𝗙𝗶𝗲𝗹𝗱 𝗡𝗼𝘁𝗲𝘀: 𝗛𝗼𝘄 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 𝗛𝗮𝗻𝗱𝗹𝗲𝘀 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮

A customer sends a support ticket. They ask for warranty details on specific servers from a past project. They also need contract terms and the current support contact.

Answering this is hard. It requires data from four different places:

  • CRM for customer history.
  • ERP for contract terms.
  • Asset management for serial numbers.
  • HR systems for staff details.

These systems use different databases and different permissions. Standard RAG fails here. It searches once and gives up if it finds nothing.

Agentic RAG solves this by turning retrieval into a plan. It does not just search. It thinks, acts, and checks its work.

Here is how the workflow works:

  1. The Orchestrator The system breaks the question into subtasks. It identifies which data sources to use and which tasks depend on others.

  2. The Query Rewriter Every system speaks a different language. One needs SQL. Another needs keyword search. The rewriter translates the user question into the right format for each tool.

  3. Parallel Retrieval The system queries multiple sources at once. It must respect security. The AI only accesses data the specific user has permission to see.

  4. The Sufficiency Checker This is the most important part. The system asks: "Is this enough information to answer the question?" If a piece is missing, like a specific PDF attachment, the system does not stop. It creates a new plan to find that specific file. It loops until it has the full picture.

  5. Synthesis The final agent gathers all fragments and builds a single, accurate answer with sources.

Agentic RAG is not a silver bullet. It is slower and more expensive than traditional RAG.

Use traditional RAG for simple questions in one database. Use Agentic RAG for complex, multi-step questions across many systems.

The goal is to move from a simple query-response model to a stateful workflow: Plan. Execute. Evaluate. Iterate.

Source: https://dev.to/luhuidev/field-notes-how-agentic-rag-handles-the-real-mess-of-enterprise-data-a68

Optional learning community: https://t.me/GyaanSetuAi