AI Agent Reliability Audit
We review your AI agents with structured trace analysis, classify the failure patterns we observe, and deliver a prioritized roadmap to fix them.
Your AI agents are failing in ways you cannot see or diagnose
- Agents produce confident answers that are not grounded in the right source material
- Failures are silent, with no alerts, traces, or clean reproduction path
- Debugging takes days because there is no structured approach to classify failure types
- Tool calls fail intermittently with no retry logic or graceful degradation
- Context windows overflow silently, truncating critical information without warning
How we solve this
Our audit captures execution traces, groups failures into clear categories, and delivers specific fixes ranked by business impact.
Full Trace Capture
Every agent run recorded end-to-end: inputs, retrieval, tool calls, LLM reasoning, and outputs
14-Type Failure Classification
Each failure categorized (hallucination, tool misuse, context overflow, stale data, etc.) with root cause analysis
Reliability Scorecard
Quantified reliability metrics per agent: success rate, failure distribution, latency, and cost
Prioritized Fix Roadmap
Specific fixes ranked by business impact and implementation effort, with estimated timelines
Monitoring Setup
Alerting configured so you are notified immediately when reliability degrades
How it works
Engineer Outreach
A senior engineer reviews your setup and confirms the audit scope
Instrumentation
We add trace capture to your agents - typically 1-2 days, zero downtime
Analysis Period
A defined observation window where production traffic is captured and reviewed
Report & Strategy Call
Detailed findings report + 60-minute walkthrough with fix recommendations
Frequently asked questions
What are the 14 failure types?
Retrieval Failure, Stale Context, Hallucination, Unsupported Claim, Tool Misuse, Tool Failure, Missing Approval, Policy Violation, Prompt Injection, Context Overflow, Reasoning Error, Output Format Error, Cost Anomaly, and Latency Anomaly. Each has a specific diagnostic approach and fix pattern.
Do we need to give you access to our production systems?
We need read access to agent logs/traces and the ability to add lightweight instrumentation. We never need write access to your databases or production data. We work under NDA and follow your security policies.
How is this different from running evals ourselves?
Evals test known scenarios. Our audit captures real production traffic and finds failure modes you did not know existed. We also classify failures into actionable categories - instead of "the agent was wrong," you get "this was a retrieval failure caused by stale index data, fixable by implementing incremental indexing."
What if our agents are custom-built (not using a standard framework)?
No problem. Our instrumentation works at the LLM call level. If your agent makes API calls to an LLM provider, we can trace it - regardless of whether it is built with LangChain, a custom orchestrator, or raw API calls.
Ready to get started?
Book a consultation with a senior AI engineer. We will use the conversation to understand your requirements and suggest next steps.
Request a Reliability Audit