RAG gave language models a way to pull in relevant information. Agents gave them a way to plan and act. Now those two systems are being combined to handle more complex, multi-step tasks — ones that involve retrieval, reasoning, and real-time decisions.
This setup is starting to show up across a range of GenAI use cases: assistants that troubleshoot internal docs, copilots that write and test code, systems that switch between tools based on live context. Agentic RAG makes that possible by connecting retrieval with structured reasoning in a tight loop.
But these systems come with more moving parts, and more room for failure. You’re stacking retrieval, planning, tool use, and generation into one pipeline. Without proper evaluation, it’s hard to know where things are working and where they’re quietly breaking.
In this piece, we’ll break down how RAG and agents work on their own, what happens when you combine them, and how to evaluate Agentic RAG systems so they actually hold up in the real world.
What is RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is a technique that helps language models give better, more grounded answers by pulling in information from external sources. Instead of relying only on what the model “knows” (which can be outdated or incomplete), RAG lets it search through a database, documents, or API to find the right context, and then generate a response using that.
At its core, RAG is about combining two steps:
- Retrieval – finding relevant data based on the user’s query.
- Generation – using that data to craft a coherent, useful answer.
This method became popular because it reduces hallucinations, improves factual accuracy, and keeps outputs more up-to-date, especially when dealing with fast-moving or niche topics.
Now that RAG is a core part of many enterprise AI systems, we’re starting to see the agents that not only read retrieved content, but act on it.
What is an AI Agent
An AI or LLM agent takes a task and runs with it. It plans, gathers what it needs, and moves through the steps without waiting for instructions every time. It can use tools, run code, pull info from documents, or talk to APIs.
An AI agent works through goals rather than just prompts. You give it an outcome, and it figures out how to reach it. Sometimes that means asking follow-up questions. Other times, it means pulling in extra context or switching tools mid-task.
The key thing: agents are designed to work through multi-step problems. And when you give them access to external knowledge through RAG, they get even better at it. Let's look at how the two come together.
Agentic RAG
Agentic RAG combines the structure of RAG with the planning abilities of agents. The agent starts with a goal, retrieves the context it needs, and takes action based on that information. It works through tasks in steps, using real data to guide each move.
RAG Agent Components
Agentic RAG systems rely on a few core parts working together. These components shape how the agent thinks, acts, and improves over time.
Memory
Memory helps the agent retain useful context from past interactions. It tracks what’s been said, what’s been tried, and what worked. This lets the system avoid repeating steps and get smarter with every cycle.
Tools
Tools are how the agent gets things done. These might include search functions, document retrievers, web scrapers, or APIs. Tools extend the agent’s abilities beyond pure text generation, allowing it to fetch real-time data, process information, or trigger workflows.
Reasoning
Reasoning is handled by the language model. It’s what helps the agent plan, make choices, and move through complex problems. Most RAG agents follow a ReAct-style loop: think, act, observe, and decide the next step, until the agent reaches its goal.
How Does a RAG Agent Work?
A RAG agent moves through a loop of planning, retrieving, and refining until it reaches a solid outcome. Here's what that process typically looks like:
1. Understanding the user query
The agent starts by analyzing the user’s input. It may rewrite the query, sometimes more than once, to make it easier to handle. This is also where the agent decides whether it needs more context to proceed.
2. Triggering retrieval
Then, the agent triggers the retrieval step. This can involve one or several agents working to determine what sources to pull from, like real-time user data, internal documents or databases, public web sources and APIs.
3. Filtering and reranking results
Once the data is collected, it’s narrowed down by a reranker. This model is stronger than the usual embedder and helps isolate only the most relevant information.
4. Composing the output
When the context is ready, the agent builds a response using a language model. This could be a final answer or the next step in a task.
5. Evaluating the output
The agent checks the quality of the response. If it meets the bar, it sends it back to the user. If not, it may rewrite the query and run the loop again. This cycle continues within a defined limit to avoid getting stuck.
Vanilla RAG vs. Agentic RAG
Traditional, or Vanilla RAG is your classic setup: when you ask a question, the AI pulls relevant info from a fixed database and uses it to answer. Think of it like a smart search engine with a single source. It grabs what it can in one go, then responds, without follow ups and extra digging.
Agentic RAG takes it further. It acts more like an AI assistant with initiative. Instead of one fixed search, it can plan steps, use multiple tools (like web search or APIs), and adjust based on what it finds. If the first result isn’t enough, it tries again, more like a researcher, not just a retriever.
In short:
- Vanilla RAG = find and answer once, from one source.
- Agentic RAG = reason, search, act, and repeat if needed.
If your task is simple Q&A from a known knowledge base, vanilla works fine. But if your use case needs deeper reasoning, tool use, or step-by-step actions, you can switch to agentic.
RAG Agent Evaluation
When you're building with RAG agents, it's easy to assume things are working just because the final output looks fine. But these systems are complex. They're rewriting queries, pulling context from different sources, calling tools, and generating multi-step responses. A lot can go wrong in the middle, and if you're not looking closely, you'll miss it.
RAG agents often fail quietly. Maybe the retrieval step pulled the wrong document. Maybe the reranking buried the useful context. Maybe the agent skipped a reasoning step or made decisions based on outdated information. If you're not tracing that process, you won’t catch it. And if you don’t catch it, you're shipping something that’s broken.
Evaluating retrieval systems on their own is already hard. The same goes for evaluating agents. But with RAG agents, you’re evaluating both, at once. You’re looking at how retrieval and reasoning interact, how tools are used, how context flows through the entire pipeline. It’s a different level of complexity, and you need everything to work together cleanly. That’s why the platform you use to build your evaluation sets matters just as much as the system you're evaluating.
Evaluating RAG Agents with SuperAnnotate
SuperAnnotate gives you a clean, structured way to evaluate every part of your RAG agent, from the original query all the way to the final output. You can trace and evaluate how a response came together, what tools were used, how context was applied, and where decisions were made.
Here’s what makes it work for RAG agent evaluation:
- Custom interfaces, built around your pipeline
Every agent setup is different. SuperAnnotate lets you build evaluation views that actually match your workflow, whether you’re showing prompt rewrites, retrieved content, tool usage, or final answers with scoring rubrics. You can also control who sees what, depending on their role. - Workflow routing that doesn’t get messy
Complex evaluations often involve multiple steps, from first-pass annotation to expert review or escalation. SuperAnnotate supports that flow out of the box, so you’re not managing it manually in spreadsheets or Slack threads. - Real collaboration
Evaluating edge cases takes conversation. You can leave feedback, ask follow-up questions, or return tasks for clarification — all in one place, right on the example. - Built-in automation support
You can run automated checks alongside human review. For example, use LLMs to pre-score outputs or compare reasoning paths, then flag any low-confidence results for escalation. It saves time without cutting corners. - Analytics that surface patterns
Once your evaluations start piling up, you need a way to make sense of them. SuperAnnotate gives you breakdowns by prompt type, model version, rubric score, or task category. You’ll start to see where things consistently go wrong, and where your next round of improvements should focus.
RAG agent systems need evaluation because the failure points are often subtle and easy to overlook. SuperAnnotate gives you the structure to catch those weak spots early, and improve with clarity.
Databricks Case Study
Databricks had been building a RAG-based assistant capable of handling complex workflows, from document retrieval to writing and debugging SQL pipelines. To evaluate its performance, they initially relied on an LLM-as-a-judge setup. But the results were inconsistent. The system struggled with bias, vague scoring, and unclear feedback signals.
That’s where SuperAnnotate stepped in. We worked with the Databricks team to bring human oversight into the loop, focusing on three core steps:
- Define a clear evaluation framework tailored to their assistant’s goals
- Configure SuperAnnotate’s platform around their workflow and data
- Train a dedicated team of expert reviewers to build a high-quality evaluation dataset
This gave Databricks the structure it needed to improve performance quickly and reliably. They tripled the speed of their evaluations and reduced costs significantly.
If you're running into similar issues like unclear scoring, inconsistent judgments, or outputs that pass inspection but still feel off — we can help you break it down. Book a short call, share a few examples, or tell us what part of your pipeline feels the noisiest. We’ll show you how to trace it, evaluate it, and move forward with confidence.
Final Thoughts
Agentic RAG systems involve a lot of moving parts — retrieval, planning, memory, tool use, reasoning. Each one adds value, but also adds complexity. And without visibility into how those parts interact, the system becomes hard to trust.
Evaluation is what brings clarity. It helps you trace where things go off, spot patterns, and improve the system with real feedback — not just guesses. When you can see the full path from query to response, you’re in a better position to build something reliable.
This doesn’t require overcomplicated processes. What it does require is the right structure: the ability to review each step, compare model behavior over time, and involve the right people at the right stage. With that in place, iteration becomes faster and more focused.
As these systems show up in more production workflows, the teams that invest in proper evaluation will move faster — and with more confidence.
