Agent-to-agent communication introduces a class of risk that doesn’t map cleanly to existing security controls. In multi-agent systems, risk can live in a single tool or the agent communications, emerging from the connections between agents and how those connections behave over time.

As agents are chained together to execute complex workflows, they begin to delegate, infer intent, and act on each other’s outputs. At that point, failures are rarely the result of a single bad decision. They are the result of compound behavior: small deviations that propagate across agents until the system produces an outcome no one explicitly approved.

For security teams, the problem manifests across three dimensions

Opacity. Agent frameworks are designed for autonomy, not observability. Agents discover each other dynamically, form execution paths on the fly, and adapt behavior based on context. Without explicit discovery of these relationships, security teams are left blind to which agents are interacting, in what order, and for what purpose. You can’t secure what you can’t see, and in agentic systems, the attack surface is the interaction graph itself.

Behavioral Drift. Even when individual agents operate as expected, their collective behavior can evolve in ways that violate organizational intent. A workflow that starts as low-risk enrichment can, over several handoffs, cross into decision-making or action-taking territory. Traditional controls don’t reason about sequence, dependency, or cumulative impact across agents.

Policy Enforcement at Runtime. Static design-time assumptions break down when agents are autonomous. Guardrails need to exist at the interaction layer: which agents may collaborate, what types of tasks may be delegated, how far a chain may extend, and which behaviors should immediately terminate execution. Logging after the fact is insufficient when agents can act faster than humans can respond.

What this means for CISOs

Securing A2A means treating agent interactions as a first-class security domain. This requires:

• Continuous discovery of agent relationships
• Real-time monitoring of emergent behavior
• Policy controls that constrain how autonomous systems operate together, not just how they operate individually

Agentic AI isn’t inherently unsafe. But without visibility and governance at the interaction layer, it becomes unpredictable. And in security, unpredictability is the risk that matters most.