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Agent-to-Agent (A2A) Protocol

1 post · Jun 2026 – Aug 2026

What this trail covers

This post analyzes five common agent architectures and their protocol needs, comparing the Model Context Protocol (MCP) for agent-to-tool communication and the Agent2Agent (A2A) Protocol for agent-to-agent collaboration. It emphasizes choosing protocols based on the specific boundary being crossed (agent-to-tool vs. agent-to-agent) and highlights security considerations for each. MCP is presented as a convenience layer for tool access with potential security gaps, while A2A focuses on agent identity and coordination. The post also discusses the need for a shared-state layer to manage context across different agent frameworks, as neither MCP nor A2A directly address state management. Redis Iris is presented as a solution for providing this shared state, offering memory, retrieval, and operational data synchronization for multi-framework agent systems.

All Redis trails
  1. When Does the A2A Protocol Actually Matter?This post clarifies the practical application of the A2A protocol, emphasizing that its value is primarily realized when agents owned by different teams or vendors need to communicate across trust boundaries. It contrasts this with internal agent coordination, where orchestration frameworks and MCP are often sufficient. The post details A2A's core primitives (Agent Card, Task, Message, Part, Artifact) and its transport-agnostic nature. It highlights the security model, which relies on external identity providers and standard web transport security (HTTPS, OAuth), and the need for explicit identity and authorization at trust boundaries. The post also discusses the evolving landscape of delegated authentication and identity chaining standards, and notes that while A2A has broad industry support, its production prevalence is still being determined.
  2. 5 agent architecture scenarios: assess MCP vs A2AThis post provides a comparative analysis of MCP and A2A protocols across five agent architecture scenarios. It details the use cases for MCP in connecting agents to tools and data, and A2A for inter-agent communication. Specific scenarios illustrate when one, both, or neither protocol is necessary, with a focus on the security implications of each. The post highlights that MCP's security model is less robust for authorization compared to A2A's focus on identity and credentials, and discusses the challenges of observability in multi-agent systems.
  3. Model Context Protocol (MCP) vs. Agent2Agent (A2A): which protocol do you need?This post clarifies the distinction between the Model Context Protocol (MCP) for agent-to-tool integration and Agent2Agent (A2A) for agent-to-agent collaboration. It highlights that A2A is designed for communication between independent agents across trust boundaries, featuring Agent Cards, stateful Tasks, and Artifacts. The post emphasizes that A2A adds overhead and is most beneficial when agents do not share ownership or control, contrasting it with in-framework orchestration for single-team agent systems. It also notes that neither protocol fully addresses state correlation, observability, or performance challenges, leaving these to the implementer, with Redis positioned as a platform for managing such state.
  4. Agent interoperability: a complete explainerThis post provides a comprehensive explanation of agent interoperability, focusing on the MCP (Model Context Protocol) for agent-to-tool communication and A2A (Agent2Agent Protocol) for agent-to-agent coordination. It details the failure modes of lacking interoperability, such as integration sprawl, duplicated work, brittle wiring, and fragmented context. The post clarifies that ACP (Agent Communication Protocol) has merged into A2A. Crucially, it highlights that neither MCP nor A2A solve the problem of shared state, which remains a critical challenge for multi-framework agent systems. It introduces the concept of a shared-state layer and discusses common patterns like blackboard architecture, layered memory, checkpoints, and pub/sub messaging. The post positions Redis Iris as a solution for this shared-state layer, emphasizing its ability to provide low-latency, real-time data synchronization across different agent frameworks and its native integrations with frameworks like LangGraph, AutoGen, Cognee, and MCP.
  5. Multi-Step AI Agents: What They Are & How They WorkThis post explains the concept of multi-step AI agents, contrasting them with single-step systems. It details the think-act-observe loop (ReAct pattern) and highlights the challenges of state management, context accumulation, error propagation, and context-related failures (poisoning, distraction, confusion, clash) in production environments. It emphasizes the critical role of the data layer in providing short-term memory, long-term memory, and operational state for agent reliability, and mentions Redis Iris as a solution for these needs.
  7. AI agent access control: a practical guideThis post introduces the concept of AI agent access control, differentiating it from traditional application authorization. It details three common authorization failures: overly broad permissions, checks at the wrong layer, and authority leakage across hops. The post emphasizes the importance of least privilege and the blast radius of over-scoped agents, particularly in RAG pipelines. It proposes three patterns for enforcing permissions in RAG retrieval pipelines: metadata filtering, permission-aware retrieval, and layered policy enforcement. It also discusses document-level, row-level, and field-level access control, highlighting ABAC and ReBAC models. The post concludes by stressing the value of a governed data model for enforcing these controls.
  8. What MCP Gives Your Agents & What It Leaves to YouThis post clarifies the scope of the Model Context Protocol (MCP), highlighting that while it standardizes agent-to-tool communication via a JSON-RPC 2.0 wire format and defines roles (hosts, clients, servers) and primitives (resources, prompts, tools), it explicitly does not standardize agent memory, data freshness, or reliability. Tool calls are stateless by design, and session state management is being removed from the protocol to be handled at the application or data layer. The post emphasizes that developers must build solutions for cross-session memory, keeping retrieval indices current to avoid stale data (context rot), and ensuring reliability in long agent chains, where each step's success is critical. It positions a real-time data layer like Redis as essential for filling these gaps, providing capabilities for agent memory tiers (short-term, long-term semantic, episodic), data freshness through integration and semantic caching, and reliability by maintaining a fast, current data layer.