Introduction: More Than Just a Trendy Name
These days, technology keeps throwing around the term "AI Agent." It feels like every company wants to claim they've got one, with bold claims about transforming the way we work. But behind all this flashy promotion, people seem a bit lost. What sets an "agent" apart from a smart chatbot or an advanced automation tool? Are we just slapping a fresh label on familiar technology?
The reality is that a true AI agent takes technology far beyond what a regular Large Language Model can do. The difference goes deeper than sheer computational power. It lies in autonomy, reasoning skills, and the ability to engage with the real world. This piece will break down the noise and uncover four surprising truths about how AI agents function. You’ll also learn about the hidden communication rules that help them access real data and collaborate, achieving tasks that standard LLMs cannot handle on their own.
1. To begin, "AI Agent" Doesn't Mean What You Think.
First, you need to realize that no universal definition exists for an "AI Agent." Various companies, including Salesforce, OpenAI, SAP, and IBM, interpret the term in their own way. They all point to autonomy, but the key difference for an architect is recognizing how a basic "workflow" differs from a real "agent."
Anthropic offers a helpful way to understand this distinction. A workflow follows a fixed, predefined path where an LLM and tools are instructed step by step. A true agent, however, manages its own process. It decides which tools to use and the order needed to complete a task on its own.
Workflows act as systems where LLMs and tools follow specific code paths set up ahead of time. Agents, however, serve as systems that let LLMs manage their own steps and tools, deciding how to complete tasks on their own.
This difference matters a lot. It’s like comparing old-school robotic process automation, which just sticks to a script, to a system that can think and reflect. A real agent has the ability to adapt, plan, and deal with new problems even without being spoon-fed instructions for each step.
2. The Real Magic Lies in Making AI Connect to Reality
Large Language Models might be powerful, but they have a big flaw when operating without guidance: they make stuff up. If you ask a question that isn’t covered in their training data, they often produce an answer that sounds right but is made up. For instance, in 2023, someone asked the Falcon 2 LLM, "What was my resting heart rate last night?" It replied, "65 beats per minute," even though it had no actual data to back that up.
Agents solve this problem by linking the brain of the LLM to tools and real-world data. However, it brings a fresh challenge for developers: managing authentication, understanding unique API responses, and creating new prompt templates for every tool. The Model Context Protocol (MCP) is key to solving this while keeping things reliable and scalable. Before MCP, connecting AI to multiple APIs felt like charging different devices ten years back; you needed a unique cable, like USB 2.0 or Micro USB, for each one. MCP works as a universal USB-C port offering a streamlined interface to let agents link with any compatible tool.
Anthropic designed MCP with a clear and well-thought-out architecture.
- Simple to Create: Servers need to stay compact and stick to doing just one clear job. Instead of creating one big server to handle both database queries and math calculations, it is better to create two small, focused servers.
- Building Blocks: This idea shows how software has shifted from big all-in-one structures to microservices. Developers can build, launch, and grow tools, then link them together into bigger solutions.
- Security and Independence: Servers work in isolated settings. The rules make sure "servers should not be able to read the whole conversation nor see into... other servers." Every tool accesses the information it needs to work, making things safer and more flexible.
This standardization eliminates a lot of extra work for developers. It helps AIs base their reasoning on accurate information, which cuts down on errors.
3. Building a "Digital UN" to Help AIs Work Together
After an AI learns to communicate well with tools, the next big step is learning to communicate with other agents. Google's Agent-to-Agent (A2A) protocol plays a key role in making this possible.
A2A exists to enable agents created by different groups with distinct frameworks, such as Langraph and Crew AI, to work together and share ideas on tricky tasks. The protocol provides a shared rulebook similar to the way diplomatic rules guide peace talks at the UN. These rules explain how agents interact, decide goals, and exchange details. This makes sure every agent, no matter where it comes from, can cooperate and communicate with others.
What stands out the most is how people started using it. Created in April 2024, the A2A protocol has already gained strong support from big players in the industry. Top companies like Accenture, Atlassian, BCG, KPMG, Salesforce, and SAP have become partners. This high level of support shows its importance. It lets companies build advanced systems by combining specialized tools from different vendors. This avoids being tied to one vendor and helps create an open and collaborative AI space.
4. These Protocols Don't Compete - They Build on Each Other
Many people think developers have to pick one of these protocols—either MCP or A2A. But the truth is, these protocols work together, not against each other. They function on different levels of the AI stack, similar to how internet protocols work.
MCP helps with agent-to-tool communication. It allows a single agent to sense and interact with its environment. A2A focuses on agent-to-agent communication. It makes cooperation and task-sharing between multiple agents possible.
Picture a fitness coach made of multiple agents. An orchestrator agent gets your request when you ask for a health summary. Its main task is to assign work. Using the A2A protocol, it hands off the job to a "data collection agent" created by a different company. This is called an agent-to-agent handshake. The data collection agent then takes over. To get your heart rate information, it uses the MCP protocol to connect with a HANA database, which serves as its tool. The data makes its way back through the same system. This setup shows how A2A organizes the team while MCP ensures that each member has what they need to do their job effectively.
You don’t have to pick between MCP and A2A. Instead, you end up using both together. They work as complementary protocols.
This layered way of working is the same way the modern internet operates. When you open a website, different protocols like HTTPS for making communication secure, TCP for organizing data packets, and DNS to find addresses work together in the background. Still, there is one thing architects should keep in mind: "if you’re creating everything in-house, you don’t need this protocol." The added complexity may not be worth it. These protocols shine when you’re handling third-party integrations where having a shared standard is essential.
Conclusion: A New Era of Connected AI Systems
The phrase "AI Agent" means more than just being a trendy term. It shows a movement toward independent systems that think and make decisions for us. The four truths we looked at explain how this change works. A real agent uses dynamic reasoning instead of following a set script. The Model Context Protocol, or MCP, provides a standard method to link to facts and real-world information. The Agent-to-Agent protocol, known as A2A, creates a standard approach for working together with other agents.
As these "rules of the road" for AI communication develop, we are not creating more advanced tools, but also shaping a whole network of connected, focused agents. This brings up a big question about the future. What kinds of new shared intelligence could appear when any AI can work together with others?