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63% of AI agent tasks fail silently

Upload your logs. Get a diagnosis.
Know exactly what broke.

Agent Failure Forensics is a micro-SaaS for AI operators. Drop in your sanitised logs, get a structured failure report with evidence chains, replay fixtures, and a regression checklist. No credentials. No production access. No guessing.

Get early access → See sample report ↓
63%
of complex AI agent tasks fail silently
<5 min
time to upload and trigger analysis
3
artefacts per report — report, fixture, checklist
0
credentials required
How it works

Three steps from "something's wrong" to "here's exactly what to fix"

No onboarding calls. No configuration. No guesswork about where to start.

1

Upload sanitised logs

Drag and drop your agent logs, API traces, or cron output. Sanitise first — we don't need credentials, just the execution record.

→
2

Get a structured report

Within minutes, receive a diagnosis report with severity-ranked findings, each traced back to specific log entries — not vibes or hunches.

→
3

Fix with confidence

Use the included replay fixture to reproduce the failure in CI, and the regression checklist to confirm the fix before you ship.

Sample output

This is what your report looks like

Every finding is traceable to a specific line in your logs. You verify; you don't trust.

agent-failure-forensics-report-2026-05-08.html 3 findings · 1 fixture · 1 checklist
CASCADING HALLUCINATION — downstream data corruption Critical
An LLM-generated tool parameter was accepted without schema validation. Downstream tool call succeeded silently with wrong input, producing corrupted vector-store entries that propagated to the next three agent tasks.
Evidence chain
[09:14:02] LLM output → {"tool": "upsert_vector", "param": {"id": "usr_0091", "score": "0.91", ...}} [09:14:02] Schema validation: PASSED (param.id is string, LLM returned string — type match only, semantic mismatch) [09:14:03] Tool call: upsert_vector → SUCCESS [09:14:08] Downstream read: returned score=0.91 instead of expected 0.73 → Next 3 agent tasks built on wrong confidence score.
SILENT TOOL CALL FAILURE — no exception thrown High
Tool call returned HTTP 429 (rate limit) but the agent scaffold swallowed the error and retried with identical parameters, burning 4× the expected token budget before moving on without the intended result.
Evidence chain
[11:02:11] POST /api/embed → HTTP 429, retry=1 [11:02:13] POST /api/embed → HTTP 429, retry=2 [11:02:15] POST /api/embed → HTTP 429, retry=3 [11:02:17] Agent continued without embedding — no exception raised, no user notification. Token waste: ~$0.38 at current rate × 3 retries × 4 similar incidents today = ~$4.56/day.
ORCHESTRATION LOOP — unbounded re-plan cycle Medium
The agent entered a 7-step re-plan loop triggered by a low-confidence classification. No guardrail was in place to break the cycle after N failed attempts, resulting in 38 identical LLM calls and $1.12 in token waste.
Evidence chain
[14:33:01] Classification confidence: 0.31 (below 0.40 threshold) [14:33:04] Re-plan triggered → confidence: 0.29 [14:33:08] Re-plan triggered → confidence: 0.33 [14:33:11] ... (4 more iterations, confidence: 0.28, 0.31, 0.30, 0.29) [14:33:28] Loop broken by external timeout after 27 seconds. Fix: Add max_replan_attempts=2 guardrail; fallback to human escalation.
Want your own report? Upload your logs and get a diagnosis within minutes of launch. Get early access →
Pricing

Early access — locked in for life

Join before launch. Your rate never goes up. Cancel anytime.

Monthly
$29
per month
  • Unlimited log uploads
  • Full diagnosis report per upload
  • Replay fixture download
  • Regression checklist
  • Error-budget metric
  • Email support
  • Annual billing saves $99/yr
Start monthly
🔒 Early access rate
Best value
Annual
$249
per year — saves $99
  • Everything in Monthly
  • Locked at $20.75/month
  • Priority processing queue
  • Direct Slack access to Milo
  • Feature requests prioritised
  • 12 months, cancel anytime
  • No refunds on annual plans
Get annual access
🔒 Early access rate — lock it in

Early access begins when the product launches. You'll be notified by email and given first access before the waitlist opens to the public.

🔒

No credentials needed

You sanitise your logs before uploading. No API keys, no production access, no PII in our system.

⚡

Minutes, not days

Structured diagnosis within minutes of upload. Full report, fixture, and checklist ready to act on.

📋

You verify, not trust

Every finding is traceable to a specific log entry. Dispute, extend, or confirm — your call.

FAQ

Questions, answered

What does the diagnosis report actually contain?
A structured report with: severity-ranked failure modes, traceable evidence chains linking each finding to your raw log entries, a replay fixture (deterministic test case), a regression checklist, and an error-budget metric. Everything is scoped to the logs you upload.
What counts as "AI agent logs"?
Any text output from an autonomous AI operator setup: LLM API call logs, tool-use traces, cron output, agent scaffold logs, or exported conversation histories. If it records what your agent did, it can be diagnosed.
Is my data handled securely?
Yes. You sanitise your own logs before uploading. No credentials, no production systems, no PII are required. Logs are processed and discarded after your report is delivered.
How does early access pricing work?
Early access is $29/month or $249/year, locked in for life. After the full launch, the price increases. Join the waitlist now to secure your rate.
What if no failures are found?
If the diagnostic surfaces zero actionable findings, you'll receive a clean bill of health with recommendations for ongoing monitoring. You still get the full report.
Can I cancel early access at any time?
Yes. Cancel monthly anytime. Annual plans are non-refundable but remain active for 12 months from signup.
Early access

Stop guessing why your agent failed.
Start with the report.

Join the waitlist. Early access is $29/month or $249/year, locked in for life at signup. No spam. No card charged until launch.

Request early access →

Or email miloantaeus@gmail.com directly. I reply to every message.