The rise of AI Agent

Recently there is a lot of focus on implementing AI in enterprises that want to enhance their operational efficiency and decision-making processes. With the advent of technologies like GPT models, it has become easier to develop sophisticated AI agents. These AI Agents are developed to be autonomous, capable of performing tasks without human intervention.

You can refer my earlier article on AI Agents

The rise of AI agents has been fueled by advancements in machine learning, natural language processing, and distributed computing. These agents are designed to perform tasks autonomously, making decisions based on the data they process. As organizations increasingly adopt AI technologies, the need for effective communication and collaboration between agents becomes paramount.

Cloud has been another enabler for the development and deployment of AI agents, providing the necessary infrastructure and scalability to support their operations. With cloud, it has become easier to manage and orchestrate multiple AI agents, allowing them to work together seamlessly. This has truly help in bringing AI to the forefront of business innovation even for small businesses.

A typical enterprise will have multiple AI agents working on different tasks, from customer support to data analysis. These are often specialized agents designed to handle specific functions. Though organizations are going for a more decentralized approach in deploying AI agents, enabling them to operate independently while still collaborating with other agents when needed.

Challenges in Distributed AI Agents

As the number of AI agents in an enterprise grows, so does the complexity of managing their interactions. Some of the key challenges include:

Discovery: How do agents find each other and understand their capabilities? Communication: What protocols and formats should be used for inter-agent communication? Coordination: How can agents work together effectively to achieve common goals? Security: How to ensure secure communication and data exchange between agents? Scalability: How to manage a large number of agents without overwhelming the system? Interoperability: How to ensure that agents developed by different teams or vendors can work together seamlessly?

Agent 2 Agent (A2A) Protocol

Agent-to-Agent (A2A) Protocol is a vendor-neutral, contract-first way for autonomous agents to discover each other, advertise capabilities, negotiate tasks, and exchange messages securely across runtime boundaries. It standardizes how multi-agent systems interoperate—whether agents run in different frameworks, clouds, programming languages, or teams.

Why A2A now? As enterprises adopt specialized agents (for data ops, customer support, content generation, compliance, etc.), emergent collaboration becomes a force multiplier—but only if agents can find, trust, and talk to each other in a consistent way.

A2A enables:

• Discovery (who can do what)
• Interoperability (shared message contract & schemas based on open standards)
• Negotiation (intent → proposal → acceptance)
• Observability (correlation, metrics, audit)
• Security & Policy (authn/z, data classification, consent)
• Scalability (async patterns, backpressure, resilient)

Key Design Principles of A2A

A2A is built on the philosphy of simplicity, enterprise readiness, security and future readiness. Below are some of the key design principles:

Simple / Build on existing standards A2A reuses well‑understood web standards—HTTP, JSON‑RPC 2.0, and Server‑Sent Events (SSE)—to lower the barrier to adoption and maximize interoperability.

Enterprise‑Ready The protocol is designed to align with established enterprise practices for authentication, authorization, security, privacy, tracing, and monitoring—so it can be deployed in real production environments.

Async‑First A2A is explicitly designed for long‑running tasks and human‑in‑the‑loop interactions. Streaming updates and asynchronous notifications are first‑class citizens.

Modality‑Agnostic The content model supports text, files, structured data/forms, and references to audio/video—so agents can negotiate and exchange the right modality for a task.

Opaque Execution Agents collaborate via declared capabilities and exchanged information without revealing internal thoughts/plans/tools—supporting IP protection and safer security boundaries.

Core Challenges it addresses

Interoperability & Collaboration — provide a common language for agents built by different vendors and frameworks to work together (delegation, context exchange, multi‑agent tasks).

Discovery — let agents find and understand one another’s capabilities (via an Agent Card).

Flexibility — support sync request/response, streaming for real‑time progress, and asynchronous push notifications for long‑running work.

Security — align with standard web security practices to enable secure, enterprise deployments.

Asynchronicity — native support for long‑running tasks and HITL scenarios.

Implementation Details

Core concepts of A2A

Key Components of A2A system

Communication Mechanism

Integration Patterns

Conclusion

The Agent 2 Agent (A2A) protocol represents a significant advancement in the field of AI agents, addressing the challenges of interoperability, discovery, and secure communication. By providing a standardized framework for agent interactions, A2A enables more effective collaboration among distributed AI agents, ultimately enhancing their capabilities and impact.