When production AI goes wrong,get to the answer without losing the day.
TalosRed turns the telemetry you already have into a reviewable incident investigation, so your team spends less time reconstructing failures and more time resolving them.
Example incident record
Unexpected tool selection
Current assessment
The behavior changed after a tool update.
Source-linked findings and unknowns are ready for review.
Evidence
Findings linked
Incident evidence
Scope
Related runs identified
The incident starts with one bad outcome.
Then the real work begins.
The workflow completed. So why was the result wrong?
A customer reports a bad outcome. Engineering opens the trace and nothing obviously crashed, so someone has to work backwards through prompts, tool calls, state, and logs.
Everyone has part of the answer.
Engineering has traces, another person is querying logs, and support has customer context. The useful evidence exists, but the investigation still has to be assembled by hand.
You find a likely cause. The scope is still unclear.
Teams still need to search production history and work out which runs belong to the same incident, not just whether one strange run occurred.
The fix ships. The uncertainty does not.
A regression test can pass while production behavior remains messy. Teams keep sampling runs because they need to know whether the incident pattern disappeared.
This is not a missing-dashboard problem. It is the cost of turning telemetry into a decision.
//Start with the incident
Keep the tools you already use.
Put the telemetry to work.
Start with a real failed or suspicious workflow and the traces or logs around it. No production routing change is required for the first review.
Bring the incident and its telemetry
OpenTelemetry, OpenInference, and exported telemetry can be used without changing production routing for the first review.
Reconstruct what actually happened
TalosRed correlates the workflow into a reviewable timeline across model calls, tools, state, and supporting events.
Separate evidence from assumption
Make clear what the telemetry shows, what the evidence suggests, and what still requires authoritative customer or business context.
Spend less time reconstructing incidents.
Spend more time fixing them.
Bring the traces and logs around an incident into one investigation. Keep the timeline, findings, evidence, open questions, and remediation state together.
Keep your observability stack.
Your telemetry platform should keep capturing the runs. TalosRed begins when someone opens those traces and asks what happened, who else was affected, and whether the fix held.
Review an incidentWhat the investigation contains
A customer reports that the AI did something wrong
Reconstruct the exact workflow, gather the evidence, and determine whether similar behavior appeared elsewhere.
Behavior changes after a prompt, model, or tool update
Compare against previous behavior and identify where the execution path began to differ.
Support or leadership needs an answer engineering can defend
Turn telemetry, findings, and remaining unknowns into one reviewable incident record.
You fixed the failure but need to know whether it is really gone
Watch actual production behavior after remediation instead of relying only on a regression test.
Bring one incident that is already
costing your team time.
We work with teams already running AI in production. Bring a recent failure, recurring investigation problem, or suspicious production change and the telemetry around it.
Best fit
- Multi-step production AI workflows
- Meaningful customer or business consequences when behavior is wrong
- Existing traces or logs
- Investigations that still require manual stitching
Low-friction start
- Exported or anonymized telemetry accepted
- No production routing change for the first review
Private design partner program
We will use the incident to test whether TalosRed can reduce the work required to reconstruct what happened, establish affected scope, assemble the evidence, and verify what changed after remediation.