FAQ
Questions we get from enterprise teams.
Straight answers on why agent systems break, how we engage, and what you actually get.
Why do enterprise agentic AI systems fail in production?
Most failures aren't model failures. They come from broken workflows, unmaintained tool integrations, weak evaluation, missing guardrails, unclear human handoffs, and silent quality degradation that nothing is watching for. The model is rarely the bottleneck.
What is an agent failure mode?
A failure mode is a specific, recurring way an agent system breaks under real conditions — tool misuse, hallucinated tool calls, stuck loops, wrong escalation, retrieval drift, permission leaks, or silent output degradation. We catalog these per system and design controls against them.
How is FailureModes.ai different from an observability or evals vendor?
Observability tools show you traces. Eval frameworks score outputs. We do the engineering work: diagnose root causes across architecture, orchestration, tools, governance, and human handoffs, then redesign the system for reliable execution and run a continuous critic loop on it in production.
When should we engage — before, during, or after launch?
All three. Assess (2–4 weeks) before you commit, Recover (6–12 weeks) when an initiative is stalled or fragile, and Improve as an ongoing operating layer once agents are live. Most enterprises engage us at Recover, then continue with Improve.
Do you work with a specific agent framework or LLM provider?
We are platform-agnostic. We work across LangGraph, CrewAI, custom orchestration, OpenAI, Anthropic, Google, Bedrock, Azure, and in-house stacks. The failure modes are mostly the same; the fixes are stack-specific.
What deliverables do we actually get?
Concrete artifacts, not slides. Assess produces a Failure Map, Readiness Scorecard, Use-Case Prioritization Matrix, and Pilot-to-Production Roadmap. Recover produces a Root Cause Review, Failure Cascade Analysis, Recovery Plan, and Evaluation Harness. Improve runs an ongoing Runtime Critic Dashboard with weekly optimization reviews.
How do you measure success?
Production reliability metrics: task completion rate, escalation accuracy, tool-call success, output quality drift, time-to-detect a regression. We agree on the metrics in the first two weeks and report against them.
Is our data safe?
Yes. We work inside your environment, under your data controls, and never train on customer data. NDAs and security reviews are standard at engagement start.
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