AI Agents, LLMs, RAGs & Agentic AI Explained | Rakesh Gohel's Latest Insights 🤖

📌 After months of feedback and iteration, we are finally releasing our first technical cohort, "AI Agent Engineering"

🔗 Enrol here: https://lnkd.in/gDEPcXBB

Comparing AI Agents with the modern GenAI architectures

Let us understand the core difference between them...

As GenAI systems become more intelligent, we are looking at a rapid rate of progression.

📌 Starting from level 1, lets see how systems have progressed so far:

1. LLM (Large Language Model)

- Context-Free Generation: Produces text purely from prompt input without external retrieval.

- Fast & Simple: Easy to deploy with low complexity, but limited in context understanding or integrating new data sources.

2. RAG (Retrieval-Augmented Generation)

- Knowledge-Enhanced: Combines LLM output with real-time retrieval from external sources for more accurate, up-to-date responses.

- Data-Dependent Precision: Excels at Q&A and knowledge tasks but is sensitive to the quality and structure of underlying data sources.

3. AI Agent

- Autonomous Task Execution: Uses planning, reasoning, memory, and tool integrations to complete workflows that need decision-making.

- Goal-Oriented Automation: Ideal for well-defined tasks like multi-step data processing or tool-based operations needing structured plans.

4. Agentic AI

- Multi-Agent Collaboration: Deploys multiple specialized agents that coordinate, divide labor, and even negotiate to handle complex problems.

- Adaptive & Persistent: Supports memory, feedback, and reasoning across agents to tackle large-scale tasks requiring ongoing strategy.

📌 Progression

1. LLM Workflow:

- Begins with next-word prediction on static training data—ideal for simple text generation and chatbots with limited context.

2. RAG:

- Enhances LLMs by retrieving real-time external knowledge, grounding responses with accurate, up-to-date information.

3. AI Agents:

- Introduces planning, memory, and tool use to autonomously execute multi-step workflows with reasoning.

4. Agentic AI:

- Evolves into a collaborative multi-agent ecosystem where specialized agents coordinate, share memory, and divide tasks to solve complex problems together.

📌 Use-Cases

1. LLM:
For generating text or answering simple, general questions without needing external data.

2. RAG:
For retrieving and summarizing up-to-date, domain-specific knowledge during a conversation.

3. AI Agent:
For automating single-user tasks that need planning and tool use—like research assistance, report generation, or workflow automation.

4. Agentic AI:
For managing complex, multi-step, multi-user processes where multiple specialized agents coordinate as an ecosystem.

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