Here we go again! Another wave of new terms is making ripples across the AI community. Keeping an eye on all these trends can be tough (especially when many of them are, let’s be honest, just buzzwords! 😏)
In particular, the latest trend is an agentic approach to AI. Look around, and you’ll see everyone talking about AI agents. More and more companies are building them, planning to, or at least wanting to.
But are agentic AI and agentic RAG the real future? Or just more hype to wade through? 🤔 Well, keep reading to find out!
From “Traditional” AI to a New Agentic Approach
AI is evolving at a breakneck pace, and even though it feels weird to say it, we can now refer to some approaches as “traditional AI”! 😲
We’re talking about all those LLM integrations we’ve seen over the past few months (I mean, even Paint got AI features!).
Now, just like it usually happens when a new technology or cool feature/practice comes out, everyone wants to jump on board. (Remember when every app suddenly had “Reels” or some spin-off? Yeah, that frenzy. 😉)
With time, the technology itself evolves, and users and the market determine which applications are truly worth exploring and make sense. 🦖
So, it’s no surprise that in the past few weeks, we’ve moved beyond earlier AI approaches to something much more dynamic: Agentic AI and Agentic RAG! ✨
Think of “traditional AI” and earlier LLM integrations like a brilliant but somewhat passive assistant. You’d ask it a question, and it would give you an answer. For example, you ask a chatbot, “What’s the weather?” and it tells you. Simple, direct, and effective for many tasks. 🌤️
So, why the shift? We wanted AI that could do more, not just tell more!
We need systems that can plan, execute multi-step tasks, interact with applications, self-correct, connect to tools (via new AI protocols!), and much more. This is where agentic AI and agentic RAG workflows step in!
Instead of just answering, an agentic AI system acts like a proactive problem-solver superhero. 🦸But is this the future of AI or just hype? Let’s learn more! 👇
Decoding Agentic AI: What’s the Hype About?
Time to dive deeper into the world of agentic AI 🤿.
Defining Agentic AI
So, what exactly is agentic AI? Imagine your current AI assistant, but supercharged with the ability to think, plan, and act autonomously to achieve complex goals.
It’s not just answering questions anymore; it’s actively problem-solving based on a given prompt! 🧠 + 💪
At its core, agentic AI uses LLMs integrated into “AI agents.” 🕵️♂️
Those AI agents break down big, complex tasks into smaller, manageable steps. Then, for each step, they utilize various tools (often through MCP integrations) to complete the micro task—all of that autonomously!
Once all the individual steps are completed and a good result is achieved, the agent integrates these results to deliver a final outcome. This could be generating content—much like regular LLMs do (but with far more accurate results due to the multi-step process)—or even performing real-world operations like buying groceries online for you, given a shopping list. 🛒
How to Achieve It
Agentic AI is a broad term, and for good reason—there are numerous ways to bring it to life!
In most scenarios, an AI agent doesn’t operate in isolation. Instead, it orchestrates multiple sub-agents (even remotely, thanks to protocols like Google’s A2A 🌐). Each sub-agent is typically powered by one or more LLMs and equipped with specialized tools to accomplish specific goals.
So, in essence, Agentic AI means orchestrating LLMs and the tools they can connect to into a goal-driven workflow. 🎯
In this exciting new frontier, several possible architectures are emerging:
- Sequential Agents: One agent completes a task, then seamlessly passes its output to the next agent in the chain. Agent 1 ➡️ Agent 2 ➡️ … ➡️ Agent N.
- Loop Agents: Imagine an agent that executes its sub-agents repeatedly, either for a set number of iterations or until a specific condition is met. Think of it as iterative refinement! 🔄
- Parallel Agents: For tasks that can be performed independently, sub-agents are run concurrently to speed up the process! ⚡
- And more…
The core idea is to craft a multi-step workflow where highly specialized agents (which excel at solving particular tasks) work together. This combination leads to highly sophisticated, autonomous AI systems capable of tackling incredibly diverse challenges. 🚀
Things become much easier to understand with a real-world example: See how you can build a journalist AI agent using three sub-agents! 🗞️
Traditional AI vs Agentic AI
|
Feature |
Traditional AI |
Agentic AI |
|---|---|---|
|
Implementations |
Usually, integration with a single LLM |
A set of AI-orchestrated agents, each integrated with its own LLMs and specialized tools |
|
Core function |
Generating content by answering queries |
Proactive problem-solving focused on achieving complex goals |
|
Autonomy |
Reactive; waits for explicit instruction |
Autonomous; plans, executes, and self-corrects |
|
Task handling |
Single-step tasks or simple sequences |
Complex, multi-step workflows where problems are broken down. |
|
Tool usage |
Possible plugin integration |
Extensive integration with external tools, APIs, and more via AI protocols like MCP |
|
Interaction |
Primarily text-in, text-out |
Interacts with applications, real-world systems, and other agents |
|
Example |
“Give me the best places I should see in my 3-day trip to Miami” |
An AI vacation planner looking for fresh and new places to visit on your trip, while simultaneously booking flights, hotels, and buying tickets for the selected experiences for you |
|
Best analogy |
A knowledgeable assistant |
A highly skilled project manager or a team of specialized experts |
|
In emojis |
🧠 |
🧠 + 🦾 |
Unpacking Agentic RAG: What You Need to Know
Time to explore agentic RAG, the new frontier of retrieval-augmented generation.
What It Is
So, you’ve heard of RAG (Retrieval-Augmented Generation), right? It’s like giving an LLM a super-powered library card to find accurate, fresh, and highly relevant information before it produces an answer. 📚
That’s great for grounding LLMs, but there’s been a catch: you had to find and pass that content to the AI yourself. 😫
Well, agentic RAG takes that concept and injects some serious autonomy!
The core idea here is to apply agentic AI principles directly to the RAG workflow. This means you’ll have an agent-based system that can strategically plan context data retrieval. 🤯
Think of it as having a dedicated AI research engine! This agent doesn’t just passively search. Instead, it performs multiple targeted searches 🔍, intelligently assesses the quality of information it finds, and even refines its queries on the fly based on what it discovers.
This proactive process means the agentic RAG system can find, understand, and select high-quality content all on its own. The meticulously curated information is then fed downstream to other agents in the workflow, enabling them to produce far more accurate and nuanced results.
How to Implement It
Implementing agentic RAG revolves around designing a specialized “retrieval agent” equipped with LLMs and the right tools to interact with and extract data from diverse sources like databases, web APIs, and the company’s knowledge base.
This clever agent will handle the heavy lifting:
- Turn the prompt into optimized search queries.
- Apply those searches across all the sources it has access to (potentially even in parallel! 🔀).
- Evaluate the relevance of the retrieved content.
- Summarize the retrieved, high-quality information.
Finally, the meticulously curated information is then passed to a “generation agent” (or other specialized agents) to use that data to craft the final output.
⚠️ Note: Steps 1-3 can be repeated multiple times by the AI orchestrator if the extracted content isn’t considered accurate or sufficient enough.
Traditional RAG vs Agentic RAG
|
Feature |
Traditional RAG |
Agentic RAG |
|---|---|---|
|
How context retrieval works |
The LLM’s answers are grounded in a pre-defined knowledge base that it can access |
A dedicated retrieval agent strategically plans, executes, and refines multi-step searches across diverse sources |
|
Autonomy |
Reactive retrieval |
Proactive and autonomous retrieval |
|
Complexity |
Simpler to set up for basic Q&A tasks. |
More complex to design and implement due to the orchestration of multiple agents and specialized tools |
|
Example |
“What is the future of AI given the content in these research papers: <RESEARCH_PAPERS>?” |
An agent workflow tasked with: “Summarize the latest trends in AI from recent academic papers.” |
|
In emojis |
📚 ➡️ 💬 |
🧠 🗺️ 🔍 ➡️ 📝 ➡️ ✨ |
What You Need To Build Agentic AI and Agentic RAG Workflows
So far, the narrative in this article might make you think that agentic AI and agentic RAG are always superior to “traditional” AI and RAG.
While these “agentic” reinterpretations of AI and RAG can definitely offer better accuracy and more complex automation, building an entire AI workflow with multiple specialized agents talking to each other isn’t always the best approach—especially for simple tasks. 🤯
Remember, AI was born to simplify and automate. Over-engineering it can definitely be counterproductive. 🙅
Agentic AI and agentic RAG truly shine when you need to repeat elaborate actions or demand highly accurate results multiple times. In these scenarios, the time and effort invested in building the entire agent-based workflow make perfect sense.
Now, even if you decide an agentic workflow is the right path, implementing it is a completely different challenge. You’ll need an entire architecture of tools and solutions to support your agents. Think about:
-
Data packets: Fresh, curated, AI-optimized datasets specifically designed for RAG workflows. 📦
-
MCP servers: Servers packed with tools for data conversion, data retrieval, browser interaction, and much more. ⚙️
-
SERP APIs: AI-integrable APIs that LLMs can use to retrieve fresh and accurate content from search engines—for RAG pipelines. 🔎
-
Agent browsers: AI-ready browsers that agents can connect to for visiting and interacting with webpages while bypassing IP bans, CAPTCHAs, and other roadblocks. 🆓
-
And many other tools! (yes, the ecosystem is constantly growing 📈 ).
In other words, to truly implement Agentic AI and RAG workflows with no effort, you’d need access to a comprehensive AI and BI infrastructure, just like what Bright Data offers, to support the entire lifecycle of your AI initiatives!
Final Thoughts
Now understand what agentic AI and agentic RAG refer to, how to implement them, and how they stack up against their “traditional” counterparts. You’re fully up-to-date with the latest directions in the AI evolution!
As we highlighted, bringing these powerful agentic workflows to life requires an AI infrastructure that can support your AI agents and workflows from start to finish.
At Bright Data, our mission is simple: to make AI accessible for everyone, everywhere. So until next time—stay curious, stay bold, and keep building the future of AI! 🦄
