Agentic RAG

Who this is for and what you will be able to do

This guide is for developers who already run retrieval-augmented generation (RAG) and want to know what changes when the model decides its own retrieval steps. By the end you should be able to describe the loop that Agentic RAG runs, explain how it recovers from failed queries, name the limits that stay in place, and judge whether your task suits it ^[1].

What Agentic RAG is

Agentic Retrieval-Augmented Generation is an emerging AI paradigm in which large language models plan their own next steps while they pull information from external sources ^[1]. Unlike a static retrieve-then-read pattern, it runs a series of calls to the LLM, interleaved with tool or function calls and structured outputs ^[1].

At each step the system checks what it has found, rewrites queries if needed, calls another tool if needed, and keeps going until it has a satisfactory answer ^[1]. This "maker-checker" style aims at three things: better correctness, handling of malformed queries (for example NL2SQL queries against a structured database), and balanced, high-quality results ^[1].

Why the model owns the reasoning

What makes a system agentic is that it owns its reasoning process. Traditional RAG often depends on a person defining in advance what to retrieve and when. An agentic system decides the order of steps itself, based on the quality of the information it finds ^[1].

Take a product launch strategy as an example. The model can decide on its own to retrieve current market trend reports with Bing Web Grounding, identify competitor data with Azure AI Search, correlate historical internal sales metrics from Azure SQL Database, and synthesize the findings through Azure OpenAI Service. It then checks the strategy for gaps or inconsistencies, and if it finds any, it runs another round of retrieval ^[1]. None of these choices is scripted by a person ^[1].

The system can also rewrite queries that fail, pick a different retrieval method, and combine several tools before it gives an answer. These tools can include vector search in Azure AI Search, SQL databases or custom APIs ^[1]. A fairly simple loop of "LLM call, tool use, LLM call" can produce well-grounded output without a heavy orchestration framework ^[1].

How the loop runs

A typical run has five parts ^[1].

• Initial call: the user's goal goes to the LLM.
• Tool invocation: if information is missing or the instructions are ambiguous, the model picks a tool or retrieval method. That might be a hybrid search in Azure AI Search over private data, or a structured SQL call ^[1].
• Assessment and refinement: after it reviews the returned data, the model decides whether the information is enough. If not, it refines the query, tries another tool or changes its approach ^[1].
• Repeat: the cycle goes on until the model has enough clarity and evidence to give a well-reasoned answer ^[1].
• Memory and state: because the system keeps state across steps, it can recall earlier attempts and their outcomes. That helps it avoid repeating itself and make better decisions as it goes ^[1].

Over many steps, this lets the model handle multi-step tasks without a person reshaping the prompt each time ^[1].

What happens when a search fails

Agentic RAG is designed to recover from dead ends such as irrelevant documents or malformed queries. It has three main ways to do so ^[1].

1. Iterate and re-query. It tries a new search strategy, rewrites a database query, or looks at another data set, rather than returning a weak answer ^[1].
2. Use diagnostic tools. It can call functions that help debug its reasoning or confirm that the retrieved data is correct. Azure AI Tracing is named as important for observability and monitoring ^[1].
3. Fall back on human oversight. For high-stakes cases or repeated failures, the model can flag uncertainty and ask a person for guidance. The model can then take that correction into account for the rest of the session ^[1].

The limits of agency

Agentic RAG has real autonomy within a task, but it is not a form of general artificial intelligence. Its capabilities are limited to the tools, data sources and policies that developers provide. It cannot invent new tools or step outside the domain it was given ^[1]. Three limits are worth keeping in mind ^[1].

• Domain-specific autonomy: it works toward user-defined goals within a known domain, using query rewriting and tool selection to improve results ^[1].
• Infrastructure dependence: what it can do depends on the tools and data developers connect. Going beyond them requires a person ^[1].
• Respect for guardrails: ethical guidelines, compliance rules and business policies still apply, and safety measures and oversight constrain what the agent may do ^[1].

Where Agentic RAG fits

Three kinds of task suit this approach ^[1].

• Correctness-first work, such as compliance checks, regulatory analysis or legal research. The model can check facts repeatedly, consult several sources and rewrite queries until the answer is well vetted ^[1].
• Complex database work, where queries often fail or need adjustment. The system can refine its queries against Azure SQL or Microsoft Fabric OneLake until the result matches the user's intent ^[1].
• Long-running workflows, where new information keeps arriving. The system can add new data as it goes and change strategy as it learns more about the problem ^[1].

Governance, audit trails and human review

The more autonomous the reasoning, the more it matters to see what happened. The model can produce an audit trail of its queries, the sources it used and the steps that led to its conclusion. Azure AI Content Safety and Azure AI Tracing / GenAIOps are named as tools that help with transparency and risk ^[1]. Without a clear record of actions, a multi-step run is very hard to debug ^[1].

Bias also needs attention. Developers can tune retrieval so that it draws on balanced, representative sources, and they can audit outputs regularly for bias or skewed patterns ^[1]. For sensitive tasks, human review stays essential. Agentic RAG is meant to give people better-vetted options in high-stakes decisions, not to replace their judgment ^[1].

Start small and keep the trail

If you want to try the idea, start with a narrow domain and two or three sources, such as a search index and one structured database. Log every query, every result and every rewrite from the start, so that you can see where the loop goes wrong. Then add the checks and human review that the sensitive parts of your task require ^[1].

This article has no code. The concepts and the tool names above are general and do not describe a specific setup of ours.

Adapted from lesson 5 of Microsoft's open-source course *AI Agents for Beginners*^[1], released under the MIT License.