Article June 4, 2025

by Gabriel Juarez, Salesforce Tech Lead and Micaela Vidart, Technical Support Representative

Understanding agent-to-agent (A2A) communication

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Service

Platform optimization Salesforce

What is agent-to-agent (A2A) communication?

In simple terms, agent-to-agent (A2A) communication is the communication system that allows autonomous AI agents to interact seamlessly. On the other hand, sources like Wallarm and Lisowski refer to A2A Protocol as a cross-platform specification for enabling AI agents to interact with each other and complete tasks.

In other words, we can describe A2A communication as a universal meeting room where every AI is invited and speaks the same structured language. Just like humans use languages (English, Italian, Spanish), AI agents communicate using A2A protocol as their common language to work together like a well-coordinated team.

A2A communication induces a significant shift in organizational operations by collaborating and delegating tasks across heterogeneous systems. Now, companies can enhance workflows, reduce manpower dependency, and improve decision-making capabilities.

How does A2A communication work?

Let’s imagine a company related to the distribution of printed and digital study material has a high demand for support and order tracking. Due to the massive volume of items sold and the seasonal load, managing customer inquiries fast is humanly impossible. As a customer-centric company, customer experience is a top priority, and it needs to be easy for clients to contact support.

Here's where AI agents and A2A communication turn up: they need to process different types of requests, understand the sentiment, and generate personalized responses. Internally, AI agents reroute the questions to domain-specific agents or involve humans in the loop. Different LLMs are used for specific tasks, like summarizing the content of long texts or documents and later locating references of order numbers. These AI agents not only provide quick responses, but they also enrich cases with information so that employees have more decision-driving data in complex scenarios.

Let’s see a few additional use cases:

Imagine an online shopping portal. A customer's order placement on the portal triggers automatic communication between the portal’s software and that of managing inventory. The silent exchange under review ensures apt order processing and inventory revisions, demonstrating a prime example of A2A interaction.

Another use case comes when we picture two applications—let’s call them App A and App B. App A possesses a specific feature that App B requires. The need to construct this feature anew in App B is circumvented through APIs; by utilizing the API from App A, App B can gain access to the desired feature, enabling the A2A interaction.

What’s the difference between A2A communication and human-to-machine communication?

A2A communication diverges from standard, human-to-machine exchanges. In the latter, a human user interacts with software to carry out a specific task, like document editing or internet browsing. Contrarily, agent-to-agent hinges on direct software-to-software conversations to execute tasks, with no human involvement required. The resulting workflow speeds up task completion, lowering the probability of human error in the process.

It’s clear that A2A communication has emerged as a cornerstone in present-day computing systems. It earned its spot through enabling various software platforms to interact in a seamless, integrated fashion, making significant contributions to a wide range of digital systems.

Here’s a summary.

Feature	A2A Communication	Human-to-machine communication
Participants	Autonomous AI agents	Human user and a machine/AI system
Interaction model	Fully automated: peer-to-peer, agent collaboration	Requires human interaction: client-server or user-agent interaction
Purpose	AI agents collaborate on complex tasks	Human directs or queries the machine
Interface	APIs, system calls, data protocols	UI/UX, voice input, keyboard, etc.
Communication style	Conversational, iterative, adaptive	Command-response, often single-turn
Protocols/standards	Standardized protocols like JSON-RPC 2.0	Varies, often proprietary or UI-based
Use cases	System integrations, data sync	Chatbots, command interfaces, voice assistants
Examples	Multiple AI agents coordinating workflows; SAP ↔ Salesforce	Human uses a chatbot or voice assistant; User asks Siri to play music

Introducing Salesforce Agentforce

What is Salesforce Agentforce?

Salesforce Agentforce is a set of AI-powered productivity tools incorporated into the Salesforce ecosystem. This proactive, autonomous AI application provides specialized, always-on support to employees or customers through tailored agents. The platform enables AI agents to reply to customer inquiries or requests, execute complex tasks, integrate seamlessly with your marketing systems, and collaborate with other AI agents.

Learn more about Agentforce for marketers

What are the benefits of using Agentforce?

Improve work processes

Lower operational costs

Deliver quick and personalized responses

Identify and prioritize leads

Create content

Refine copies before going live

How Salesforce enables agent-to-agent communication

Agentforce is a top leader in streamlining operations with AI agents across organizations, whether it’s for marketing, sales, customer service, or beyond. Plus, it integrates seamlessly with applications within the Salesforce ecosystem. But enterprise platforms have become more complex, and more often than not they need to interact with third-party apps and external services like market analysis tools or payment systems.

From low code to pro code, Salesforce Platform provides the appropriate tools for each use case, whether simple or advanced. Flow Builder will become your best partner for orchestrating communications among agents and even talking to external systems. These invocations can move in both directions, either by enriching agents’ chat capabilities or by leveraging GenAI power from your existing flows.

A great way to make agents talk to each other and distribute the efforts is through flows. In one example, an agent is asked to provide information about the latest touchpoints with a customer. As this agent is not the specialist, it will invoke a flow to reroute the question to one or multiple dedicated agents. This way you can chain many steps and transform an inconvenient or dead-end task into a collaborative effort among autonomous agents.