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AUTOBOTS.
Analyze a Failure

HOW IT WORKS

A controlled path from incident to validated change.

Bring the evidence you already capture. Autobots connects the symptom to the repair locus, creates a regression case, and tests the change before your team decides.

Analyze a Recent Failure

INPUTS, NOT REPLACEMENTS

TracesEvaluationsFeedbackVersionsTool resultsRepository contextBusiness outcomes

HOW IT WORKS

One reliability loop for the entire agent system.

Use production evidence to move from observation to a review-ready change—without replacing the stack that already captures it.

01

Bring the evidence you already have

Import traces, evaluations, feedback, version history, repository context, and business outcomes.

  • Production traces
  • Evaluation results
  • Prompt and model versions
  • Tool definitions and results
  • Repository context
  • Business outcomes
02

Reconstruct the causal execution graph

Map the complete incident, then rank likely failure points with the evidence behind each conclusion.

  • User intent and planning
  • Context, retrieval, and memory
  • Skills and tool execution
  • Permissions and external systems
  • Retries, budgets, and policies
  • Final business outcome
03

Create a reusable regression case

Turn the production incident into a reproducible scenario with durable assertions and evaluation criteria.

  • Expected state transitions
  • Deterministic assertions
  • Domain-specific criteria
  • Cost and latency thresholds
  • Permanent release coverage
04

Generate the smallest useful fix

Propose a focused change at the actual repair locus, with evidence, expected effect, and known uncertainty.

  • Prompt or routing instructions
  • Context or memory policy
  • Tool contract or validation
  • Retry, timeout, or model configuration
  • Application code
05

Validate before deployment

Replay the incident and related scenarios, compare outcomes, and hand the evidence to a human reviewer.

  • Task and business correctness
  • Regression rate
  • Cost and latency
  • Safety and policy compliance
  • Review-ready change

THE WORKFLOW SHIFT

Replace reconstruction and guesswork with a controlled reliability loop.

BEFORE AUTOBOTS
  1. User reports an incorrect outcome
  2. Team searches logs and traces
  3. Engineers inspect a long trajectory
  4. Several causes are debated
  5. A one-off evaluation is created
  6. A prompt or code change is attempted
  7. Adjacent scenarios are tested manually
  8. Team deploys and waits
WITH AUTOBOTS
  1. Incident is submitted
  2. Execution path is reconstructed
  3. First unrecovered failure is identified
  4. Regression case and candidate fix are generated
  5. Change is replayed against related scenarios
  6. Team reviews the evidence and decides

RECURSIVE IMPROVEMENT

Continuous improvement—without uncontrolled self-modification.

Every automated recommendation is a hypothesis. Evidence, testing, human approval, and production measurement determine whether it becomes a real change.

ObserveDetectLocalizeGenerateReplayReviewDeployMeasure

THE HUMAN GATE

No blind self-modification. No automatic production changes.

READY FOR REVIEW

Evidence travels with the recommendation.

Each suggestion includes its supporting evidence, expected effect, known uncertainty, replay results, and related scenarios that may be affected.

01 / Inspect evidence02 / Compare replay03 / Approve or reject

START WITH EVIDENCE

Bring us one agent failure your team could not explain quickly.

No generic sales demo. Start with a real production incident from your agent.

Analyze a Recent Failure Apply for a Design-Partner Pilot