Aryan Chauhan

Posted on Jul 9

Why AI Agents Are Suddenly Everywhere (And What the Heck is an MCP Server?)

#ai #llm #mcp #serverless

You've seen it. I've seen it. The entire tech world has seen it. One minute, we were all impressed by chatbots that could write a poem. The next, we're watching demos of AI systems that can book flights, debug code, and build entire marketing plans autonomously.

Projects like Auto-GPT, BabyAGI, and a flood of similar tools didn't just appear out of nowhere. They represent the next logical leap in AI: the rise of the AI Agent.

So, what exactly is an AI agent, why is this happening now, and what is this "MCP Server" that acts as its brain? Let's break it down.

🤔 What's an AI Agent, Really?

An AI agent is more than just a chatbot. A chatbot is a conversational partner. An AI agent is an autonomous entity that takes action to achieve a goal.

Think of it like a very capable, very fast junior developer. You don't tell them exactly what to type. You give them a high-level task:

"Hey, find the top 5 open-source alternatives to Stripe, analyze their GitHub activity, and write a summary for me."

An AI Agent breaks this down. It has four key components:

🎯 Goal: The high-level objective it needs to accomplish.
🧠 Reasoning Engine: This is the "brain," almost always a powerful Large Language Model (LLM) like GPT-4 or Claude. It observes the current state, thinks, and decides on the next logical step.
🛠️ Tools: These are the agent's "hands." They are functions or APIs the agent can call to interact with the world. Examples include a web_search tool, a file_system tool to read/write files, or a terminal tool to execute commands.
💾 Memory: The ability to remember past actions, observations, and feedback. This is crucial for learning and avoiding loops.

The agent operates in a loop: think, act, observe, repeat—until the goal is complete.

🔥 Why Are Agents Booming Now? The Perfect Storm

The concept of agents isn't new, but we've just hit a technological tipping point. Four key factors created the perfect storm for the agent boom:

1. The Reasoning Power of Modern LLMs

This is the big one. Previous models were good at language, but GPT-4 and its contemporaries are incredible at reasoning and planning. You can give them a complex goal, a set of available tools, and they can generate a coherent, step-by-step plan. This was the missing "brain" component.

2. The API-ification of Everything

Agents need to do things. Today, almost every service has an API. Want to send an email? There's an API for that. Book a hotel? API. Query a database? API. This rich ecosystem of APIs provides the "tools" for agents to manipulate the digital world.

3. The Rise of Vector Databases

How does an agent remember what it learned in step 2 when it's on step 42? Storing raw text in a database is inefficient. Vector databases (like Pinecone, Weaviate, Chroma) allow agents to store information based on its semantic meaning. This gives them an effective long-term memory, so they can recall relevant context from past actions.

4. Open-Source Scaffolding (LangChain & LlamaIndex)

Frameworks like LangChain have done the heavy lifting. They provide the "plumbing" to connect LLMs, tools, and memory. Instead of building the entire agent loop from scratch, developers can now use these libraries to assemble powerful agents in a few lines of code, democratizing their creation.

🤖 The "MCP Server": The Master Control Program

This brings us to the core of the system. You might hear people refer to the orchestrator of an AI agent as the "MCP Server."

"MCP Server" isn't an official industry term. It's a conceptual name, and if you're a sci-fi fan, it's a direct nod to the Master Control Program from the movie TRON—the all-powerful AI that managed the system. It's a fitting name.

The MCP Server is the central process that runs the agent's main loop. It's the orchestrator that connects the brain, tools, and memory.

Here's what the MCP Server does:

State Management: It holds the agent's current state: the ultimate goal, the tasks completed so far, and the results of past actions.

LLM Coordination: It takes the current state and formats it into a prompt for the LLM. This is a critical step. The prompt usually looks something like this:

You are a helpful assistant.
Your goal is: [GOAL]
You have access to the following tools: [TOOL_LIST]

Here is the history of your work so far:
[HISTORY_OF_ACTIONS_AND_OBSERVATIONS]

Based on this, what is your next thought and action?
Respond in JSON format: {"thought": "...", "action": "tool_name", "args": {...}}

Tool Dispatching: The LLM responds with a JSON object, like {"thought": "I need to search for competitors.", "action": "web_search", "args": {"query": "Stripe alternatives"}}. The MCP Server parses this and calls the actual web_search() function with the provided arguments.
Memory Management: After a tool is used, the MCP Server takes the result (e.g., a list of search results) and saves it to the agent's memory (often a vector database) for future reference.
The Execution Loop: The server repeats this process—prompting the LLM, dispatching a tool, observing the result—until the LLM responds with a special "finish" action.

Here's a simplified pseudo-code of what the MCP Server is doing:

# The heart of the MCP Server
goal = "Find top Stripe alternatives and summarize."
memory = VectorMemory()
tools = [web_search, file_writer]

while not goal_is_complete():
    # 1. Prepare prompt for LLM
    prompt = create_prompt(goal, memory, tools)

    # 2. Get next step from the LLM "brain"
    response_json = llm.invoke(prompt) # e.g., {"action": "web_search", "args": ...}

    # 3. Dispatch the action to the correct tool
    action = response_json['action']
    args = response_json['args']
    observation = execute_tool(action, args) # The agent "acts"

    # 4. Save the result to memory
    memory.save(action, observation)

    # 5. Check if the LLM thinks it's done
    if action == "finish":
        break

Tying It All Together

So, how does it all relate?

The AI Agent boom is happening because powerful LLMs (the brain) can now use a vast ecosystem of APIs (the tools) and Vector Databases (the memory). The MCP Server is the conceptual name for the central orchestrator that runs the agent's loop, connecting all these pieces together to achieve a goal.

We are at the very beginning of this new paradigm. While today's agents can be brittle and expensive to run, they point to a future where we can automate incredibly complex digital workflows.

What are your thoughts? What kind of AI agent are you most excited to build or see in the wild? Let me know in the comments! 👇

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