From Generative AI to Agentic AI: RAG, TAG, A2A, and MCP

🧠 Introduction:

AI has come a long way from just generating poems and paragraphs. Today, it’s not just talking — it’s thinking, planning, retrieving real data, calling tools, and even collaborating with other AI agents.

But how did we get here?

This blog takes you on a fast, 5-minute journey through the evolution of intelligent AI systems — from Generative AI to Agentic AI — breaking down key milestones like RAG, TAG, Agents, A2A, and the protocol that holds it all together: MCP.

🚀 1. Generative AI — The Creative Engine: It can generate new content, such as text, images, audio, and videos, based on learned patterns from existing data.

“Write a birthday poem for my dog.”

Boom — it creates one instantly.

✅ Strength: Creativity and natural language generation
⚠️ Limitation: No access to real-time facts or external data (like calendars, databases)

📚 2. RAG — Retrieval-Augmented Generation
Want your model to provide accurate, up-to-date information? That’s where RAG comes in.

RAG enables an LLM to retrieve relevant facts from external sources — like PDFs, websites, or internal documents — before generating a response. It’s like giving your model access to a reference library.

Example:
“Summarize company policies.”
🔍 The system pulls the most recent policy document, then the model reads and summarizes it accurately.

✅ Strength: Enhances answers with factual, real-time data
⚠️ Limitation: Still a passive system — it doesn’t take actions or interact with external tools or environments

🛠️ 3. TAG — Tool-Augmented Generation
What if you want your model to do things, not just say things?

That’s the power of TAG. Tool-Augmented Generation connects an LLM to real-world tools and APIs, enabling it to take actions on your behalf.

Examples:
“Check weather in Paris” → Calls get Weather API
"Book a meeting" → Triggers the Google Calendar API

✅ Strength: Empowers the model to interact with external systems and perform real tasks
⚠️ Limitation: Requires orchestration — while the model can execute tools, it doesn’t strategize, plan ahead, or evaluate outcomes

🧠 4. Agents — Thinking & Acting Entities
Agents take things a step further. They’re LLMs that not only understand your goals, but can plan, act, and adapt along the way.

An agent can:
✔️ Understand objectives
🧭 Plan next steps
🔧 Use tools
🔄 Respond to feedback

Example:
“Plan a Tokyo vacation under $3000.”
🎯 The agent breaks it down:

• Checks the budget

• Finds affordable flights

• Suggests hotels

• Asks if a visa is needed

✅ Strength: Handles multi-step reasoning with tool usage
⚠️ Limitation: Scaling requires structure — how do agents coordinate, delegate, or work in teams?

🧑‍🤝‍🧑 5. Agentic AI — A Team of Smart Collaborators
Picture a group of AI agents, each with a specialized role:

🧳 Travel Agent
💰 Finance Agent
📄 Document Agent

These agents can:

• Assign tasks among themselves

• Share relevant context

• Adjust strategies based on results

• Communicate using agent-to-agent protocols (for requests, responses, error handling)

This is the essence of Agentic AI — a dynamic network of intelligent, goal-oriented agents working together.

✅ Strength: Handles complex, multi-domain workflows
⚠️ Challenge: Requires shared protocols for tools, task chaining, and communication

🔗 6. Model Context Protocol (MCP) — The Key to AI Collaboration

Think of MCP as the universal connector that enables smooth communication between different AI systems.

As we’ve evolved from individual creative models (Generative AI) to systems that can retrieve real-time data (RAG), use external tools (TAG), and finally collaborate as a team of AI agents (Agentic AI), there’s been a challenge: how can all these systems talk to each other and access information seamlessly?

Enter MCP. It acts as a single standard protocol that allows AI agents to:

• Access any data

• Communicate clearly

• Connect to any tool

In short, MCP simplifies the collaboration between AI systems by providing a standardized way for them to connect, communicate, and access resources. It eliminates the complexity of building custom connections between each system, allowing developers to focus on making the AI smarter.

🔗 Model Context Protocol (MCP) — In Action

Let’s revisit your Tokyo vacation planning agent.

🎯 Goal: Plan a Tokyo vacation under $3000.

Now, instead of one large agent doing everything, multiple specialized agents are working together:

• 🧳 Travel Agent – Finds flights & accommodations

• 💰 Finance Agent – Tracks expenses & budget

• 📄 Visa Agent – Checks visa requirements

• 🕒 Scheduler Agent – Aligns dates with your availability

Without MCP: each agent would need custom integrations to talk to others, leading to messy communication and data silos.

With MCP:

• The Travel Agent uses MCP to fetch flight options from multiple airlines via standard APIs.

• It shares the options with the Finance Agent using a unified request format:
  "GET /options/flights?dest=Tokyo&budget=1500"

• The Finance Agent evaluates the cost, updates the budget context, and responds in MCP format:
  "RESPONSE /budget/update { remaining: 1350 }"

• Meanwhile, the Visa Agent pulls data from a government database using MCP to check if you need a visa.

• The Scheduler Agent accesses your Google Calendar through MCP to find open travel dates.

📡 MCP makes this all seamless.

Every agent:

• Speaks the same protocol (MCP)

• Shares state and updates in real-time

• Understands responses and errors

• Connects to tools (APIs, calendars, databases) using a unified adapter layer

✅ Result: A coherent, well-structured trip plan built by a team of AI agents, each doing what they do best — all made possible by MCP.

✅ Strength: It makes AI systems more efficient by letting them easily connect, communicate, and access all the tools and data they need.
⚠️ Challenge: For MCP to work effectively, all systems need to adopt this protocol, and security must be top priority to protect the data being shared.