Your agent finished.
But did it actually work?
ARGUS catches silent failures and traces root causes before you deploy — so broken pipelines never reach production.
Runs
| RUN | STATUS | STEPS | DURATION | TOKENS | |
|---|---|---|---|---|---|
parse_diff → detect_risk → review_code → ch... 20260613-061921-080f8dparse_diff | Silent Failure | 5/5 | 25.09s 4d ago | — | |
router → weather → general 20260617 | Clean | 3/3 | 2.23s 7h ago | — | |
clean → meta 20260617 | Clean | 2/2 | 3.43s 7h ago | — | |
process 20260617 | Clean | 1/1 | 1.68s 7h ago | — | |
respond → summarize 20260617 | Clean | 2/2 | 2.84s 7h ago | — | |
answer 20260617 | Clean | 1/1 | 1.33s 7h ago | — | |
step_1 → step_2 → step_3 → step_4 → step_5... 20260617 | Clean | 7/7 | 11.51s 7h ago | — | |
scan → report 20260617 | Clean | 2/2 | 2.96s 7h ago | — | |
compute 20260617 | Clean | 1/1 | 1.33s 7h ago | — |
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.”
Every node, traced.
Every silence, surfaced.
ARGUS watches every step of your agent pipeline during development and testing,
catching silent failures before they ever reach production.
Data extracted successfully
Entities enriched and structured
Silent failure:
placeholder returned
ARGUS detected this failure before it degraded downstream nodes
ARGUS detected this before it degraded downstream
Degradation detected in downstream step
Output at risk
Let your AI set up Argus
Copy this prompt, paste it into Claude or ChatGPT, and it will audit your pipeline and wire up ARGUS automatically.
See the truth.
Re-run from any step. ARGUS reuses everything that's already correct — so you only pay for what changed.
Built for
engineers
who ship.
They trace. We detect.
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.