Summarize

AI agents work well alone but fail to collaborate, slowing enterprise workflows. Google’s new Agent2Agent (A2A) Protocol fixes this by enabling real-time, vendor-neutral communication between agents, creating coordinated digital workforces that execute faster, reduce risk, and scale more efficiently.

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A sales agent closes a deal. The win is there, but the process stalls when procurement needs to step in. The sales agent sends it across, yet nothing moves. The procurement agent works with different systems, different logic, and different workflows. The two cannot connect. Days are lost.

This is the silent failure of enterprise AI.

We have trained agents to perform tasks with precision, but not to work together.

That is starting to change.

In April 2025, Google Cloud announced the Agent2Agent (A2A) Protocol — an open standard that enables real-time communication between agents, no matter who built them. With backing from more than 50 partners including Atlassian, SAP, Salesforce, and Accenture, it signals a new era of enterprise automation.

Google Just Opened the Gate: Introducing Agent2Agent (A2A)

In April 2025, Google Cloud unveiled the Agent2Agent (A2A) Protocol — an open, vendor-neutral standard designed to let AI agents communicate with each other in real time.

More than 50 industry leaders, including Salesforce, Atlassian, SAP, Accenture, and LangChain, have already thrown their weight behind it. The goal is clear: remove one of the biggest roadblocks in enterprise AI by making inter-agent interoperability possible.

Before A2A: Agents Worked in Silos

Enterprise adoption of AI agents has grown quickly. McKinsey reports that 60% of large enterprises now use generative AI tools in operations or customer service.

Despite this growth, most agents cannot collaborate effectively. The main reasons are:

Siloed deployment: Agents are introduced by different departments with little coordination.
Framework fragmentation: They are built on varied LLMs, APIs, and logic flows.
Lack of a common language: For example, a finance agent cannot request verification from a compliance agent.

These limitations lead to:

Missed task handoffs
Increased manual interventions
Loss of contextual information
Reduced confidence in automation outcomes

Research by Deloitte supports this issue. It found that 68% of enterprise leaders view the lack of interoperability between AI systems as the biggest barrier to scaling AI, ranking above model performance and computing power.

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After A2A: Agents Can Coordinate in Real Time

The Agent2Agent (A2A) Protocol addresses interoperability by introducing a vendor-agnostic communication layer. This layer makes it possible for agents to:

Discovery: Locate and identify other agents within the ecosystem.
State-sharing: Exchange variables, outputs, or decisions to maintain context.
Task chaining: Trigger and manage workflows across multiple agents.
Trust mechanisms: Verify roles and apply scoped permissions for secure collaboration.

A2A functions much like TCP/IP did for the internet. Regardless of who developed them, agents can now communicate, delegate responsibilities, and coordinate effectively.

Google Cloud describes A2A as “a leap toward composable agent ecosystems.” In practice, it transforms individual agents into cooperative teams.

What Happens When You Add Synoptix AI to A2A?

A2A is a protocol, not a product.

You still need a platform to build, orchestrate, observe, and secure your agents. That’s where Synoptix AI makes the difference.

Synoptix is a Microsoft-certified enterprise AI platform that now supports Agent2Agent-compliant agents. It doesn’t just run agents. It coordinates them.

Key capabilities Synoptix AI brings to A2A:

No-code Agent Builder: Define logic, goals, roles, and toolkits for each agent
Real-Time Messaging Layer: Agents communicate live with zero-latency switching
Evaluations + Tracing: Know who said what, when, and why—in milliseconds
SynoGuard: Role-based access control, encryption, audit trails for every agent interaction
Compliance by Design: Built-in checkpoints for risk, legal, or policy agents to intervene

This is where the magic happens. It’s not just AI doing tasks. It’s agents coordinating in real-time across departments, tools, and compliance boundaries—as one digital workforce.

Use Case: Finance Agent → Compliance Agent → Audit Agent

Here’s a real-world example.

Scenario: Your finance department is processing vendor payments. Every transaction must be checked for compliance (sanctions, internal policies) and tagged for quarterly audit review.

In traditional setups:

The finance agent processes data.
A manual handoff is required to compliance.
Another team later extracts logs for audit.
That’s three systems, three delays, and zero agent collaboration.

With Synoptix + A2A:

Finance Agent flags a high-risk payment.
Compliance Agent is auto alerted, checks AML databases.
Approval triggers an event that logs to Audit Agent.
Audit Agent packages a traceable report in real-time.

No humans routed it. No context was lost. No time was wasted.

That’s agentic orchestration in action.

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Why This Matters to You

If you’re a CIO, CTO, or COO, here’s why this changes the conversation.

Faster execution: Inter-agent communication slashes handoff time by up to 90%
Risk containment: Built-in tracing + compliance means no more guesswork during audits
Lower cost-to-scale: Instead of reinventing logic per agent, reuse behaviour modules
Better governance: Role-based control over who can talk to who—and about what

IBM found that organizations using multi-agent AI reduce administrative overhead by 25% within 12 months. But the missing link was inter-agent trust. A2A now makes that scalable.

Where This Is Going

The future of enterprise AI is not about isolated task automation. It is about creating agent ecosystems. These ecosystems can:

Coordinate across different platforms
Monitor and optimise their own performance
Adapt to evolving business goals
Intervene when risks or exceptions arise

With Synoptix AI, enterprises move beyond deploying individual agents. They gain a managed network of agents that collaborate, share context, and improve outcomes together.

In practice, it means AI that works the way people do. Teams of specialists coordinating to achieve more than any one agent could alone.

Curious? You Should Be.

Enterprise AI is entering a new phase. The focus is shifting from individual task automation to building systems of agents that work together.

If agents cannot communicate, the result is the same as a workplace where departments never share information. Progress slows and outcomes suffer.

With Synoptix AI, agents are not just performing tasks in isolation. They are collaborating, exchanging context, and supporting enterprise goals as a coordinated network.

Now is the time to explore how Synoptix AI enables interoperable agents that deliver stronger results.

Agent to Agent (A2A): The Missing Link in Enterprise AI Collaboration