A RAG AI agent is an AI agent that uses retrieval-augmented generation (RAG) to pull relevant, current information from company and external sources before it reasons, answers, or takes action. Retrieval grounds the agent’s outputs in trusted data instead of the model’s training data alone.
Plain RAG answers a single question with a single retrieval. A RAG AI agent, also called agentic RAG, plans its steps, retrieves again when it needs more context, and acts, like drafting a reply to a support ticket.
RAG enables the creation of intelligent AI agents that can reason across internal data sources, pull in external context, and take action on behalf of users. These agents answer questions and help employees finish work faster.
Let’s take a closer look at how RAG AI agents work, why they matter, and how Glean builds them for the enterprise.
What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) is an AI technique that enhances large language models (LLMs) with real-time, domain-specific knowledge, so responses are accurate, reliable, and grounded in trusted data. Instead of relying solely on static training data, RAG allows AI to retrieve and incorporate relevant information from enterprise sources — giving teams access to real-time insights while reducing the risk of hallucinations.
At its core, RAG has two key functions:
- Retrieval: AI searches across internal knowledge bases, CRMs, and enterprise apps to pull in the most relevant, permission-aware information.
- Generation: The LLM synthesizes the retrieved data with its existing knowledge, so responses are coherent, contextually accurate, and specific to the organization.
For enterprises managing vast amounts of unstructured data, RAG matters most:
- Improves accuracy and relevance – Ensures AI-generated content reflects the most up-to-date, organization-specific information.
- Reduces hallucinations – Grounds responses in real, vetted enterprise data, minimizing the risk of misinformation.
- Enhances knowledge management – Enables AI agents to retrieve and apply insights from across the company without manual effort.
- Delivers personalized, contextual experiences – AI can respond with customer-specific insights in real-time, driving deeper engagement.
As businesses scale their AI capabilities, RAG is becoming a key enabler of intelligent, enterprise-ready AI systems. Glean uses RAG to ground answers in company knowledge, with permission-aware retrieval so the model only sees content the user is allowed to access.
How RAG, agentic RAG, and AI agents differ
Traditional RAG retrieves once and generates an answer, while an AI agent plans and takes actions with tools. Agentic RAG, also called a RAG AI agent, puts retrieval inside the agent’s loop. The agent decides what to search, checks whether the results are good enough, and retrieves again before it answers or acts.
ApproachWhat it doesHow it retrievesCan it take actions?Best forTraditional RAGRetrieves relevant content and generates one answerSingle pass per questionNoFAQ lookups and policy questionsAI agent (without retrieval)Plans steps and uses tools to complete tasksNone, or only through specific toolsYesSimple task automationRAG AI agent (agentic RAG)Plans, retrieves, checks its results, and actsIterative and multi-sourceYesMulti-step work like ticket resolution
In a RAG AI agent system, retrieval runs inside each individual agent, so each agent retrieves the context it needs for its own step in the task.
Glean’s agentic reasoning architecture evaluates each query and decides whether fast search can answer it or whether it needs a multi-step agentic plan. A question like “What are the OKRs for the engineering department?” goes to search, while “Respond to this support ticket” gets a plan.
Glean’s Agentic Engine 2 extends this with adaptive planning across enterprise tools, re-planning as it learns.
How do RAG AI agents work?
RAG AI agents are built to act on behalf of users by combining retrieval, reasoning, and action. Here’s how they operate behind the scenes:
- Indexing and permissions: Before anyone asks a question, the system crawls, indexes, and embeds content from connected apps and attaches each source’s permissions.
- Query understanding: The agent interprets the user’s request and identifies what kind of information it needs to answer accurately.
- Planning: For complex requests, the agent breaks the work into steps and rewrites queries with enterprise context, like team names, project terms, and relevant data sources.
- Semantic retrieval: It searches connected data sources using hybrid search that combines semantic and keyword matching, filtered by what the user is allowed to see.
- Response generation: It combines retrieved context with its own capabilities to generate a precise, trustworthy response. Each response carries citations so users can verify the sources.
- Actions and reflection: In advanced use cases, the agent may take next steps — like sending a follow-up message, triggering a workflow, or summarizing a document. Before acting, the agent checks its own results and retrieves again if the context falls short.
With this architecture, agents become active participants in your workflows.
In Glean, the Enterprise Graph and permission-aware retrieval power steps 1 and 4. The Enterprise Graph captures the relationships between people, data, and activity.
Benefits of RAG AI agents for enterprises
RAG AI agents give enterprises a distinct advantage by grounding every response in verified, real-time data. This reduces misinformation, builds trust, and ensures outputs are accurate, relevant, and aligned with the nuances of the organization’s knowledge and workflows — critical for industries where precision and compliance are non-negotiable.
RAG AI agents also change how teams manage and use knowledge:
- Turn unstructured data into actionable insights – AI systematically organizes scattered information, making it easier to retrieve and apply.
- Optimize workflows and scale knowledge sharing – Employees get the right answers without manual effort.
- Improve customer and employee interactions – AI delivers real-time, contextually aware responses, leading to more engaging, personalized experiences.
By making AI smarter, more secure, and enterprise-ready, RAG AI agents enhance productivity, streamline decision-making, and drive stronger engagement across the business.
In early research on that same agentic reasoning architecture, Glean saw a 24% increase in the relevance of responses and actions. The Agentic Engine breaks complex requests into multi-step plans and runs each step with tools like search, data analysis, and expert search.
Single-agent and multi-agent RAG architectures
Not all RAG agents are built the same. The architecture behind them can vary based on how complex the task is, how much data they’re retrieving, and how the system is expected to respond. Most deployments fall into one of two categories: single-agent or multi-agent systems.
Single-agent RAG (routing agent)
In a single-agent setup, one AI agent handles the entire process — from interpreting the user’s question to retrieving information and generating a response. Think of it as a smart, self-contained assistant. It evaluates the query, selects the most relevant data source, and pulls in the information it needs to provide a useful answer.
This works well when:
- The query is straightforward.
- Data sources are clearly defined.
- Speed and simplicity are top priorities.
For many teams, this is a great place to start — it’s efficient, focused, and easier to implement.
Multi-agent RAG systems
When queries get more complex or span multiple domains, a multi-agent architecture can offer more flexibility and precision. In this setup, you have a group of specialized agents working together, each responsible for a specific task.
Here’s how it works:
- One agent interprets the query.
- Others retrieve information from different sources — internal tools, external data, customer records.
- A coordinator (or “master agent”) orchestrates the process, breaking down the task and stitching together a final response.
This model allows agents to handle more layered questions, synthesize multiple perspectives, and adapt to dynamic enterprise environments. It’s especially useful in large organizations where data lives in many places and workflows are interdependent.
Glean Agents support both patterns. Teams can build a single-purpose agent in Agent builder or orchestrate several agents across systems with agent orchestration.
Use cases for RAG AI agents
Here’s how enterprises are already seeing value from RAG-powered agents:
- Customer support: Agents can resolve tickets instantly using real-time product documentation, previous interactions, and CRM data — improving resolution time and satisfaction.
- Sales and marketing: By pulling data from recent interactions and historical deals, agents help reps personalize outreach and identify upsell opportunities.
- Healthcare: Clinicians use agents to summarize patient histories, interpret lab results, and surface treatment guidelines based on the latest research.
- Financial services: Agents analyze risk reports, summarize regulations, and generate personalized financial recommendations — all in a compliant, auditable way.
- IT service management: Agents triage help desk tickets, retrieve past incident resolutions and runbooks, and draft a fix or route the ticket to the right team.
- Engineering: Agents pull design docs, recent pull requests, and incident channels to help engineers debug production issues.
Challenges of building RAG AI agents
RAG AI agents are only as reliable as the retrieval, permissions, and data behind them. Here are five challenges to plan for, and how to address each one:
- Retrieval quality: Weak retrieval leads to confident wrong answers, and Databricks names it the most common RAG failure mode. Tune search relevance before tuning prompts.
- Permissions and data leakage: An agent that ignores source permissions can expose sensitive data to the wrong people. Enforce permissions at retrieval, before content reaches the LLM.
- Stale or scattered data: Data spread across many apps goes out of date quickly. Keep it current with continuous indexing through native connectors.
- Latency on multi-step tasks: Each retrieval and reasoning step adds time. Route simple questions to search and save agentic reasoning for complex requests.
- Evaluation: Teams need a repeatable way to measure retrieval and answer quality. Grade whether the agent found the right documents and whether it used them correctly.
Glean enforces the existing permissions of your data sources, so users and agents only see what they’re allowed to access. Glean Protect continuously finds sensitive content that’s been overshared and automatically hides it.
How Glean agents work with enterprise context
Glean agents are built on our enterprise-grade Work AI platform. That means they’re not just smart — they’re secure, scalable, and deeply integrated with the way your organization works.
What sets Glean agents apart?
- Universal knowledge access: Glean agents combine structured and unstructured enterprise data with real-time internet knowledge, giving users answers grounded in both internal and external context.
- Horizontal integration: These agents aren’t siloed to a single team or task. They can assist across departments — support, engineering, HR, sales, and more.
- Agentic reasoning: Glean agents can handle complex, multi-step processes, perform multi-step analysis, and even initiate follow-ups — without hand-holding.
- Built-in governance: With fine-grained permissioning and enterprise-grade security, responses always respect data access controls.
By embedding Glean agents across the tools your teams already use — like Slack, Gmail, Zendesk, and Salesforce — you enable smarter, more connected work across your org.
Getting started with RAG AI agents
Deploying RAG AI agents is a business decision as much as a technical one. And like any smart rollout, it starts with a clear understanding of where these agents can add the most value.
1. Start with a specific use case
Look for high-friction workflows where employees are constantly searching for answers, switching between tools, or handling repetitive requests. Good candidates include:
- Employee onboarding
- Support ticket resolution
- Sales enablement
- Contract analysis
The goal isn’t to do everything at once. Start with one clear use case where better retrieval and reasoning will save time or improve accuracy.
2. Connect the right data sources
RAG agents are only as effective as the data they can access. Identify the internal systems, wikis, file repositories, and external sources your teams rely on. Then, make sure your AI platform can connect to them securely and in real time.
With Glean, this step is fast — we offer more than 250 prebuilt connectors, with granular permissioning baked in.
3. Choose a platform built for flexibility
To get the most out of RAG agents, you’ll need a platform that can support both simple and complex use cases — whether it’s a single assistant answering HR questions or a multi-agent system generating insights from layered financial data.
Look for:
- Built-in orchestration
- No-code customization
- Enterprise-grade security and access controls
Glean’s platform gives you that foundation, without heavy implementation or custom dev work.
4. Take an iterative approach
Even the smartest AI agents get better over time. Launch with a pilot group, collect feedback, and make refinements based on real-world usage. This helps build trust and ensures the experience improves with every interaction.
During the pilot, track answer accuracy, citation usage, and time saved so you can measure each improvement.
RAG agents make AI useful for real work
The real promise of RAG AI agents is that they can work — retrieving context, understanding intent, and taking action across your organization’s most complex systems.
And while many platforms are racing to offer some version of this, Glean agents are built for the real world of enterprise. They understand your company’s language, respect your permissions, and integrate with the tools your teams already use.
Curious how it all works? Request a demo and see how Glean agents can help your teams work smarter.
Frequently asked questions
What is the difference between RAG and an LLM?
An LLM generates answers from the data it was trained on. RAG retrieves current company or external data at query time and gives it to the LLM, so answers reflect information the model never saw in training.
How is a RAG AI agent different from agentic RAG?
The two terms are largely interchangeable. Both describe an AI agent that controls when, where, and how often retrieval happens as it works through a task.
Does RAG stop AI hallucinations?
RAG reduces hallucinations by grounding answers in retrieved sources with citations you can check, but it doesn’t eliminate them. Retrieval quality determines how much that grounding helps.
Is RAG better than fine-tuning?
RAG and fine-tuning solve different problems: RAG adds fresh, permissioned knowledge at query time, and fine-tuning changes how the model behaves. Many enterprises start with RAG because it keeps answers current without retraining the model.
Do RAG AI agents respect data permissions?
RAG AI agents respect permissions only if the retrieval layer enforces them. Glean enforces each data source’s existing permissions, so agents only use information the user is allowed to see.
