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As enterprises look to AI to automate tasks, streamline operations, and improve access to information, Retrieval-Augmented Generation (RAG) has become one of the most promising techniques in the field. RAG combines large language models (LLMs) with real-time retrieval mechanisms — giving AI agents the ability to generate relevant, accurate responses grounded in up-to-date knowledge.
But what does that actually mean for your business?
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 don’t just respond — they help employees work smarter and faster.
Let’s take a closer look at how RAG AI agents work, why they matter, and how Glean is taking them to the next level.
What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) is a powerful AI technique that enhances large language models (LLMs) with real-time, domain-specific knowledge, ensuring 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, ensuring responses are not just coherent, but contextually accurate and specific to the organization.
For enterprises managing vast amounts of unstructured data, RAG is a game-changer:
- Improves accuracy & 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. By ensuring AI works with trusted, real-time knowledge, organizations can unlock new levels of efficiency, decision-making, and innovation — without compromising on security or accuracy.
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:
- Query understanding: The agent interprets the user’s request and identifies what kind of information it needs to answer accurately.
- Semantic retrieval: It searches connected data sources using semantic search — ranking results based on meaning, not just keywords.
- Response generation: It combines retrieved context with its own capabilities to generate a precise, trustworthy response.
- Optional actions: In advanced use cases, the agent may take next steps — like sending a follow-up message, triggering a workflow, or summarizing a document.
This architecture allows agents to be more than just chatbots — they become active participants in your workflows.
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 transform enterprise knowledge management:
- 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, instantly, 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.
Glean agents: Going beyond generic AI chatbots
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 robust 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.
RAG architectures: From single-agent to multi-agent systems
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.
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.
Getting started with RAG AI agents
Deploying RAG AI agents isn’t just a technical decision — it’s a strategic 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 over 100 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.
RAG agents aren’t just smarter — they’re more useful
The real promise of RAG AI agents isn’t just that they generate better answers. It’s 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.
