# Agentic AI for Production Support: Moving from Alerts to Intelligent Incident Resolution > Source: > Published: 2026-08-16 07:37:27+00:00 Production support today is still highly dependent on engineers. An alert comes in → engineer checks logs → searches previous incidents → identifies possible RCA → validates the solution → takes action. While working on this problem, I started exploring a simple question: **Can Agentic AI reduce this dependency without giving AI uncontrolled access to production?** This is the idea behind my **Agentic Production Support** project. The basic flow I am building is: **Incident → Clean Layer → RAG → AI Agent → RCA → Validation → Human Approval → Action** Instead of sending every incident directly to an LLM, the system first prepares and controls the context. **RAG (Retrieval-Augmented Generation)** searches historical incidents and knowledge to provide relevant context. The AI agent then uses this context to generate a possible **Root Cause Analysis (RCA)** and recommended action. But there is an important challenge here. Production logs can contain: The LLM doesn't need most of this information to understand that a service failed because a disk became full. So I introduced a **Model Egress Governance Layer**. Production Incident ↓ Clean / Sanitization Layer ↓ RAG + Embeddings ↓ AI Agent ↓ RCA + Validation ↓ Human-in-the-Loop ↓ Action The principle is simple: **Give AI the technical context it needs, but not the identity of your production environment.** The same control should apply before **LLM calls, embeddings, RAG indexing, retrieval queries and AI observability**. For high-risk information such as passwords, API keys or authorization tokens, my approach is **fail closed**: **Sensitive information still detected → Block the model call.** I don't see Agentic AI replacing production engineers. I see it reducing the repetitive work around: **Incident Detection → Correlation → Knowledge Retrieval → RCA → Validation → Recommended Action** while keeping governance and **Human-in-the-Loop (HITL)** controls around high-impact actions. This project is still evolving, and I am building it openly so that the architecture can be tested, challenged and improved. The implementation includes **Python, LLM integration, RAG, embeddings, AI agents, incident management, RCA and AI governance**. 👉 **GitHub:** [https://github.com/devhazare/agentic-production-support](https://github.com/devhazare/agentic-production-support) If you are working on **AIOps, Agentic AI, SRE automation, LLMOps, RAG or AI Governance**, I would be interested in your feedback and contributions.