Make Your Agentic Applications More Powerful With MCP (Model Context Protocol)

The rapid evolution of artificial intelligence has led to the development of increasingly sophisticated AI agents capable of performing complex tasks. These agents, particularly those leveraging the power of Large Language Models (LLMs), often require access to information and tools beyond their inherent training data to function effectively . For instance, an AI agent designed to schedule meetings needs to interact with calendar applications, email services, and potentially other productivity tools. Without standardized mechanisms for such interactions, the process of integrating these agents with diverse data sources and functionalities becomes intricate, time-consuming, and difficult to scale . The inherent limitations of LLMs, such as their knowledge cutoffs and the finite size of their context windows, further underscore the necessity for dynamic methods to augment their knowledge and capabilities.

A Gentle Intro to AI Agents

An AI agent can be understood as a computer program that can perform tasks or make decisions on its own, based on its goals and the information it receives.

An AI agent will primarily have three main components - Perception, Brain and Action.

These three components work together: the perception gathers information, the brain figures out what to do, and the action carries out the plan. This process allows AI agents to perform a wide variety of tasks, from answering questions to controlling complex systems.

The diagram illustrates the general workflow of an AI agent, which typically involves two core components: intra-execution and interaction.

The intra-execution component focuses on the processes within a single AI agent and comprises three main modules: perception, brain, and action, as dicussed above. The workflow begins with user input that goes through an input formatter tool as part of the perception module.?

The input is then embedded, which is followed by reasoning and planning performed by the brain, which is where an LLM is used to analyse and deduce information and to devise strategies for specific objectives.?

This results in subtasks, which are then executed by the action module, which may include calling external tools. The process iterates, moving between the brain and the action modules, until a final outcome is reached. The agent also interacts with its memory and environment.?

Let's take a real-life example to see how AI agents work. Let's say you need Nike shoes for running in under $500 and the color should be Blue. The AI agent would break down the user's request for Nike running shoes under $500 in blue into structured data. It would then use reasoning and planning to identify relevant shoe models, gather details, and evaluate options against the criteria. The agent would execute subtasks like querying e-commerce APIs and reviewing customer feedback, iterating between its brain and action modules until recommending the optimal shoe. Throughout, the agent would leverage its memory and environment to fulfill the user's requirements efficiently.

Here is my complete guide on building AI agents using LangChain!

Agentic Workflows

Put AI agents to work with agentic workflows!

Agentic workflows are designed to autonomously execute a series of tasks through specialized AI agents that operate without constant human oversight. These workflows manage tasks by organizing and assigning work to various agents, ensuring that each task is handled by the component best suited to perform it. This decentralized task management enables the system to make decisions dynamically—AI agents analyze data, adjust processes, and even learn from feedback over time to enhance performance.

The benefits of this approach are significant. Automation streamlines tasks from data entry to complex decision-making, reducing human error and freeing up resources for more strategic activities. Optimization comes from the system’s ability to refine and improve task sequences over time, leading to better efficiency. Scalability is another critical advantage, as the modular design of agentic workflows allows them to handle large-scale processes by simply adding more agents or integrating new tools.

These characteristics make agentic workflows highly valuable across a variety of applications.

Understand How to Build Efficient Agentic Systems.?

Also, here is how you can build Agentic systems & LLM applications the right way.?

Here is my step-by-step guide on building agentic workflows using LangGraph.?

Let’s Understand Multi-Agent Systems (MAS)

Multi-Agent Applications are revolutionizing the way we approach complex problem-solving. These applications utilize multiple intelligent agents that collaborate to tackle tasks that require diverse expertise. The orchestration of these agents allows for the division of labor, where each agent specializes in a specific aspect of the task. As organizations increasingly seek to automate complex tasks, the demand for Multi-Agent Applications continues to rise. Their ability to streamline workflows and improve productivity makes them a valuable asset in various fields, from software development to data analysis.

Multi-Agent System (MAS) is defined by a group of agents, also referred to as a Crew.?

Each possesses unique skills and specialized capabilities. These agents collaborate to solve simple tasks in order to achieve a bigger and more complex common goal.

These agents communicate with one another by sending the output of their tasks to subsequent agents to build upon.

The structure of a Crew can be categorized into three main types based on the interaction between agents:

? Sequential: The agents work in a chain, where one agent’s output is the next’s input. By solving smaller tasks, they can solve the bigger and more complex objective for which the Crew was designed.

? Hierarchical: It usually consists of a manager and multiple subordinates, and the role of the leader is to delegate, plan, and manage the completion of tasks. The subordinates execute the leader’s instructions. In this scenario, we can have agents performing tasks simultaneously since not every agent has a sequential dependency.

? Hybrid: This structure has Sequential and Hierarchical environments within the same Crew. It typically happens when some agents, with complex tasks at hand, break them down into smaller ones and build a sub-crew with new agents. They become the leader of that sub-crew and, at the same time, a subordinate of the original Crew.

Here is my practical guide on building multi-agent systems.?

Here is my hands-on guide to building multi-agent systems using CrewAI in 10 minutes.?

Know more about multi-agent systems in this article.?

Building Multi-Agent Systems

Like to see how two AI agents engage in a heated debate?

Multi AI Agent systems represent a significant advancement in the field of AI.?

Well, the future is all about multi AI agent systems.?

These systems consist of various autonomous AI agents that collaborate to accomplish specific tasks or solve complex problems. Unlike single AI agents, which operate independently, Multi AI Agents work collectively, leveraging their unique capabilities to enhance performance and efficiency.

Multi-Agent RAG systems utilize multiple specialized agents, each with its own knowledge base. The user prompt is processed sequentially by these agents, with each one contributing its specific expertise.

In my recent tutorial, you can see how two agents, one is Elon Musk's fan and the other one is Sam Altman's fan and they both engage in a heated debate and share their views on who is better. The third agent, that is the judge agent, enters the conversation to finally announce the winner.?

Interesting, right??

Learn how to build a multi agent AI systems in my step-by-step guide.?

The tutorial is inspired by Aniket Hingane and this is the article you can refer to know more.?

Multi-Agent System Layers & Tools

At the core of each AI agent is an LLM wrapped around a specialized app or function to handle natural language queries and analyze data. Agents interpret user requests, break them into subtasks and assign them to other specialized agents. They communicate continuously within the multi-agent framework, ensuring tasks are completed efficiently and effectively.

How do they differ from workflows then?

A multi-agent system comprises multiple autonomous agents that interact, collaborate, or coordinate to accomplish tasks, solve problems, or achieve specific goals. These agents can operate independently, adapt dynamically, and make decentralized decisions.

In contrast, workflows represent a structured sequence of tasks or processes designed to achieve a predefined outcome. Unlike multi-agent systems, workflows typically follow a linear or predefined path with limited adaptability and autonomy.

What are the different tools & frameworks you can use to build multi-agent systems?

- crewAI

- AutoGen

- LangGraph

- Agno (Phidata)

- OpenAI Swarm?

- Pydantic AI & etc

The workflow image below shows a multi-agent appointment booking system for doctors. You can easily see how multiple agents can collaborate efficiently to handle scheduling, cancellations, and patient interactions seamlessly.

Learn more about multi-agent AI systems from my guides below.?

Here is my guide on building multi-agent systems using crewAI.?

Enter Model Context Protocol (MCP)

MCP, which started as a project by Anthropic, is now the talk of the town.?

Even though it was introduced a few months ago, developers have gradually begun to understand its importance.

It's a protocol that’s trying to standardize how LLMs access external data and tools.?

So why is everyone talking about it suddenly??

MCP simplifies the lives of developers by providing a standardized protocol for integrating AI agents with external tools and data sources.

It promotes interoperability, reduces the need for custom integrations, and streamlines AI application development. MCP acts as a universal adapter, enabling LLMs to access real-world data and perform actions in a consistent and scalable manner.?

MCP offers benefits such as enhanced context awareness, streamlined development, and improved security, making it a valuable tool for AI tool integration. The goal is for MCP to be the USB-C of AI, allowing for standardised AI model interactions. MCP fosters an ecosystem of reusable connectors, allowing developers to build once and reuse them across multiple LLMs and clients, eliminating the need to rewrite the same integration in numerous ways. This unified data access means that with MCP, one protocol is configured, and then the LLM can "see" all registered connectors.?

For example, now MCP servers let you connect Claude to powerful tools like GitHub, Slack, and Google Maps. These integrations help you save time, streamline workflows, and focus on what matters most. Forget RAG, MCP is poised to significantly benefit AI agents and agentic applications by enabling more autonomous, context-aware, and efficient workflows.

Know more about MCP.?

Core Components of MCP

Image Credits: Digital Ocean

The Model Context Protocol operates based on a client-server architecture, which involves distinct roles for different components: MCP Hosts, MCP Clients, and MCP Servers . MCP Hosts are the AI applications themselves or the interfaces through which users interact with these applications, such as Anthropic's Claude Desktop or various AI-driven Integrated Development Environments (IDEs) . These hosts are the entities that require access to external data or tools to fulfill user requests or perform specific tasks.?

MCP Clients are protocol-specific clients that establish and maintain a dedicated, one-to-one connection with MCP Servers . Acting as intermediaries, these clients are responsible for forwarding requests originating from the MCP Host to the appropriate MCP Server and then relaying the server's response back to the host. This separation of concerns allows the AI application to focus on its core logic while the client handles the intricacies of the MCP communication protocol.?

MCP Servers are lightweight, independent programs that expose specific capabilities or data sources through the standardized MCP interface . These servers act as the gateway to various local or remote data sources, which can include file systems, databases, Application Programming Interfaces (APIs) of external services, and more . Each MCP Server advertises the specific tools, resources, and prompts it offers, allowing MCP Clients to discover and utilize these functionalities.

MCP in the Landscape of AI Integration: Comparison with Existing Approaches

Powering the Infrastructure for Agentic Applications!

Agentic applications rely on a robust infrastructure encompassing significant computational power, often utilizing GPUs and TPUs, and scalable data storage solutions like vector databases such as SingleStore for efficient information retrieval. High-bandwidth, low-latency networking is also crucial . Tools and platforms supporting this include LLM management services from major cloud providers like Google Cloud AI and Azure AI, orchestration frameworks such as LangChain and Kubernetes for managing complex workflows, and monitoring tools for performance tracking.

AdaniConneX provides data center infrastructure with a focus on scalability, reliability, and sustainability, offering a potential platform for deploying the demanding resources required by agentic applications.

The strategic locations of AdaniConneX's data centers in emerging IT hubs across India also offer proximity to talent pools and potential users of Agentic Applications.

I really liked AdaniConneX's ‘What You Rely On, Relies On Us’ campaign.?

Imagine asking your AI assistant to schedule your day, only to hear silence. Picture opening your favorite app that uses AI to personalize recommendations—and finding it unresponsive.

No AI helping you draft that important email. No smart assistant ordering your groceries. No personalized health insights analyzing your sleep patterns. No intelligent navigation system rerouting you around traffic. Sounds frustrating, doesn't it?

Today's agentic AI applications work tirelessly behind the scenes, handling complex tasks with seemingly human-like understanding. But have you ever wondered what powers these digital marvels? Behind every smart response, personalized recommendation, and autonomous decision is a sophisticated network of high-performance data centers—the true enablers of the AI revolution. State-of-the-art data centers oF AdaniConneX provide the computing power, reliability, and security needed for today's most demanding AI applications to function seamlessly.