Can agent-to-agent communication protocols like A2A and ANP unlock true multi-agent orchestration in enterprise AI?

Why are enterprises exploring agent-to-agent protocols as the next layer of AI integration architecture?

As enterprise adoption of AI agents matures, the limitations of isolated, single-agent deployments are becoming increasingly evident. Companies are now turning their attention to a new class of open standards that enable agents to communicate directly with one another. These protocols—including Agent2Agent (A2A), Agent Network Protocol (ANP), and Agent Communication Protocol (ACP)—are designed to facilitate secure, observable, and scalable agent-to-agent coordination across distributed workflows.

The shift comes as enterprise AI leaders seek to move beyond tool invocation toward collaborative agentic ecosystems. While Salesforce Inc. (NYSE: CRM) addressed the agent-to-system layer with Model Context Protocol (MCP) in Agentforce 3, emerging agent-to-agent protocols are positioned to unlock horizontal interoperability—where agents exchange task status, negotiate delegation, and share state context without human or central orchestrator intervention.

What is Agent2Agent (A2A) and how is it enabling decentralized workflow orchestration?

Agent2Agent (A2A) is an open protocol co-developed by Google Cloud and more than 50 contributors, including Salesforce, ServiceNow, Atlassian, and SAP. It provides a common language for agents to discover each other, share capabilities, and coordinate actions. Built atop widely adopted standards such as JSON-RPC, HTTP, and server-sent events, A2A supports asynchronous task exchanges with authentication layers like mutual TLS and enterprise identity systems.

A2A allows agents to initiate tasks, respond with outputs, and participate in cascading workflows. For example, a customer onboarding agent in one system can request KYC validation from a partner agent, then route the verified record to a billing agent—all governed by identity access controls and telemetry. The protocol is designed for flexibility, enabling use cases in customer support, DevOps automation, finance processing, and more.

Institutional sentiment around A2A is largely positive, with early adopters highlighting its potential to simplify enterprise-scale agent orchestration. However, industry watchers caution that integration complexity and security frameworks will remain top of mind during deployment phases.

How does the Agent Network Protocol (ANP) propose a decentralized alternative to platform-centric models?

The Agent Network Protocol (ANP) offers a more decentralized vision for agent interoperability, using W3C decentralized identifiers (DIDs), semantic metadata, and open trust registries to support peer-to-peer discovery. Developed by open-source communities and early-stage startups, ANP is being positioned as the HTTP-equivalent for the “agentic web.” Agents using ANP can authenticate via cryptographic proofs, exchange structured capabilities, and log interactions to verifiable audit trails.

This architecture is particularly compelling for industries where agents span multiple organizations—such as supply chains, decentralized finance, and cross-border logistics. By removing the need for centralized brokers or API layers, ANP enables scalable coordination without reliance on a single platform’s governance.

Despite its promise, ANP adoption in production remains limited. Analysts point to the lack of standard registries, agent certification mechanisms, and robust fallback mechanisms as barriers to enterprise rollout. Nonetheless, several pilot programs in the EU and Asia-Pacific are evaluating ANP for government-to-government data exchange and distributed resource planning.

What is Agent Communication Protocol (ACP) and where does it fit into the AI orchestration stack?

Agent Communication Protocol (ACP) is a lower-level messaging protocol that focuses on asynchronous, multimodal interactions between agents. It supports RESTful API semantics, multipart message formats, and streaming data—making it suitable for long-running workflows, hybrid human-AI coordination, and complex task flows that require real-time state updates.

While not yet widely deployed, ACP has gained traction in enterprise labs and internal use cases where agents need to share document fragments, media content, or semi-structured updates during resolution processes. Examples include incident response coordination between IT agents or document assembly workflows involving legal, compliance, and contract negotiation agents.

As AI ecosystems become more modular, experts see ACP complementing both A2A and ANP as part of a layered stack—handling protocol-level messaging beneath discovery and orchestration semantics.

How are Salesforce, Microsoft, and Google positioning themselves in the agent-to-agent protocol landscape?

Salesforce is reportedly experimenting with agent-to-agent frameworks to complement its MCP tooling in Agentforce 3. While it has not yet formally announced support for A2A or ANP, the architecture of Agentforce Studio and Command Center suggests readiness for coordinated agent behavior. Observability features such as OpenTelemetry tracing, session logs, and real-time agent wallboards lay the groundwork for agent intercommunication.

Google Cloud has taken a more direct approach. A2A is already integrated into its Vertex AI Agent Builder, allowing developers to compose multi-agent workflows using secure event-based coordination. Microsoft, meanwhile, is embedding A2A into its Azure AI Foundry and Copilot Studio, leveraging the protocol to ensure compliance and identity governance across LLM-powered agents. These implementations demonstrate how hyperscalers are competing to provide full-stack AI agent infrastructure—from LLMs to orchestration and tool connectivity.

According to institutional investors, these moves reflect the growing demand for platform-agnostic agent collaboration. Analysts expect Salesforce to announce formal A2A or ANP alignment by late 2025, depending on regulatory progress and ecosystem stability.

What are the technical and governance challenges blocking widespread adoption of agent-to-agent protocols?

Despite promising capabilities, multi-agent protocols face significant hurdles. Enterprise IT leaders cite authentication complexity, agent state management, auditability, and compliance assurance as unresolved issues. Standards bodies have yet to converge on consistent models for fallback behavior, data provenance, or error propagation between agents.

Security concerns are also prominent. Analysts warn that without rigorous control over agent invocation, rogue agents could flood trusted networks with false signals, prompt injections, or malformed tasks. Protocol hardening, registry whitelisting, and session encryption are becoming focal areas for those developing governance layers atop protocols like ANP and A2A.

In addition, developers must grapple with integration complexity. Enterprises with heterogeneous tech stacks will need middleware, observability layers, and DevSecOps workflows to safely implement these protocols across agents operating in different clouds or compliance regimes.

What is the future outlook for agent-to-agent protocols and enterprise AI architectures?

Enterprise AI leaders anticipate that 2025–2026 will be pivotal for determining whether agent-to-agent communication becomes the norm. Analysts project that within 18–24 months, 20 to 30 percent of enterprise AI workflows could involve more than one autonomous agent, driven by automation in customer service, supply chain, knowledge management, and decision support.

Protocol selection may stratify by use case. For internal orchestration, A2A is likely to gain traction in large platforms like Salesforce, ServiceNow, and Microsoft Azure. For decentralized B2B networks or regulated data sharing, ANP offers a privacy-first, federated alternative. ACP may emerge as the preferred interface for asynchronous, document-rich coordination.

Institutional investors monitoring AI platform maturity will look closely at agent-to-agent protocol adoption as a signal of long-term defensibility and extensibility. Success metrics include number of interoperable agents in production, cross-vendor compatibility, and developer ecosystem momentum.

If standards continue to mature and tooling becomes easier to adopt, agent-to-agent protocols could play the same transformative role for agentic AI that REST and OAuth did for the API economy. However, absent clear governance and convergence, the risk of protocol fragmentation remains a serious concern.