Can your AI tools really work together, or are they still stuck in silos? With Google’s new Agent-to-Agent (A2A) protocol, the days of isolated AI agents are numbered. This emerging standard lets specialized agents communicate, delegate, and collaborate—unlocking a new era of modular, scalable AI systems. Here’s how A2A could transform your workflows, and why making it observable is just as important as making it possible.
To understand why A2A is such a breakthrough, it helps to look at how AI agents have evolved. Until now, most agents have relied on the Model Context Protocol (MCP), a mechanism that lets them enrich their responses by calling out to external tools, APIs, or functions in real time.
MCP has been a game-changer, connecting agents to everything from knowledge bases and analytics dashboards to external services like GitHub and Jira, giving them far more context than what’s stored in their training data..
However, MCP is still fundamentally a single-agent architecture: the agent enhances itself by calling tools.
Google’s A2A protocol takes things a step further. It introduces a standard for how multiple AI agents can discover, understand, and collaborate with one another—delegating parts of a query to the agent most capable of resolving it.
In a world where agents are being trained for niche domains (e.g., finance, healthcare, customer support, or DevOps), this multi-agent collaboration model could redefine how we build intelligent applications—modular, scalable, and highly specialized.
To appreciate why A2A is such a meaningful step, it helps to zoom out and see the broader trend across modern infrastructure:
Across DNS, CDN, cloud, and even AI, we've seen a shift from relying on a single provider to orchestrating multi-vendor ecosystems that optimize for performance, cost, reliability, and use-case fit.
This "multi" strategy is not just about risk management—it's about specialization and optimization.
Now, in the AI domain, we're witnessing the same pattern. While early adopters picked a single foundation model (e.g., GPT-4, Gemini, Claude), the next generation of intelligent systems will likely be multi-agent systems. One agent might be optimized for data interpretation, another for decision-making, and another for domain-specific compliance.
Google’s A2A protocol enables a framework where agents can collaborate dynamically. Think of this scenario:
A user asks: "What’s the weather in New York?"
Agent 1 receives the query but lacks access to real-time weather data. However, it knows (via the A2A protocol) that Agent 2 is specialized in live weather updates. It queries Agent 2, gets the accurate data, and serves it back to the user—seamlessly.
This interaction is powered by a few key concepts:
I tried implementing a basic A2A interaction locally using the open-source specification from Google. It’s remarkably modular and extensible—just like APIs revolutionized service-to-service communication, A2A may do the same for agent-to-agent orchestration.
Here’s a snapshot from my local implementation:
The remote agent listens on port 8001, ready to receive tasks. It advertises its capabilities via an Agent Card and executes incoming requests accordingly.
The host agent first discovers the remote agent, retrieves its capabilities, and sends a query prompt to the appropriate endpoint defined in the Agent Card. It then receives and returns the final response.
Multi-agent AI systems bring powerful new capabilities—but also new risks. In traditional architectures, observability stops at the edge of your stack. But in an A2A world, a single user request might pass through a chain of agents—each running on different systems, owned by different teams, and dependent on different APIs.
Every agent interaction is essentially a service call. That means:
Take a chatbot for a ticket booking app. It may rely on internal microservices for availability and payments, but call out to a weather agent or flight-status agent using A2A. If one of those agents is slow or unresponsive, the whole experience degrades. And it’s hard to fix what you can’t see.
This is where visibility matters. By mapping your service and agent dependencies—internal and external—you can:
Tools like Catchpoint’s Internet Stack Map help teams visualize these flows. It leverages Internet Performance Monitoring (IPM) to illustrate how requests flow through internal components and out to external agent APIs, making it clear where dependencies exist and where issues could arise.
Just as we evolved from single-CDN to multi-CDN, or from monolithic apps to microservices, we are now entering an age of multi-agent intelligence. And just like we learned to monitor those distributed system, we’ll now need to monitor multi-agent systems with the same rigor.
Because the future isn’t just AI—it’s AI working together. Modular. distributed, collaborative. And IPM is what makes that visibility possible.
Learn more
Can your AI tools really work together, or are they still stuck in silos? With Google’s new Agent-to-Agent (A2A) protocol, the days of isolated AI agents are numbered. This emerging standard lets specialized agents communicate, delegate, and collaborate—unlocking a new era of modular, scalable AI systems. Here’s how A2A could transform your workflows, and why making it observable is just as important as making it possible.
To understand why A2A is such a breakthrough, it helps to look at how AI agents have evolved. Until now, most agents have relied on the Model Context Protocol (MCP), a mechanism that lets them enrich their responses by calling out to external tools, APIs, or functions in real time.
MCP has been a game-changer, connecting agents to everything from knowledge bases and analytics dashboards to external services like GitHub and Jira, giving them far more context than what’s stored in their training data..
However, MCP is still fundamentally a single-agent architecture: the agent enhances itself by calling tools.
Google’s A2A protocol takes things a step further. It introduces a standard for how multiple AI agents can discover, understand, and collaborate with one another—delegating parts of a query to the agent most capable of resolving it.
In a world where agents are being trained for niche domains (e.g., finance, healthcare, customer support, or DevOps), this multi-agent collaboration model could redefine how we build intelligent applications—modular, scalable, and highly specialized.
To appreciate why A2A is such a meaningful step, it helps to zoom out and see the broader trend across modern infrastructure:
Across DNS, CDN, cloud, and even AI, we've seen a shift from relying on a single provider to orchestrating multi-vendor ecosystems that optimize for performance, cost, reliability, and use-case fit.
This "multi" strategy is not just about risk management—it's about specialization and optimization.
Now, in the AI domain, we're witnessing the same pattern. While early adopters picked a single foundation model (e.g., GPT-4, Gemini, Claude), the next generation of intelligent systems will likely be multi-agent systems. One agent might be optimized for data interpretation, another for decision-making, and another for domain-specific compliance.
Google’s A2A protocol enables a framework where agents can collaborate dynamically. Think of this scenario:
A user asks: "What’s the weather in New York?"
Agent 1 receives the query but lacks access to real-time weather data. However, it knows (via the A2A protocol) that Agent 2 is specialized in live weather updates. It queries Agent 2, gets the accurate data, and serves it back to the user—seamlessly.
This interaction is powered by a few key concepts:
I tried implementing a basic A2A interaction locally using the open-source specification from Google. It’s remarkably modular and extensible—just like APIs revolutionized service-to-service communication, A2A may do the same for agent-to-agent orchestration.
Here’s a snapshot from my local implementation:
The remote agent listens on port 8001, ready to receive tasks. It advertises its capabilities via an Agent Card and executes incoming requests accordingly.
The host agent first discovers the remote agent, retrieves its capabilities, and sends a query prompt to the appropriate endpoint defined in the Agent Card. It then receives and returns the final response.
Multi-agent AI systems bring powerful new capabilities—but also new risks. In traditional architectures, observability stops at the edge of your stack. But in an A2A world, a single user request might pass through a chain of agents—each running on different systems, owned by different teams, and dependent on different APIs.
Every agent interaction is essentially a service call. That means:
Take a chatbot for a ticket booking app. It may rely on internal microservices for availability and payments, but call out to a weather agent or flight-status agent using A2A. If one of those agents is slow or unresponsive, the whole experience degrades. And it’s hard to fix what you can’t see.
This is where visibility matters. By mapping your service and agent dependencies—internal and external—you can:
Tools like Catchpoint’s Internet Stack Map help teams visualize these flows. It leverages Internet Performance Monitoring (IPM) to illustrate how requests flow through internal components and out to external agent APIs, making it clear where dependencies exist and where issues could arise.
Just as we evolved from single-CDN to multi-CDN, or from monolithic apps to microservices, we are now entering an age of multi-agent intelligence. And just like we learned to monitor those distributed system, we’ll now need to monitor multi-agent systems with the same rigor.
Because the future isn’t just AI—it’s AI working together. Modular. distributed, collaborative. And IPM is what makes that visibility possible.
Learn more
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