Forensic observability for AI agents
Your agent finished.
But did it actually work?
ARGUS catches silent failures and traces root cause through your graph before you deploy, so broken pipelines never reach production.
enrich_account reported pass while omitting csat_history. Its stripe_api call was rate-limited and the handler swallowed the error, so the crash surfaced three nodes later in policy_check.
Root cause · confidence 0.91 · enrich_account → draft_reply → policy_check
Execution graph · 7 nodes
Duration3.12sSteps4 / 7Tokens3,842Cost$0.041Tool calls9
The internet has been complaining about this for years.
We're just building the fix.
“our agent hallucinated product features for 3 days straight. responses looked perfectly formatted so nobody flagged it. we only found out when a customer asked about a feature that doesn't exist.”
“the planner agent was outputting YAML but the executor expected JSON. pipeline showed green. outputs were garbage for a week. logs said 200 OK the entire time.”
“a tool API changed its response format quietly. our agent started retrying in a loop. $15k in extra LLM costs before anyone caught it. no alert, no error, just a slowly growing bill.”
“42% of teams had production incidents from hallucinations within 3 months of launch. if you're shipping agents without semantic validation, you're just waiting for your turn.”
“our sales agent started offering unauthorized 50% discounts. worked perfectly in demos. connected to real customer data? just started making things up. cost us 3 enterprise deals.”
“multi-agent telephone game is real. step 2 misinterprets step 1, step 3 builds on the wrong interpretation, final output is logically corrupted but looks completely fine to reviewers.”
“our agent hallucinated product features for 3 days straight. responses looked perfectly formatted so nobody flagged it. we only found out when a customer asked about a feature that doesn't exist.”
“the planner agent was outputting YAML but the executor expected JSON. pipeline showed green. outputs were garbage for a week. logs said 200 OK the entire time.”
“a tool API changed its response format quietly. our agent started retrying in a loop. $15k in extra LLM costs before anyone caught it. no alert, no error, just a slowly growing bill.”
“42% of teams had production incidents from hallucinations within 3 months of launch. if you're shipping agents without semantic validation, you're just waiting for your turn.”
“our sales agent started offering unauthorized 50% discounts. worked perfectly in demos. connected to real customer data? just started making things up. cost us 3 enterprise deals.”
“multi-agent telephone game is real. step 2 misinterprets step 1, step 3 builds on the wrong interpretation, final output is logically corrupted but looks completely fine to reviewers.”
“an agent 'completed' a maintenance task by deleting 1,200 records and fabricating 4,000 fake ones to cover it. every dashboard showed green. we found out 3 weeks later from a customer report.”
“merge queue bug silently reverted commits across 600+ repos. the UI showed green checkmarks the entire time. devs had no idea their code was reverted until someone dug into the actual diffs.”
“only 14% of enterprises with AI agent pilots have reached production scale. the gap isn't model quality — it's that nobody can tell when their agents silently degrade. observability is the actual bottleneck.”
“our agent drifted over 2 months. slowly gave worse outputs. nobody noticed because there were no errors — just gradually degrading quality. customers churned before we connected the dots.”
“POV: you analyze 12M agent logs and discover 78% of issues were silent regressions and hallucinations — not timeouts or errors. traditional APM catches literally none of this.”
“our agent posted 47 near-identical messages to a public slack channel when an API call failed. no circuit breaker, no alert, no observability. just 47 messages and a very confused sales team.”
“an agent 'completed' a maintenance task by deleting 1,200 records and fabricating 4,000 fake ones to cover it. every dashboard showed green. we found out 3 weeks later from a customer report.”
“merge queue bug silently reverted commits across 600+ repos. the UI showed green checkmarks the entire time. devs had no idea their code was reverted until someone dug into the actual diffs.”
“only 14% of enterprises with AI agent pilots have reached production scale. the gap isn't model quality — it's that nobody can tell when their agents silently degrade. observability is the actual bottleneck.”
“our agent drifted over 2 months. slowly gave worse outputs. nobody noticed because there were no errors — just gradually degrading quality. customers churned before we connected the dots.”
“POV: you analyze 12M agent logs and discover 78% of issues were silent regressions and hallucinations — not timeouts or errors. traditional APM catches literally none of this.”
“our agent posted 47 near-identical messages to a public slack channel when an API call failed. no circuit breaker, no alert, no observability. just 47 messages and a very confused sales team.”
ARGUS detects01 silent failures in your agent pipeline, explains02 where the state actually broke, and fixes03 the node that actually failed.
Placeholder summary returned. Pipeline marked the node succeeded.
01
Detects silent failures
Heuristics, anomaly scoring and a semantic judge run on every node output — so a step that returns a placeholder instead of a summary gets flagged, not marked green.
{"ticket_id": "T-4419","customer": "Northwind","plan": "enterprise","body": null}
First bad state. ticket.body dropped four hops before validate failed.
02
Explains the root cause
ARGUS walks the graph backwards to the node that actually introduced bad state, and shows the state diff that proves it.
Root cause: enrich_account
csat_history was None. Upstream returned null instead of an object.
03
Hands you the fix
argus fix writes a paste-ready prompt for the node that dropped the field — exact source line, no hunting through traces.
01
Catch failures before they ship
A node can return valid JSON, exit cleanly and still be wrong. ARGUS scores every output for emptiness, placeholder text, collapsed confidence and hallucinated entities, then fails the run instead of marking it green.
Multi-layer detection
- Heuristic signatures
- Anomaly detection
- Semantic judge
- Contract validators
- Regression baselines
- CI gating
02
Know which node actually broke
The node that raises is rarely the node that failed. ARGUS walks execution lineage backwards to the first step that introduced bad state, and shows the field-level diff that caused everything downstream.
Forensic root cause
- Graph walkback
- State diffs
- Tool-call forensics
- Downstream impact
- Confidence tracking
- Multi-agent lineage
From a design partner
“The crash annotation is the best thing in the project. Exact source line plus the upstream null beats most observability tooling I’ve used.”
Your traces stay yours
Runs in your environment. Nothing ships to us by default.
Redaction before persistence
Secrets and PII are stripped before any snapshot is written.
Never trained on your code
Your pipelines and prompts are never used to train models.
Changelog
The latest
argus init
Writes Cursor and Claude skills that attach ArgusWatcher and debug from .argus/runs. Commit them and ask your editor to wire ARGUS.
argus fix
Paste-ready prompt for the root-cause node — exact source line and why it failed.
Strict mode for CI
Fail a build when any detector fires above a configured severity.
Questions,
answered.
A node technically “succeeds” but returns degraded state — empty arrays, placeholder text, collapsed confidence scores, hallucinated tool outputs — that quietly poisons every downstream step.