Apr 14, 2025
AI Agent Technologies Compared: AFC, A2A, MCP
AI moves quickly, and if you're building agents, you've likely run into three technologies that keep popping up: Agent Function Calling (AFC), Agent2Agent (A2A), and Model Context Protocol (MCP). Each one does something different, and picking the right one can save you from a lot of frustration down the road.
This guide covers what these technologies are, how they evolved, where they're being used, what they're best at, and how they actually work under the hood.
Introduction to the Technologies
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Agent Function Calling (AFC): This is the basic way agents call specific functions or APIs to get things done. It's straightforward but not very flexible when you need agents to work together on complex tasks.
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Agent2Agent (A2A): Google built this protocol so agents can talk to each other directly. The goal is simple: make it easy for different agents to collaborate, even if they come from different systems.
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Model Context Protocol (MCP): Anthropic created this as a standard way for agents to work with external tools and keep track of context. It's especially useful when you're dealing with complicated tasks that need multiple tools working together.
Development Timelines
Here's where each technology stands right now:
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AFC: This is the oldest and most widely used. It's stable and reliable, but it wasn't designed for agents to collaborate dynamically, so it has some real limits there.
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A2A: Google announced this in April 2025 and released it as open source. The plan is to have it production-ready later in 2025. Big companies are already on board, which is a good sign.
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MCP: Anthropic introduced this in 2023. It's already in production with a solid ecosystem behind it, and enterprises are actively adopting it.
Geographical Impact
These technologies don't affect every region the same way:
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AFC: Works well for regional data analysis and customization. Companies around the world use it across many industries.
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A2A: Makes it easier for teams in different regions to work together. That's a big deal for global industries like supply chains and healthcare, where coordination across borders is essential.
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MCP: Helps solve some of the geopolitical challenges around data and tool access by standardizing how agents interact. It also supports applications that need to work with multiple languages.
Practical Applications
Here's where these technologies show up in the real world:
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AFC: Great for customer service chatbots, healthcare diagnostic tools, and smart home systems. It handles single tasks well.
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A2A: Useful for connecting enterprise systems, helping research teams collaborate, and automating workflows that span multiple tools or departments.
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MCP: Powers applications that need to process a lot of data, assists with content creation, and manages energy use in smart homes by keeping context consistent across interactions.
Competing Technologies: A Comparative Analysis
Each technology has clear strengths and weaknesses:
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AFC: Simple to set up and good for discrete, single tasks. The downside is that it doesn't scale well when you need agents working together.
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A2A: Excels at dynamic, multi-agent systems. The industry support behind it is strong, which means you're less likely to hit dead ends.
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MCP: Provides standardized tool integration, which is crucial when you're building complex systems that need to stay predictable as they grow.
Inventor Background
The people behind these technologies shaped what they can do:
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AFC: Individual developers like Tianyi Li and Yadd Paddalwar contributed to its development and adoption.
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A2A: Google led the effort with help from major companies like JetBrains and Salesforce.
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MCP: Anthropic built this to solve context-handling problems in AI models, and it shows in how well it maintains state across interactions.
Operational Mechanics
Here's how each technology works under the hood:
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AFC: Executes functions without keeping state between calls. That makes it fast and simple, but it means you can't easily chain complex operations together.
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A2A: Manages state dynamically as agents collaborate. It includes error handling so the system can recover when something goes wrong mid-workflow.
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MCP: Integrates tools in a predictable way, maintaining context throughout so agents don't lose track of what they're working on.
Conclusion
These three technologies each solve different problems in AI development. AFC is a solid choice when you need something reliable for straightforward tasks. A2A and MCP give you more power when you're building complex, collaborative systems. As AI continues to advance, these protocols will shape how agents work together and handle increasingly sophisticated challenges.
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