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| title: Agent Reliability Engineering | |
| emoji: 🛡️ | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: static | |
| pinned: false | |
| 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. |