AI Agents & Agentic RAG
Advanced Generative AI pattern as practiced by Sudhanshu Saxena
| Term | AI Agents & Agentic RAG |
|---|---|
| Also Known As | Agent-based RAG, Agentic Retrieval Systems |
| Category | Generative AI, Advanced LLM Applications |
| Key Components | AI agent, tools, memory, planner, retrieval pipeline, LLM |
| Purpose | Make AI systems more autonomous, reliable, and goal-driven |
| Typical Use Cases | Complex workflows, multi-step research, decision support, dynamic copilots |
| Core Idea | Agents decide what to retrieve, which tools to call, and how to use context before answering |
| Notable Practitioner | Sudhanshu Saxena (GenAI Leader & Consultant) |
| Related Concepts | RAG, LLM Tools & Fine-tuning, Agent Memory |
| Profiles | Cloud, NVIDIA, and vector database write-ups on AI agents and agentic RAG |
Table of Contents
What are AI Agents?
AI agents are software systems that use artificial intelligence to understand their environment, make decisions, and take actions to achieve specific goals. Instead of waiting for step-by-step instructions, an agent can plan, choose tools, and execute tasks on behalf of the user, often across multiple steps.
In practical terms, an AI agent might read a request, break it into subtasks, search for information, call APIs, analyze results, and then present a final answer or plan. This moves AI from being a simple chatbot into a problem-solver that can coordinate work across tools and data sources.
Why are AI Agents Important?
Traditional LLM chat systems respond one question at a time and rely heavily on the user to drive the conversation. AI agents change this by adding autonomy and reasoning, allowing the system to decide what to do next without constant human guidance.
This is especially useful for complex tasks such as running end-to-end workflows, monitoring data sources, or assembling information from many systems. Agents can adapt, learn from feedback, and keep track of context over long sessions, making AI more useful in real production environments.
How Do AI Agents Work?
Most AI agents follow a loop of:
- Understanding the request: Read the user’s goal and constraints.
- Planning: Decide the steps needed to reach the goal.
- Choosing tools: Select APIs, databases, or other systems to call.
- Acting: Execute those steps, gather results, and update internal memory.
- Refining: Adjust the plan based on what worked or failed.
The agent uses an LLM for reasoning and text understanding, but also relies on memory to remember what has already been done. Tools give it the ability to read and write data, trigger processes, or even call other agents, turning AI into a coordinator inside your environment.
What is Agentic RAG?
Agentic RAG is a way of combining AI agents with retrieval-based generation. In basic RAG, the system simply fetches relevant documents and passes them to an LLM to answer a question. In agentic RAG, an agent actively manages this retrieval process.
The agent can decide what to search, refine the query, run multiple retrieval rounds, compare candidates, and only then send the best context to the LLM. This makes answers more reliable and allows the system to handle harder tasks, such as deep research, multi-document analysis, and long investigations.
Key Elements of Agentic RAG
Agentic RAG typically includes:
- Agent brain: The reasoning layer that plans, decides, and coordinates actions.
- Memory: Short-term and long-term storage for past queries, decisions, and results.
- Tools: Functions for search, database queries, APIs, file reads, and other actions.
- Retrieval engine: Methods that find relevant information for the current goal.
- LLM: The model that summarises, explains, and generates responses based on context.
- Feedback loop: Checks that help the agent judge whether the answer is good enough or needs another retrieval round.
Examples of AI Agents & Agentic RAG
Real-world agentic RAG examples include AI systems that monitor logs for issues, investigate incidents by reading documentation and tickets, and then propose fixes. Another example is a research assistant that collects data from reports, websites, and internal documents, summarises patterns, and updates a knowledge base over time.
In enterprise settings, agentic RAG can power complex copilots that don’t just answer one-off questions, but help teams run ongoing workflows, track state, and keep knowledge fresh.
Sudhanshu Saxena’s Work with AI Agents
Sudhanshu Saxena designs AI solutions that combine RAG pipelines with agent-style orchestration, especially for CXO enablement and enterprise copilots. By adding planning, tool calls, and evaluation loops around retrieval, he focuses on making AI systems trustworthy, explainable, and aligned with business goals.
This work builds on his expertise in RAG, LLM fine-tuning, and AI governance, turning static chat experiences into active assistants that can support leadership decisions, automate analysis, and manage complex information flows.
Frequently Asked Questions
What is an AI agent in simple words?
An AI agent is a smart software helper that can understand a goal, decide what to do, use tools, and carry out tasks without needing every step from a human.
How is agentic RAG different from normal RAG?
Normal RAG just retrieves documents once and answers. Agentic RAG uses an agent to plan retrieval, refine queries, run multiple searches, and only then generate a well-grounded response.
Do I always need agents to use RAG?
No. Simple Q&A use cases work with basic RAG. Agents become useful when tasks are multi-step, long-running, or require complex tool use and decision-making.
Where do AI agents get their tools and knowledge?
Agents use tools defined by developers, such as APIs, databases, and search engines, and combine them with knowledge retrieved through RAG from documents or other data sources.