Stop guessing why your
AI agents are failing.
Production AI agents fail across complex chains of LLMs, tools, APIs, and retrieval systems. SignalsForge AI connects the evidence — traces, logs, and tool call outputs — so your team gets a clear diagnosis, not a debugging marathon.
The problem we solve
AI agent failures are hard to diagnose. We make them not be.
When autonomous agents fail in production, finding the real cause requires sifting through sprawling logs and non-deterministic behavior. We bring clarity to the chaos.
Evidence is everywhere — and connected nowhere
Failures leave signals across traces, logs, tool calls, API responses, and conversation history. We pull it all into one place so engineers aren't hunting across five dashboards.
Investigations eat engineering hours
Root-causing a single agent failure can take hours of manual log-reading and additional LLM calls. We cut that down by surfacing the most relevant evidence and a reasoned diagnosis directly.
The same failure comes back
Incidents that aren't captured as tests come back — often in a slightly different form. We help teams turn confirmed failures into regression tests before the next deploy ships.
How it works
From failure signal to root cause — in one workflow.
SignalsForge AI handles the investigation so your engineers can focus on the fix. Here’s how we go from a production failure to a resolved, tested outcome.
We gather the evidence
SignalsForge pulls in traces, logs, tool results, and expected outcomes — everything relevant to the failure — so engineers don't have to hunt across systems manually.
We diagnose the root cause
We identify which step failed, why it failed, and what the supporting evidence says — giving engineers a clear, reasoned answer instead of a list of raw logs to interpret.
We turn the fix into a test
Once a failure is confirmed, we help convert it into a regression test — so the same issue can't silently ship again in a future release.
About SignalsForge AI
Engineering reliable AI agents for teams operating in production.
SignalsForge AI focuses on one critical problem: making production AI agent failures faster to diagnose and impossible to repeat silently.
As autonomous agents handle more customer-facing workflows, unexpected tool behaviors, retrieval drift, and multi-step reasoning breakdowns create severe operational overhead. We work directly with engineering teams to turn chaotic debugging sessions into structured, reproducible root cause intelligence.
Root Cause Over Raw Logs
We surface the exact tool call or context failure, eliminating hours of manual log inspection.
Deterministic Evidence
Traces, tool parameters, and schema responses are correlated into a single coherent timeline.
Continuous Protection
Every resolved incident is converted into an automated regression test before future releases ship.
Get in touch
Running AI agents in production? Let’s talk.
If your team is dealing with agent failures that are hard to diagnose, slow to investigate, or keep coming back — we’d like to understand your setup and show you how SignalsForge AI can help.