layersReview system rules, knowledge retrieval, context ordering, contradictory content and places where the model is asked to infer missing facts.
Apply targeted fixesAdjust instructions, source structure, fallback rules, context selection or output constraints rather than rewriting everything at once.
Build a regression setCreate tests for the original failure plus nearby scenarios, unsupported topics, ambiguous wording and policy-sensitive cases.
Verify and documentRerun the test set, record remaining limitations and provide concise notes explaining the issue and the changes made.

Typical outputs

Reproducible issue summary
Likely root-cause analysis
Prompt / context / fallback changes
Before-and-after test evidence where available
Regression test checklist
Remaining limitations and next steps

Typical fit: 1–6 sessions depending on reproducibility

Deliverable B · Automation failure

Broken AI automation diagnosis with recovery paths

For a workflow that fails intermittently, stops after the AI step, loses fields or produces downstream errors.

Map the failing pathIdentify the exact trigger, nodes or steps involved and the point at which expected data or behavior diverges.
Inspect logs and payloadsReview safe execution logs, request/response shapes, status codes, field mappings and error messages where available.
Isolate the failure classDetermine whether the issue is authentication, malformed data, model output, timeout, rate limit, branching logic or a third-party connector problem.
Correct the workflowFix mapping, validation, prompt output, branching, retry or authentication configuration within the permitted platform capabilities.
Add recovery behaviorDefine retries, alerts, dead-letter/manual queues or stop conditions so failures become visible instead of silently losing work.
Regression-test the flowRun successful, incomplete, invalid and intentionally failed examples and document how to identify and recover from future failures.

Typical outputs

Workflow failure map
Root-cause findings and evidence
Corrected field, branch or prompt configuration
Error / retry / alert behavior
Regression test matrix
Recovery and handover notes

Typical fit: 1–10 sessions depending on system complexity

Scope note: troubleshooting cannot guarantee access to third-party internal logs or resolve outages controlled by another provider. Where a root cause sits outside your accessible configuration, we document the evidence and the practical escalation path.

Before troubleshooting

What helps us diagnose faster

The exact failing example is usually more useful than a long description saying “it sometimes breaks.”

Exact failing input

Share the prompt, request, payload or user action that triggered the problem.

Expected vs actual

Show what should have happened and what happened instead.

Relevant configuration

Provide screenshots, screen share or safe config details for the affected prompt, workflow or integration.

Logs and error messages

Share relevant execution logs or status codes without exposing passwords, tokens or unrelated personal data.

Recent changes

Tell us whether prompts, models, APIs, connectors, source data or account permissions changed before the issue appeared.

Frequency

Explain whether the problem occurs every time, for specific inputs or only intermittently.

Scope boundaries

We focus on evidence, not guaranteed “perfect AI.”

AI systems remain probabilistic and third-party services can change. Our goal is to reduce known failure modes, make behavior more predictable and leave you with tests that reveal regressions. We do not promise zero errors or uninterrupted external platform availability.

Related services

Once the issue is understood, fix the right layer

Troubleshooting often leads to a focused improvement in prompts, integrations or workflow design.

Have a specific AI failure?

Bring the failing example. We will start by reproducing it.

A focused support session is often enough to identify the next technical step; larger systems can continue across a multi-session package.

View AI support pricing