Production readiness for agentic AI
Find hidden AI agent failures before they reach production.
Agent Reliability helps teams test, monitor, and harden AI agents, copilots, RAG systems, SQL/data agents, and autonomous workflows before they affect users, customers, or operations.
Four service paths
Four ways we help teams ship dependable AI systems.
Enterprise AI Reliability
Production-readiness audits for internal copilots, customer-facing agents, RAG systems, and business workflows.
For enterprise teams →Agent Evaluation & Benchmarking
Realistic task environments, adversarial scenarios, and benchmark design for teams measuring agent capabilities.
For AI labs and agent builders →AI Analyst Systems
AI analysts that investigate business performance, explain KPI changes, and surface likely drivers from enterprise data.
For analytics and finance teams →AI Workflow Automation
Automate repetitive business processes involving documents, approvals, emails, support requests, and operational workflows.
Explore workflow automation →The problem
AI demos are easy. Dependable production agents are hard.
The hardest failures are not obvious hallucinations. They are plausible answers that look right but quietly fail under real workflows.
Silent incorrect answers
Unsupported business recommendations
Wrong tool or API calls
Poor retrieval and missing context
Permission or data-access failures
Weak escalation paths
Prompt/model regressions
No clear go-live criteria
Reliability engineering
Find the failure modes, measure them, and engineer them down.
Evaluation
Design evals, regression tests, and release criteria so teams know whether an agent is improving or getting worse.
Observability
Trace prompts, retrieval, tool calls, outputs, errors, feedback, and escalation paths so failures can be understood and fixed.
Hallucination and grounding
Reduce unsupported answers through retrieval improvements, validation checks, constraints, and eval coverage.
Retrieval quality
Improve RAG and enterprise search so agents retrieve the right context, entities, documents, and business concepts.
Tool safety
Make tool use dependable with validation, permissions, confirmation steps, exception handling, and safe failure modes.
Production readiness
Prepare agents for launch with monitoring, incident playbooks, ownership models, human-in-the-loop controls, and reliability metrics.
About the founder
Built by someone who has worked on production AI and agent evaluation.
Agent Reliability is led by Drew Clayman, a Lead Data Scientist and AI engineer with experience building production AI systems, enterprise analytics workflows, RAG systems, agentic tools, and evaluation environments for autonomous agents.
Contact Drew- Production AI and analytics systems across enterprise environments
- Experience with RAG, LLM workflows, data agents, and evaluation design
- Hands-on work creating and hardening agent evaluation tasks and environments
- Prior work on document processing and workflow automation systems in operational domains including invoice handling and enterprise analytics workflows
- Background in forecasting, enterprise BI, and AI system reliability
Book a reliability review
Find the failures your demo set will miss.
Tell us what you are building, which workflows matter, and where failure would create customer, operational, financial, or trust risk.
Prefer email? drew@agent-reliability.com