What Is an AI Agent ?

The AI community is getting a shift in 2025 towards AI agents, which are designed to automate tasks just by taking a command as input. We're steadily moving towards an agentic framework, where multiple individual agents work together by communicating with each other. We have been following A2A and MCP in our recent blogs present here.

So, what exactly is an agent to begin with?

An agent is a software entity that can understand its environment, think about what it perceives, and then take actions to achieve a specific goal.

Let's use a simple example to understand this better. Imagine you're making pasta:

• To buy required ingredients is the primary task.
• Listing down the required ingredients means using your memory or context – in this case, the goal is to make pasta.
• Defining the quantity of each ingredient is planning.
• How to make the pasta with all the available ingredients involves reasoning.
• Utilizing pans, gas, and other equipment are the tools you use.
• Finally making pasta and serving it to the user is the task completion.

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What Exactly Is an AI Agent Today?

In simple terms, an AI agent today is usually an LLM-powered system that can:

• Understand what's going on: It captures its surroundings and the job it needs to do.
• Make decisions: It figures out the best way to move towards its goal.
• Do things using tools or other agents: This is where it gets interesting – it doesn't just think, it acts.

Imagine a tool like Cursor's agent mode, where you can describe a task and it autonomously explores your codebase, runs commands, finds context, and even applies fixes. These capabilities are pushing the limits of what an agent can do.

So, a straightforward way to think about it is:

"An AI agent is a system that can understand, plan, and act on its own to achieve goals, often by using a large language model (LLM) and other integrated tools."

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The Main Parts of a Modern AI Agent

To build these self-operating systems, you need a few key ingredients. If you're playing around with frameworks like LangChain, CrewAI, or working with tools like n8n, you'll recognize these building blocks:

• LLM (The Brain): This is the core intelligence. The large language model gives the agent its ability to understand, reason, and create human-like text, which is vital for planning and deciding what to do.
• Tools / Access to other systems: These are the agent's "hands." Whether it's a web browser, a way to look up database information, a code runner, or a connection to other online services via n8n workflows, these tools let the agent interact with the real world and get things done.
• Context: It helps the agent remember past actions, results and learned information so it can adjust its behavior over time.
• Goal / Task Planner: This part breaks down big goals into smaller, manageable steps and figures out the best order to do them in.
• Reason: Through different inputs (like documents, system prompts or past conversation), the agent understands its environment to make good decisions.

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How AI Agents Work Today: Step by Step

Let's simplify how an AI agent operates:

1. Understand: It starts by reading your instructions along with its current situation (like an email, a code file, or a document).
2. Plan: Using its thinking abilities (often with methods like ReAct or Tree of Thoughts enabled by frameworks like LangChain), it creates a plan. This might involve breaking down the problem, choosing which tools to use, and outlining the steps.
3. Act: It then carries out its plan by using its tools. This could mean searching the web for information, using an online service, running a piece of code, or interacting with a database using MCP.
4. Learn (Optional but Powerful): Based on what happened after its actions, the agent can update its memory, improve its understanding, or even change its future plans. This constant feedback loop is what makes agents truly independent and maintain cross conversation context like chatGPT.

An example in action: Imagine a research agent built with LangChain. You give it a topic. It understands what you want, plans its research (like "find articles," "summarize key points"), acts by browsing the web and processing information, and then creates a complete report with references.

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The Rise of Teams of AI Agents

This is where things get even more exciting. We're not just talking about one powerful agent anymore. The current trend is strongly moving towards Multi-Agent Systems (MAS), where several specialized agents work together to tackle incredibly complex tasks.

Think a team of human experts, but all powered by AI and communicating seamlessly. That's the idea. Agents can now talk to each other and coordinate, where Google is even taking it to the next level with their A2A (Agent-to-Agent) protocol. Frameworks like CrewAI are excellent examples of this, allowing you to define roles and goals for a team of agents.

A great example: Building a whole software application. You could have:

• A Research Agent gathering requirements and understanding the problem.
• A Coder Agent using tools like Cursor's agent mode to write the actual code.
• A Tester Agent thoroughly checking the code for errors.
• A Deployment Agent handling how the software gets released, possibly orchestrated via n8n workflows.

Platforms like CrewAI, LangGraph (part of LangChain), and n8n are leading the way in making these collaborative AI systems possible. It's truly changing how we'll build and use AI.

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New Ways Agents Communicate: Protocols and Standards

For these teams of agents to work well, they need a common language and standard ways to interact. Two important protocols are making a difference:

• A2A (Agent-to-Agent): This open system, promoted by Google, defines how AI agents find each other, agree on tasks, exchange messages, and work together across different systems. Think of it as the ultimate communication channel for your AI team.
• MCP (Model Context Protocol): This focuses on how an agent connects with external resources, giving them access to everything they need to think and act.

These protocols are essential for making sure agents can work together smoothly, share goals, and expand their capabilities. They're setting the stage for a truly connected AI future.

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Conclusion

AI agents are quickly moving from experimental tools to real-world collaborators. With the ability to understand tasks, plan intelligently, and take action using tools or other agents, they’re becoming a core part of how we build and automate in 2025. From single agents handling research or coding to multi-agent systems working together using standards like A2A and MCP, the shift toward agentic frameworks is already underway.

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Comments

[Anmol Baranwal]

This is a very broad concept imo and the architecture/concept slightly depends on the framework we use.

[HeetVekariya]

Yes, It also vary depending upon the use cases too.