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Keep shoppingAgent2Agent Protocol (A2A): AI Governance & Compliance
Role Play
New
25 students
Agent2Agent Protocol (A2A): AI Governance & Compliance
A2A vs MCP, agent identity, auditability, EU AI Act, ISO 42001 & NIST AI RMF. Multi-agent governance without coding
Created byData Universe
Last updated 7/2026
English
English [Auto],
What you'll learn
- Understand what the A2A protocol is, why it exists, and how it enables AI agents to discover and collaborate with each other.
- Explain how A2A works without jargon: agent cards, tasks, messages, artifacts, interaction modes, and the opacity principle.
- Distinguish clearly between A2A and MCP, understanding when to use each protocol and how they complement each other.
- Identify the new governance risks that emerge when agents collaborate autonomously and delegate tasks to other agents.
- Apply security controls for multi-agent systems: agent identity, authentication, data in transit, and access boundaries.
- Design traceability and auditability mechanisms that reconstruct what agents did and why in multi-agent workflows.
- Navigate the regulatory implications of A2A under the EU AI Act, ISO 42001, and the NIST AI Risk Management Framework.
- Map accountability chains in multi-agent flows and define who is responsible when cascading errors cross agent boundaries.
- Evaluate vendor A2A claims critically, write interoperable-agent usage policies, and present A2A risks to leadership.
- Apply a maturity checklist to assess your organization's readiness for A2A adoption and avoid the five most common mistakes.
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Course content
10 sections • 33 lectures • 2h 46m total length
Requirements
- No programming or engineering background required — this course is designed for governance, compliance, and business professionals.
- Basic familiarity with AI concepts like agents or LLMs is helpful but not required — all foundational concepts are introduced clearly.
- All you need is a role involving AI governance, risk, compliance, or strategic oversight and a desire to lead responsibly.
Description
AI agents are no longer isolated tools. They are beginning to collaborate: calling each other, delegating tasks, and producing outcomes across organizational boundaries without a human touching every step. The A2A protocol is the open standard that makes this collaboration possible, and it is already live in version 1.0.
For anyone responsible for governance, compliance, risk, or oversight, this is not a future concern. It is a present one.
This course gives you a complete, non-technical understanding of the A2A protocol and the governance frameworks you need to control multi-agent systems responsibly. No programming required. No engineering background needed. Just clear, structured knowledge for the professionals whose job is to make sure these systems operate within defined boundaries, with full accountability.
You will start by understanding what an AI agent actually is, why isolated agents create a silo problem that custom integrations cannot solve at scale, and what A2A proposes instead. From there, you will learn how the protocol works in practice: how agents discover each other through agent cards, how tasks are created and tracked through their lifecycle, and how the principle of opacity allows agents to collaborate without exposing their internal logic.
You will develop a precise understanding of how A2A differs from MCP, the complementary protocol for connecting agents to tools, and when to use each in a real architecture. You will then put on the governance lens: identity and authentication, traceability and auditability, observability, data in transit, and the fundamental tension between operational opacity and the transparency that compliance demands.
The course covers the regulatory landscape in depth, including how the EU AI Act assigns roles and obligations in multi-agent deployments, what ISO 42001 and the NIST AI Risk Management Framework require, and how to map the accountability chain when cascading errors cross agent boundaries.
The final section is practical: an organizational readiness checklist, a framework for evaluating vendor claims, a template for writing interoperable-agent usage policies, a guided end-to-end case study, and a structured approach for presenting A2A risks and controls to leadership.
No technical background required. Just the responsibility of governing AI systems that are becoming more autonomous, more interconnected, and more consequential by the month.
Who this course is for:
- AI governance and compliance professionals responsible for overseeing how autonomous agents operate within their organizations.
- Data Protection Officers and privacy professionals who need to understand how agent-to-agent data flows create new obligations.
- Risk managers and internal auditors evaluating multi-agent systems and the accountability gaps they introduce across workflows.
- Business architects and enterprise architects designing systems where multiple AI agents collaborate across functions or vendors.
- Legal and regulatory affairs professionals navigating EU AI Act, ISO 42001, and NIST obligations for multi-agent deployments.
- CISOs and security professionals assessing identity, authentication, and data-in-transit risks in agent-to-agent interactions.
- Technology leaders and CTOs who need to evaluate A2A vendor claims and make informed decisions about multi-agent adoption.
- AI ethics and responsible AI teams developing policies and controls for autonomous agent collaboration at organizational scale.
- Consultants and advisors helping organizations govern, audit, and control AI agent systems across industries and use cases.
- Any professional involved in AI oversight who wants to understand A2A before multi-agent systems become standard infrastructure.