What happened?
Reconstruct the execution path across models, prompts, context, memory, tools, policies, code versions, and external systems.
PRODUCT
The reliability layer above your agent stack.
Autobots reconstructs production failures, identifies the earliest unrecovered failure, creates reusable regression coverage, and validates candidate changes for human review.
ONE WORKFLOW
From incident to evidence-backed fix.
Autobots brings together the evidence needed to answer four questions.
What failed first?
Identify the earliest failure that was not recovered downstream—not merely the final incorrect output.
What should change?
Generate a regression case and propose a focused change at the actual repair locus.
How do we know it is safe?
Replay the incident and related scenarios across task success, business outcomes, cost, latency, and policy compliance.
DIFFERENTIATION
Not another tracing dashboard.
Observability and evaluation tools are important inputs. They usually stop before the most labor-intensive part of the workflow.
Traditional observabilityAutobots
Shows individual spansReconstructs the causal incident
Explains what occurredIdentifies what likely failed first
Scores the final outputEvaluates the workflow and business outcome
Leaves test creation to the teamGenerates a reusable regression case
Leaves remediation to the engineerProposes a focused system change
Requires separate validationReplays the incident and related scenarios
Produces dashboardsProduces evidence and a review-ready change
EARLY ACCESS SCOPE
Coverage across the complete agent system.
Early access is focused on failures that are common, expensive, and diagnosable.
Tool-use failures
- Wrong or missing tool call
- Incorrect or incomplete arguments
- Schema ambiguity
- Response misunderstood
- Permission or authentication failure
- Duplicate or conflicting actions
Context and memory failures
- Relevant context omitted
- Irrelevant context dominated
- Wrong retrieval evidence
- Stale memory overrode current state
- Conflicting facts unresolved
- Important history lost
Planning and orchestration failures
- Wrong skill or sub-agent
- Plan failed to adapt
- Failed step not recovered
- Retries caused loops
- Timeout or budget termination
- Incorrect stop criteria
Evaluation failures
- Style scored over correctness
- Judge contradicted business outcome
- Deterministic condition missing
- Neighboring regression untested
- Dataset drifted from production
COMPOUNDING RELIABILITY
Every incident should make the agent harder to break.
A resolved incident should leave behind a known failure signature, reusable regression test, clearer evaluator, documented repair, and stronger release gate.
Faster incident resolution
Spend less time locating the relevant trace, reconstructing state, and debating possible causes.
Fewer recurring failures
Convert production incidents into regression coverage so the same failure cannot quietly return.
Safer agent changes
Test prompt, tool, model, policy, and code changes before deployment.
Faster releases
Reduce the manual work required to establish whether an agent change is ready for production.
Clearer accountability
Locate the repair in the model, harness, tool, memory system, runtime, or business logic.
Better evaluation coverage
Build evaluations from actual production behavior, not only imagined test cases.
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.
Observe→Detect→Localize→Generate→Replay→Review→Deploy→Measure
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.