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Retrieval & Reasoning

The Evolution of RAG: Why AI Agents Are Taking Over

2025-11-207 min read

Let's talk about something that's fundamentally changing how AI systems work: the shift from traditional RAG pipelines to agentic RAG. If you've been following enterprise AI developments, you've probably noticed that everyone from AWS to Microsoft to has been launching products centered around "agentic" capabilities. There's a reason for that, and it's not just marketing hype.

The Problem with Traditional RAG

Traditional Retrieval-Augmented Generation works like this: you ask a question, the system retrieves relevant documents once, and then generates an answer. Done. It's a straight line: query → retrieve → generate.

This works fine for simple questions. "What's our return policy?" Perfect. The system grabs the policy document and answers.

But what happens when you ask something complex? "Compare our Q3 2020 revenue growth to Q3 2023, and explain what factors drove the difference." Suddenly, that one-and-done retrieval approach falls apart. The system might miss crucial documents, pull in irrelevant information, or confidently generate an answer based on incomplete data. And here's the kicker: it has no way to realize it made a mistake.

Enter Agentic RAG

Instead of treating retrieval as a single step, it gives an AI agent control of the entire process. The agent can plan, retrieve information multiple times, validate what it finds, and course-correct when something doesn't look right.

Think of it like the difference between following a recipe blindly versus cooking by feel. Traditional RAG follows the recipe: "Step 1: Get ingredients. Step 2: Mix. Step 3: Serve." Agentic RAG tastes as it goes, adjusts seasoning, and might even realize it needs an ingredient that wasn't in the original list.

How It Actually Works

The most popular approach is called ReAct (Reasoning + Acting), and it's beautifully simple in concept:

Thought: The agent thinks about what it needs. ("I need Tesla's 2020 revenue numbers.")

Action: It takes an action. ("Let me search for Tesla's 10-K filing from 2020.")

Observation: It examines what it found. ("Got it—revenue was $31.5 billion.")

Then it repeats this loop until it has everything needed to answer confidently. If the first search doesn't return good results, the agent can reformulate its query and try again. If it realizes it needs additional context, it can retrieve more documents. If it generates an answer but suspects it might be wrong, it can double-check against the source material.

This is what makes it "agentic". The system has autonomy to make decisions about its own workflow.

The Architecture

If you want to understand or build an agentic RAG system, here are the key components:

The Agent Layer: This is typically a large language model (GPT, Claude, etc.) with a specialized prompt that instructs it to follow the ReAct pattern. The agent is the "brain" that decides what to do next.

Tool Registry: Agents need tools they can use. Common ones include:

  • Vector database search for semantic retrieval
  • Keyword search for exact matches
  • SQL queries for structured data
  • Web search for real-time information
  • Calculators for quantitative reasoning
  • APIs for external services

State Management: Unlike traditional RAG, agentic systems need memory. The agent has to remember what it's already retrieved, what tools it's used, and where it is in the reasoning process. Frameworks like LangGraph handle this by representing the workflow as a state graph with nodes (actions) and edges (decision points).

Evaluation: Good agentic systems can grade their own work. After retrieving documents, the agent asks: "Are these actually relevant?" After generating an answer: "Is this response properly grounded in the data I retrieved?" If the answer is no, it loops back and tries again.

The Trade-offs

Nothing's perfect, and agentic RAG has real drawbacks:

Latency: Every reasoning step is another LLM call. A traditional system might query the model once. An agentic system might query it five or ten times. That adds up to slower responses—sometimes tens of seconds instead of a few seconds.

Cost: More calls = higher API costs. For simple FAQ-style questions, traditional RAG is cheaper and faster.

Non-Determinism: Two identical queries might produce different reasoning paths. For some applications, that's a feature. For others (like regulated industries), it's a problem.

The Market Response

Recognizing that building these systems is complex, vendors are racing to offer "Agentic RAG as a Service." Progress Software just launched a service on AWS Marketplace where you can configure agents, choose your LLM, and deploy without managing infrastructure. They've even built in automatic evaluation (their REMi system) that grades every answer for relevance and accuracy in real time.

Getting Started

If you want to build your own agentic RAG system, the ecosystem has matured significantly:

  • LangGraph: The leading framework for stateful, graph-based workflows
  • LlamaIndex: Built-in ReAct agents with query engine tools
  • AutoGen: Microsoft's multi-agent collaboration framework
  • CrewAI: Specialized for orchestrating multiple agents with task delegation

Most people start with LangGraph's pre-built ReAct loop, then customize it with their own tools and prompts.

The Bottom Line

We're watching RAG evolve from a simple retrieval technique into autonomous reasoning systems. The rigid query-retrieve-generate pipeline is giving way to flexible agents that can think, validate, and adapt.

The pipeline era is ending, and the Agent era has only just begun.