Deep Dive into AI World

Agentic Retrieval-Augmented Generation System

DataTalk’s AI uses an advanced Agentic RAG system to combine live and historical data, operator feedback, and documentation — producing actionable, grounded recommendations instead of guesses.

This approach ensures safety, reliability, and explainability in every decision.

To address these challenges, we have developed an advanced Agentic Retrieval-Augmented Generation (RAG) system. Unlike basic RAG that only retrieves documents, an Agentic RAG coordinates multiple specialized tools and agents to reason, verify, and act.

Our system includes tools that can:

• Search documentation – semantic search across manuals, technical bulletins, and procedures.
• Read live data – query PLCs, sensors, or SCADA tags in real time.
• Read historical data – access process historians or time-series databases for trends and correlations.
• Process live and historical alarms – interpret alarms, correlate them with past incidents, and recommend mitigation steps.
• Integrate operator feedback – learning from user corrections and feedback loops to improve recommendations.
• Cross-check multiple data sources – validating consistency between live data, historical logs, and documentation.

This architecture allows AI to not only generate text answers but also validate them against trusted data sources, drastically reducing the risk of hallucinations.

How our Advanced Agentic RAG System Works

Traditional LLMs are powerful at generating text but weak at handling real-world industrial data. They can hallucinate, miss critical context, or provide answers without evidence.
Our Agentic RAG system changes this by letting the LLM think like an engineer: it evaluates the problem, chooses the right tools, verifies results, and only then delivers an answer.

Step 1: Evaluate the Problem

When an operator asks a question or when an alarm is triggered, the LLM first analyzes the query:

• What is being asked (symptom, troubleshooting, explanation, procedure)?
• Which type of data is needed (documentation, live sensor data, historical trends, alarms)?
• What level of certainty and safety is required?

This diagnostic step ensures the system doesn’t just “guess” but instead frames the problem correctly.

Step 2: Select the Correct Tool(s)

The LLM then acts as an orchestrator. It chooses one or more specialized tools depending on the situation:

• Documentation search if the query involves procedures, manuals, or technical specifications.
• Live data agent if the issue needs real-time validation from PLC/SCADA.
• Historical data agent for trend analysis and root cause comparison.
• Alarm intelligence to correlate current alarms with past events.
• Vector store to retrieve semantically similar cases, even when the wording differs.

The system can chain multiple tools — for example, cross-checking a live overheating alarm with historical trends and maintenance notes.

Step 3: Fuse and Verify Results

The outputs of the tools are not blindly passed through. The LLM fuses them into a coherent picture, checks for contradictions, and prioritizes reliable sources.

• If two sources disagree, the system highlights uncertainty rather than delivering a wrong answer.
• If required, the LLM requests additional context until confidence is high enough.

Step 4: Deliver the Result

Finally, the system generates an actionable response that is:

• Grounded in real data (with references to alarms, logs, or manuals).
• Action-oriented — suggesting next diagnostic or corrective steps.
• Explainable — operators can see why the recommendation was made.

This process transforms the LLM from a text generator into an industrial problem-solving assistant.

Why It Works

Unlike general-purpose LLMs, which can hallucinate or ignore real-time and historical context, DataTalk’s Agentic RAG system ensures:

• Grounded, explainable answers
• Reduced risk of errors in safety-critical environments
• Continuous learning from operator feedback