---
title: "08: Agent-to-Agent (A2A) Implementation with Dapr Workflows in DACA"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# 08: Agent-to-Agent (A2A) Implementation with Dapr Workflows in DACA

This section focuses on leveraging Dapr Workflows to orchestrate complex interactions and collaborations between multiple DACA agents (often implemented as Dapr Actors or Dapr-enabled services).

**Objective**: Integrate Google’s A2A protocol with SACA to enable interoperable agent communication across frameworks.

**Key Concepts**:
- A2A protocol: Standardized agent-to-agent communication (HTTP, JSON-RPC, SSE).[](https://www.maginative.com/article/google-just-launched-agent2agent-an-open-protocol-for-ai-agents-to-work-directly-with-each-other/)
- A2A Agent Cards: Advertising agent capabilities.[](https://www.stanventures.com/news/google-launches-agent2agent-protocol-to-unify-ai-agents-communication-2421/)
- Dapr-A2A synergy: Combining Dapr’s stateful actors with A2A’s communication layer.
- Interoperability: Connecting Dapr actors with external A2A-compliant agents.

**Key Topics:**
*   Designing workflows that coordinate sequences of agent actions.
*   Using service invocation from workflow activities to call DACA agents.
*   Employing pub/sub patterns within workflows to communicate with or trigger agents.
*   Managing state for multi-agent tasks using workflow persistence.
*   Examples of A2A patterns:
    *   Delegation: A primary agent workflow delegating tasks to specialized agents.
    *   Aggregation: A workflow collecting results from multiple agents.
    *   Sequential Collaboration: Agents performing steps in a coordinated sequence managed by a workflow.
*   Ensuring reliability and fault tolerance in multi-agent workflows.
