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README.md

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-title: README
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+title: Agent Reliability Engineering
+emoji: 🛡️
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+short_description: Reliability engineering for AI agents
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-Edit this `README.md` markdown file to author your organization card.
+# 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.