Traditional RAG works very well for static searches within a document; however, when it comes to dynamic real-world applications, it falls short. The Agentic RAG, based on self-driven reasoning and the use of tools coupled with cloud technology, is what the future of grounded LLMs looks like. Okta protects this future by leveraging AI agents as first-class identities within the security fabric, thereby eliminating novel risks associated with identities.
The Limits of Traditional RAG
Within the last year, Retrieval-Augmented Generation (RAG) has established itself as the new gold standard for grounding LLMs with the kind of in-house knowledge that companies want. A basic implementation of this is straightforward: a user submits a question, the system searches for relevant information (most likely using embeddings), and then generates an answer based on this information. It works incredibly well with static knowledge repositories for simple questions.
However, for RAG use cases in fast-moving, real-world scenarios where an AI system needs to reason, take actions, or handle multi-step tasks, its cracks start to show:
- Limited Reasoning: Traditional RAG tends to produce single-shot answers that don’t involve deeper reasoning or step-by-step thinking.
- Lack of Planning: It can’t break down complex problems or plan what to do next.
- No Integration: It also sits on an island, as there’s no built-in way to call external tools, APIs, or business systems, which limits its practical usefulness.
- Performance Issues: As documents grow and context windows fill up, performance becomes inconsistent and more complex to rely on.
From Static Retrieval to Agentic Reasoning
The next stage in this evolution is Agentic RAG. This RAG design goes beyond retrieval and turns the system into a purpose-driven agent extending traditional RAG with reasoning, planning, and tool use. Rather than simply pulling documents and returning a one-off answer, the system understands the user’s broader intent, decides what information it actually needs, and retrieves selectively. It can call out to external tools, APIs, or small code routines when those are required, and it doesn’t stop at a single pass: the agent can reason through a problem in multiple steps, re-query or invoke tools as needed, and refine its results until the task is complete. In short, Agentic RAG shifts RAG from a static lookup into an adaptive, multi-step system that can carry out work, and not just answer questions.
How Cloud Platforms Are Enabling Agentic RAG
Leading cloud providers are building first-class agentic capabilities into their GenAI ecosystems by bringing reasoning, orchestration, and retrieval together as managed services.
AWS Bedrock
AWS is pioneering this through its Agents for Bedrock framework:
- Bedrock Agents define reusable AI agents with instructions, memory, and the ability to orchestrate multiple tools.
- Knowledge Bases provide a vector-based retrieval layer with native integrations to OpenSearch Serverless, Pinecone, Redis Enterprise, and other services.
- Action Groups (Tools) are callable APIs or Lambda functions that agents can invoke as part of reasoning and execution.
This combination allows a Bedrock agent to plan and execute flows autonomously:
Retrieve docs → call a Lambda API → synthesize a final answer.
This setup enables a layer of orchestration that represents a native and serverless approach to the Agentic Rag, where the actions of retrieving and reasoning are coupled.
Azure AI
Microsoft’s Azure AI Foundry provides a parallel pattern through:
- Agent Orchestration and Memory in Azure AI Foundry’s Agent Service
- Retrieval supported through vector-backed Azure AI Search: semantic and hybrid retrieval
- Integration of tools through Azure Functions and APIs
It makes possible reasoning-aware retrieval and dynamic execution of tasks on Azure-based GenAI systems and enterprise data sources.
Practical Example: From Q&A to Intelligent Action
Consider a cloud operations assistant built on Agentic RAG:
- It retrieves compliance or cost policies from internal documentation.
- It uses reasoning to detect non-compliant configurations.
- It invokes APIs (via registered tools) to automatically remediate the issue.
Such systems go beyond retrieval. They reason, plan, and act within enterprise environments.
Why This Evolution Matters
Agentic RAG: This is a paradigm change in information retrieval to intelligent orchestration. To be specific, from a Platform Engineering perspective, with retrieval, reasoning, and acting in a combined manner, enterprises can unlock:
- Context-aware copilots for infrastructure, analytics, and data engineering
- Self-healing platforms that reason over telemetry and trigger automated fixes
- Next-gen internal developer portals (IDPs) and control planes that use AI to assist, verify, and act
Securing Agentic RAG with Okta
At Okta, we understand that the increasing adoption of Agentic RAG systems enables AI agents to independently gather data, APIs, and workflows, introducing risks that were previously unaccounted for from the perspective of identity. These AI agents are non-human identities that have dynamic lifecycles, privileged controls, and autonomy, which are difficult for traditional user-based security controls to govern.
Our platform treats AI agents as first‑class entities within the identity security fabric, enabling complete lifecycle management from provisioning and registration to authorization, monitoring, and de‑provisioning. We help organizations discover and classify agents, enforce least-privilege and just-in-time access, assign clear ownership, log actions, and respond to anomalous behaviour.
By baking Okta's agent identity and access controls into an Agentic RAG architecture, the enterprise can shift from "can the agent retrieve and reason?" to "can the agent be trusted to act securely, compliantly and within policy?" This is how AI systems will continue to be an accelerator for enterprise innovation, rather than introducing a new, unmanaged attack surface.
Conclusion
Agentic RAG is the necessary next step in the evolution of LLMs, shifting traditional retrieval into intelligent orchestration capable of real-world action on cloud platforms. The potential of this new capability calls for a new security model, and Okta is ready to elevate your AI agents to first-class citizen identities for secure management of their access. Bring your enterprise innovation to the next level and learn how to future-proof your Agentic RAG deployment with Okta.
