How Agentic RAG Works? A traditional RAG has a simple retrieval, limited adaptability, and relies on static knowledge, making it less flexible for dynamic and real-time information. Agentic RAG improves on this by introducing AI agents that can make decisions, select tools, and even refine queries for more accurate and flexible responses. Here’s how Agentic RAG works on a high level: 1. The user query is directed to an AI Agent for processing. 2. The agent uses short-term and long-term memory to track query context. It also formulates a retrieval strategy and selects appropriate tools for the job. 3. The data fetching process can use tools such as vector search, multiple agents, and MCP servers to gather relevant data from the knowledge base. 4. The agent then combines retrieved data with a query and system prompt. It passes this data to the LLM. 5. LLM processes the optimized input to answer the user’s query. Credit: bytebytego #AgenticRAG #Agentic #RAG #systemdesign #coding #interviewtips

@Python_Dv The real contrast isn't static retrieval vs. adaptive. It's whether the retrieval layer can decide to stop, re-plan, and try a different tool. Agentic RAG is just RAG with a planner in front of it. The name is new, the accuracy gain is from the planning, not the "agent" label.

@Python_Dv Memory layer is the real gap — most still do routing, I’m building **Concurrent Neural Graph** with context as dynamic spotlight + human pruning. How do you evolve memory in your setups? Dynamic weights? Thread 👇 x.com/alkaidy2025/st…

@Python_Dv Agentic RAG agents hitting MCP servers for data fetching? Before calling, hit ToolRate's toolrate_assess (MCP-native, launching Apr 20) for reliability_score, pitfalls like GDPR risks on those servers, and top alternatives. Then toolrate_report the outcome.

The key shift in Agentic RAG is that retrieval becomes a *decision*, not just a lookup. The agent can choose when to retrieve, what to query, and whether the results are good enough to use. That self-evaluation loop is what makes it so much more useful for complex, multi-step questions.

@Python_Dv The game changer is when agents decide to NOT retrieve. Knowing when the context is already sufficient saves latency and cost. Built this pattern into our pipelines and cut token usage by 40%.

@Python_Dv kamy.dev added Python to its language and users are loving it.