AI Agent Debugging for Production Teams
Developers, ML engineers, agentic AI founders
Agents fail through slow spans, bad tool calls, retry loops, hallucinated actions, failed handoffs, and hidden quality regressions. Traditional logs show fragments, but not the full run or the business impact.
Debug from execution path to owner
AnoSys traces every production run, correlates model behavior with tools, infrastructure, evals, cost, and user context, then narrows the failure to the span, tool, model, release, or dependency most likely to explain the outcome.
Trace the run, isolate the failure, and hand engineering the evidence needed to fix it.
Trace
Capture prompts, completions, model calls, tool calls, retries, and handoffs.
Correlate
Join spans with eval scores, token cost, latency, releases, and user impact.
Explain
Surface the span, model, tool, or dependency that changed the outcome.
Fix
Route incidents with evidence so engineering can resolve failures quickly.
What supports it
Why teams buy it
- Reduce time spent reconstructing failed runs from scattered logs.
- Catch regressions and loops before they become customer-visible incidents.
- Give founders and engineering leaders a clear reliability story for production AI.
Can AnoSys debug multi-agent workflows?
Yes. AnoSys traces agent turns, handoffs, model calls, tool calls, retries, and custom spans so teams can inspect the full execution path.
Does this replace application logs?
No. AnoSys connects logs with traces, evals, cost, user context, and business impact so logs become part of a broader operational timeline.