Community
Contribute what failed. Unlock how others fixed it.
Submit a non-confidential AI failure mode to unlock operator-grade detection signals, mitigation patterns, and architecture notes.A vetted exchange for production AI failures. Submit a real, non-confidential failure mode from an LLM, agent, RAG system, or multi-agent workflow to unlock operator-grade detection signals, mitigation patterns, eval designs, and architecture notes from the Mitigation Vault.
Founding cohort now open. Help map the first 100 real-world AI failure modes.
Built for sanitized contribution: no customer data, private logs, credentials, or proprietary prompts.
What this is
A vetted exchange for production AI failures.
FailureModes.ai is not a forum or a feed. It is a curated public map of how enterprise AI systems break in production, paired with a private Mitigation Vault of how teams fixed them.
Every submission is reviewed by our editorial team. Sanitization and attribution decisions stay with the contributor. We will never publish anything you did not approve.
The starting point
The diagnostic sandbox for production AI.
We built the FailureModes platform to help teams detect, contain, and improve agentic failures at enterprise scale. But not every team is ready for a full deployment or paid diagnostic on day one.
The Community and Mitigation Vault give operators a way to start now. Bring a real, non-confidential failure mode from an LLM, agent, RAG system, or multi-agent workflow. We help structure it through the FailureModes diagnostic lens, and accepted contributors unlock the Vault: operator-grade detection signals, eval designs, monitoring checks, architecture notes, and mitigation patterns.
Trade your hardest failure mode for a clearer path to fixing it.
What you unlock
Public map. Private mitigation vault.
Public Failure Modes
The map
What failed, where it appeared, impact, symptoms, and high-level lessons. Open to everyone — the goal is to make production AI failure patterns common knowledge.
◆ Mitigation Vault
The fix
Detection signals, mitigation patterns, eval design, monitoring strategy, architecture notes, operator lessons. Reserved for accepted contributors.
◆ Mitigation Vault · Locked
Sample · not real entry
Failure mode: Planner/executor recursion in tool-using agents
- Detection
- Repeated tool calls without state progress
- Mitigation
- Bounding planner/executor recursion
- Monitoring
- Trace-level state divergence checks
The full Vault includes detection signals, mitigation patterns, eval design, and operator lessons for every accepted failure mode.
Unlock the Vault →Why contribute
Three reasons serious AI builders join.
Stop debugging in isolation
See how other operators handle context degradation, tool-call loops, retrieval failures, and state drift.
Validate your architecture
Compare your agent framework against real production failure patterns, not synthetic demos.
Unlock mitigation patterns
Accepted contributors access non-public detection signals, eval designs, monitoring checks, and architecture notes.
First 100
The Founding Cohort is open.
We are admitting the first 100 contributors to the Mitigation Vault. Founding contributors get permanent Vault access, public attribution on accepted entries, and a direct line to the editorial team.
Founding cohort now open
The ladder
How contribution works.
01
Submit
Share a link or short description.
02
Editorial Review
We sanitize and structure the failure mode.
03
Accepted Contributor
Your contribution enters the public map.
04
Vault Member
You unlock detection and mitigation patterns.
05
Verified Contributor
You earn public or private contributor recognition.
06
Domain Steward
You help shape a failure-mode category.
Enterprise constraints
Built for sanitized contribution.
No raw logs, no customer names, no internal screenshots in the public map unless the contributor explicitly approves. Editorial works with you to scrub identifiers and confirm framing before publication.
Vault entries cite their source and the verification path the editorial team followed. Speculative claims do not ship.
Contribution standards
We protect contributors, customers, and employers.
- Customer-identifying detail without explicit permission.
- Vendor takedowns dressed up as failure analysis.
- Speculative incidents the contributor cannot stand behind.
- Marketing copy or launch announcements.
- Anything that exposes secrets, credentials, or PII.
High-value patterns
High-value patterns we are actively mapping.
Multi-Agent State Drift
State corruption, role confusion, stale memory, cross-agent inconsistency.
Agent Tool-Use Failures
Wrong tool selection, invalid arguments, ignored tool outputs, unsafe tool execution.
RAG Retrieval Failures
Stale documents, wrong source authority, missing policy context, contradictory retrieval.
Model Upgrade Regressions
Behavior shifts after model, prompt, routing, or policy changes.
Prompt Injection in Agent Workflows
Injected instructions via retrieved content, tool outputs, or user input bypassing guardrails or hijacking tool use.
Cost Runaway in Autonomous Agents
Recursive planning, tool-call loops, and unbounded retries producing unexpected spend.
Schema Violations in Production Workflows
Structured-output drift, invalid JSON, missing required fields, downstream pipeline breakage.
New or Uncategorized Failures
If it broke in production and does not fit the taxonomy, submit it anyway. Edge cases are often the most valuable.
Recognition
Selected high-quality submissions may receive private architecture review, featured technical writeups, and steward consideration.
Submit a failure. Unlock the Vault.
Start with a link, a short note, or a sanitized description. Our editorial team will help structure it.
Already wrote about it? Paste a GitHub issue, blog post, or thread — we'll structure it into the taxonomy for you.