FailureModes.ai
The failure-mode engine for agentic AI

Know where your agents will fail before your users do.

FailureModes.ai maps agent runs, reviews, and incidents to known failure patterns, then turns them into reusable mitigations.

See how it works →

Built by operators who have shipped AI, search, ads, and enterprise infrastructure at Microsoft and other enterprise platforms.

Meet the operators →

The reality

Agent failures repeat. Your team should not have to rediscover them.

Brittle tool use, retrieval drift, evaluator blind spots, unsafe autonomy, reviewer overload, and weak release gates show up again and again across production agent systems. FailureModes.ai turns those recurring failures into a reusable operating loop for detection, mitigation, and improvement.

FM-014

Tool argument hallucination

FM-027

Reviewer fatigue drift

FM-041

Retrieval staleness regression

FM-063

Cascading retry storm

FM-088

Planner / executor divergence

FM-102

Memory scope contamination

FM-117

Cost runaway loop

FM-131

Approval boundary bypass

The library

7,000+ more

Browse the library →

Each failure mode maps to detection signals, proposed fixes, reviewer actions, and hardening tests.

7,000+ patterns · detection signals · mitigation playbooks · growing

The architecture

A failure-mode library that compounds with every production run.

Every run, reviewer note, red-team finding, and incident can become structured failure intelligence. FailureModes.ai maps those signals to known failure patterns and reusable mitigations, so your team improves the system instead of rediscovering the same failures.

failuremodes.ai / control-loop-engine

  1. 01 · Pre-flight hardening

    Turn known failure modes into hardening tests, regression suites, and a prioritized fix backlog before launch.

  2. 02 · Production control loop

    Watch live traces against library signals and turn matches into reviewer-ready fixes.

    Detect · Propose · Approve · Gate

  3. 03 · Reviewer-approved fixes

    Approved fixes and confirmed new failures become reusable library entries — protecting future deploys and strengthening the engine for every customer.

  4. Failure-mode library

    7,000+ known failure patterns

    Detection signals · Mitigation playbooks · Hardening tests

    Compounding with every approved fix

Pre-flight → Production → Approved fixes → Library grows

An earlier visualization — the control loop view of the same library applied in production.

Trust & data posture

Bring your own model keys. We never need to see your model traffic — only the structured failure signals you choose to share.

Customer-specific data stays isolated. Generalized failure patterns improve the library.

See what customers get →

Deployment model

Stop spending your best engineers rebuilding the agent control loop.

Most teams eventually build ad hoc evals, trace reviews, reviewer queues, incident spreadsheets, and prompt regression checks. FailureModes gives teams three ways to operationalize the control loop depending on where your agent program is today.

Start with a vetted library of known agent failure patterns, mitigations, and eval recipes.

▸ Best for
Scoping a first initiative.
▸ Typical engagement
2–4 week diagnostic.
View details
▸ What you get
Failure Map, Readiness Scorecard, Use-Case Prioritization Matrix, Pilot-to-Production Roadmap.
Learn more →

Customer evidence

Production lessons become reusable mitigations.

FailureModes.ai turns real agent failures from production systems, reviews, red-team exercises, and remediation work into reusable patterns your team can detect, test, and mitigate.

Customer proof

FailureModes did not just surface issues. It helped us fix the loop. The system drafted targeted improvements from runtime evidence and human feedback, and our reviewers could approve or refine them instead of starting from scratch.
Head of AI Platform · Enterprise Technology Company
The biggest shift was moving from human review to human approval. FailureModes focused our team on the failures that mattered and turned reviewer feedback into concrete improvements across prompts, tools, policies, evals, and escalation.
VP Business Operations · Enterprise Services Company
FailureModes helped us avoid predictable failures before launch. The design recommendations gave us safer patterns for tool use, escalation, evaluation, permissions, and human feedback before the agent reached production.
Director of AI Transformation · Global Enterprise

Outcome · 01

Avoided predictable launch failures

The team launched with a stronger reliability baseline and avoided predictable design mistakes before users experienced them.

[X] design risks resolved before launch

View context
Situation
A team was preparing to launch an agent into a real enterprise workflow, but the design had unresolved risks around tools, permissions, escalation, and evaluation.
What changed
FailureModes identified likely failure modes before production and recommended safer design patterns, fallback paths, scoped access, and evaluation coverage.

Outcome · 02

Reduced reviewer overload

The team moved from noisy review queues to a low-noise human approval loop for improving the agent.

[X%] of suggested improvements approved with little or no modification

View context
Situation
A live agent had human review in place, but the process created noise and did not consistently translate feedback into system improvements.
What changed
FailureModes drafted targeted improvements from runtime evidence and HITL feedback, then routed recommendations to reviewers for approval or refinement.

Outcome · 03

Converted runtime failures into fixes

The customer gained an operating rhythm for continuous reliability improvement instead of reacting to one-off incidents.

[X] recurring failure patterns converted into improvement actions

View context
Situation
A production agent was creating recurring issues across retrieval, tool use, escalation, and policy-sensitive workflows.
What changed
FailureModes detected the recurring patterns, diagnosed root causes, drafted interventions, and helped route improvements into prompts, tools, policies, evals, and escalation rules.
View customer lessons

Field guide

Read the Failure-Mode Field Guide.

A public, technical reference for teams shipping production agents: known failure patterns, detection signals, and mitigation playbooks — drawn from real enterprise agent work and free to browse.

Read the field guide →

Customer outputs

What your team gets from a working engagement.

Concrete operating artifacts, not advisory slides.

  • A risk-ranked map of your agent failure modes
  • A reusable taxonomy for your workflows
  • Mitigation playbooks tied to real failures
  • Eval and red-team scenarios for known patterns
  • Release-gate recommendations for production agents
  • A control-loop workflow for turning future failures into fixes

Executive-ready diagnosis and readiness summary

A structured report that shows where the agent system stands, what is blocking reliability, and what path the team should take next.

What this shows
  • ▸Readiness score across workflow, tools, data, permissions, governance, evaluation, and operations
  • ▸Top failure modes and business risks
  • ▸Prioritized roadmap for remediation and production readiness

Why FailureModes.ai

Operators who have shipped AI at enterprise scale.

Meet the team →

  • Microsoft / Azure production experience

    Built and operated AI systems at Microsoft and on Azure at enterprise scale.

  • Frontier-model failure loop

    Experience fielding and analyzing failure modes from frontier-model-powered workloads, then translating them into improvements across product, platform, and model-facing teams.

  • Enterprise agent systems judgment

    Operator judgment on how agents actually break inside real enterprise environments — workflows, tools, permissions, governance, evaluation, escalation.

  • Closed-loop improvement

    Observe → Detect → Diagnose → Improve, with low-noise human approval, as a standing operating layer.

Closing

Know where your agents will fail before your users do.

See how FailureModes.ai maps your agent workflows to known failure patterns, mitigations, and control-loop improvements.

See how it works →