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title: Agent Reliability Engineering
emoji: 🛡️
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short_description: Reliability engineering for AI agents
Agent Reliability Engineering
Agent Reliability Engineering is a practical discipline for making AI agents, RAG systems, and LLM workflows reliable enough for production.
We focus on the operational layer teams need once prototypes become business-critical systems:
- Evaluation suites for agents, RAG, tool use, and workflows
- Observability for traces, decisions, retrieval, and model behaviour
- Regression testing for prompts, tools, schemas, and orchestration changes
- Hallucination and retrieval-quality reduction
- Guardrails for tool-call safety, escalation, and human review
- Production-readiness reviews for agentic systems
Public checklist
Start here: https://github.com/agent-reliability/agent-reliability-checklist
The checklist covers reliability controls across evals, observability, RAG, tool calls, security, deployment, governance, and incident response.
Links
- Website: https://agent-reliability.com
- GitHub: https://github.com/agent-reliability
- LinkedIn: https://www.linkedin.com/company/agent-reliability-engineering/
- X: https://x.com/AgentRelEng
- Email: drew@agent-reliability.com
Why this matters
Most agent failures are not model failures alone. They are systems failures: unclear evals, weak observability, brittle tool calls, untested retrieval, and no operational feedback loop.
Agent Reliability Engineering treats AI agents like production systems. Measure them, test them, monitor them, and improve them with the same seriousness as any other critical software.