AI evaluation, reliability, analysts, and automation
Build AI systems that work outside the demo.
Agent Reliability helps teams evaluate agents, harden enterprise AI, build AI analyst systems, and automate business workflows with the tests, traces, controls, and evidence needed for production use.
Four service paths
Pick the route that matches where your AI work is stuck.
The common thread is practical reliability: understand the workflow, expose the failure modes, build the system, and give teams evidence that it is ready to use.
Agent Evaluation
Design realistic evals, adversarial tasks, regression suites, and release criteria for agent teams that need to know what actually fails.
Evaluate agents →Enterprise AI Reliability
Audit copilots, RAG systems, customer-facing agents, SQL/data agents, and workflow tools before they scale across the business.
Harden enterprise AI →AI Analyst Systems
Build AI analysts that investigate KPI changes, explain business performance, and surface evidence instead of just generating SQL.
Build AI analysts →Workflow Automation
Automate document-heavy and operations-heavy workflows across invoices, procurement, support, approvals, compliance, and shared services.
Automate workflows →Why this matters
The expensive AI failures are usually quiet, plausible, and operational.
They happen when a system looks useful in a controlled demo but breaks against messy data, ambiguous requests, missing context, edge cases, tool errors, or real business constraints.
Agents pass demos but fail real tasks
Copilots retrieve the wrong context
RAG systems give plausible but unsupported answers
SQL/data agents make quiet analytical mistakes
Dashboards show what changed but not why
Manual document workflows still burn hours
Workflow agents call tools without enough control
Teams lack clear go-live and regression criteria
How we work
Workflow-first AI engineering, with reliability built in.
Start from the workflow
We map the real business task, user intent, data sources, handoffs, escalation paths, and failure cost before designing the AI layer.
Measure what matters
We define tests, traces, evals, metrics, and release criteria around actual business outcomes, not generic model scores.
Engineer reliability in
We use retrieval improvements, validation checks, tool constraints, human-in-the-loop controls, and monitoring so systems fail safely.
Ship useful systems
The goal is not a clever demo. The goal is dependable AI that saves time, explains decisions, and can be trusted in production.
About the founder
Built by someone who has worked across production AI, evals, analytics, and automation.
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, evaluation environments, and document-processing automation.
Discuss a project- Production AI and analytics systems across enterprise environments
- Hands-on work creating and hardening agent evaluation tasks and environments
- Experience with RAG, LLM workflows, SQL/data agents, and retrieval quality
- AI analyst systems that investigate KPI changes and explain business performance
- Document processing and workflow automation for operational processes including invoice handling
Find the right AI service
What are you trying to make reliable, useful, or automated?
Tell us whether you need agent evals, enterprise reliability, an AI analyst system, workflow automation, or help choosing the right route.
Prefer email? drew@agent-reliability.com