What happened?
Reconstruct the execution path across models, prompts, context, memory, tools, policies, code versions, and external systems.
THE AGENT RELIABILITY ENGINEER
Turn production agent failures into validated fixes.
Autobots finds the earliest unrecovered failure across models, context, memory, tools, policies, and runtime—then creates a regression test and replay-validates the smallest useful change.
Keep your existing observability and evaluation stack. Human approval is required before any change reaches production.
ILLUSTRATIVE INCIDENT / 0241ANALYSIS COMPLETE
01Intent resolved12ms
02Plan assembled46ms
03Entity matched to stale recordFIRST FAILURE
04Tool call completed208ms
05Incorrect outcome returnedSYMPTOM
CANDIDATE REPAIRValidate entity state before tool executionReplay: 14/14 related scenarios passed
- Plan multi-step work
- Call external tools
- Read and write business data
- Run long-lived workflows
- Take consequential actions
THE PROBLEM
Agent failures rarely begin where they end.
The final bad response is often only the visible symptom. The real failure may have started several steps earlier.
01User intent
02Plan
03ContextEARLIEST FAILURE
04Memory
05Tool call
06Runtime
07Final responseVISIBLE SYMPTOM
“Your tracing platform can show hundreds of spans. Your engineers still have to reconstruct the crime scene.”
The planner may select the wrong skill. Relevant context can be omitted. Stale memory can override current information. A valid-looking tool call can carry the wrong arguments, or a changed permission can alter the result.
Today, resolving one incident can pull in engineering, product, domain, and platform teams—then end with a one-off test and a deployment whose real impact is still unknown.
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.
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.
Bring the evidence you already have
Import traces, evaluations, feedback, version history, repository context, and business outcomes.
Reconstruct the causal execution graph
Map the complete incident, then rank likely failure points with the evidence behind each conclusion.
Create a reusable regression case
Turn the production incident into a reproducible scenario with durable assertions and evaluation criteria.
Generate the smallest useful fix
Propose a focused change at the actual repair locus, with evidence, expected effect, and known uncertainty.
Validate before deployment
Replay the incident and related scenarios, compare outcomes, and hand the evidence to a human reviewer.
THE WORKFLOW SHIFT
Replace reconstruction and guesswork with a controlled reliability loop.
- User reports an incorrect outcome
- Team searches logs and traces
- Engineers inspect a long trajectory
- Several causes are debated
- A one-off evaluation is created
- A prompt or code change is attempted
- Adjacent scenarios are tested manually
- Team deploys and waits
- Incident is submitted
- Execution path is reconstructed
- First unrecovered failure is identified
- Regression case and candidate fix are generated
- Change is replayed against related scenarios
- Team reviews the evidence and decides
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
Initial failure coverage
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.
WHO IT IS FOR
Built for teams whose agents are already doing real work.
Your team is likely ready if you have:
- Production, customer-facing, or business-critical agent
- Several tools, models, skills, or sub-agents
- Reliability investigations every week
- A measurable terminal business outcome
- Existing traces or execution logs
- A need to improve without replacing the entire stack
Autobots is not initially designed for:
- Still searching for a first AI use case
- One-shot content generation
- Low-volume prototypes
- No production traces or observable outcomes
- No meaningful reliability incidents
- Incorrect answers have no operational consequence
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
SECURITY PRINCIPLES
Your most sensitive AI telemetry should not become someone else’s training data.
Agent traces can contain conversations, source code, internal instructions, records, credentials, and confidential tool outputs.
Read-only by default
Collect only the evidence required to investigate and validate an incident.
Human-controlled changes
No recommendation is merged or deployed without an authorized human decision.
No training by default
Customer prompts, traces, code, and outputs are not used to train shared models without explicit permission.
Complete auditability
Keep the evidence, diagnosis, validation, approvals, and deployment outcome reviewable.
DESIGN-PARTNER PILOTS
Bring us one agent failure your team could not explain quickly.
Private, paid pilots are scoped around real incidents, your existing stack, and measurable reliability outcomes.
QUESTIONS
Frequently asked questions.
01Does Autobots replace our existing telemetry?+
No. Autobots is designed to ingest evidence from tools such as LangSmith, Langfuse, Braintrust, Arize, Datadog, and your existing stack. It focuses on the workflow from observed failure to causal diagnosis, regression coverage, candidate remediation, and validation.
02How is this different from an evaluation platform?+
Most evaluation platforms help teams define tests and score outputs. Autobots starts with real production behavior, connects the symptom to the underlying system failure, then creates the evaluation and candidate change needed to prevent recurrence.
03Does the product automatically modify production agents?+
No. Early versions require human review before any prompt, configuration, tool, policy, or source-code change is applied.
04Do we need an existing evaluation dataset?+
No. Autobots can begin with production incidents, traces, user feedback, and business outcomes. Each investigated incident can become part of a durable regression suite.
05What kinds of evidence can the system use?+
Potential inputs include traces, prompt and model versions, tool definitions and responses, retrieval results, memory reads and writes, application logs, feedback, support tickets, source context, feature flags, business-state changes, cost and latency metrics, human labels, and evaluator scores.
06What agent frameworks will be supported?+
The first integrations will be selected based on design-partner demand. The architecture is intended to support OpenTelemetry-based traces as well as common agent frameworks and commercial observability platforms.
07Can the system diagnose domain-specific failures?+
Yes, when sufficient evidence about expected business behavior is available. Outcomes can be represented through deterministic checks, business-state validation, customer-defined policies, human labels, and calibrated model-based evaluators.
08What happens when the diagnosis is uncertain?+
Autobots should distinguish observed facts, likely explanations, alternative hypotheses, missing evidence, and confidence levels. A plausible model-generated explanation is never presented as proven.
09Can Autobots run in our environment?+
Private and customer-controlled deployment options are part of the enterprise roadmap. Early-access architecture and deployment requirements are reviewed individually with design partners.
10How is pricing expected to work?+
The planned model combines an annual platform commitment with usage based on production analysis and replay volume. Early design-partner pilots are paid and scoped by incidents, integrations, and security requirements.
11What do you need from us to begin?+
Start with one meaningful production failure, a sanitized trace or execution log, the expected and actual outcomes, relevant version information, and a technical owner who can review the diagnosis.
YOUR TRACES ALREADY CONTAIN THE EVIDENCE
Stop debugging the final answer. Find the first thing that went wrong.
Turn production failures into regression tests, validated fixes, and a reliability system that improves with every incident.