Agent-to-agent traffic is the communication path between two software agents that can independently exchange requests, data, or tasks. In identity terms, each hop creates a separate authorization decision that should be governed like any other privileged machine interaction, not treated as ordinary application chatter.
Expanded Definition
Agent-to-agent traffic is not just message passing between autonomous systems. In NHI governance, it is a chain of machine-to-machine trust decisions where each agent can request data, invoke tools, or delegate tasks with its own identity and privilege context. That makes the traffic closer to privileged service-to-service communication than to ordinary application chat.
Definitions vary across vendors, especially when one product treats agent messages as internal orchestration while another exposes them as external API calls. NHI Management Group treats the term as the full identity-bearing exchange between two agents, including authentication, authorization, policy enforcement, and auditability across each hop. This aligns with the risk framing in the OWASP Agentic AI Top 10 and the governance posture in the NIST AI Risk Management Framework.
The most common misapplication is treating agent-to-agent traffic as low-risk internal chatter, which occurs when teams omit per-hop authorization because both endpoints are “trusted” software.
Examples and Use Cases
Implementing agent-to-agent traffic rigorously often introduces policy overhead and latency, requiring organisations to weigh faster automation against stronger control boundaries.
- An incident-response agent asks a threat-intelligence agent for enriched indicators, but the request is only fulfilled after identity validation, scoped consent, and logging.
- A code-review agent delegates a dependency scan to a security agent, with short-lived credentials and explicit task scope to prevent privilege creep.
- A procurement agent requests supplier data from a compliance agent, where each message is checked against data-minimisation rules and retention controls.
- An orchestration layer brokers calls between multiple AI agents, using policy decisions to prevent one agent from invoking tools outside its assigned role.
- A cross-domain workflow passes from a customer-support agent to a knowledge agent, and the handoff is governed like a privileged service exchange rather than an informal prompt relay.
For practical breach patterns, see the AI LLM hijack breach analysis and the OWASP NHI Top 10, which both show how uncontrolled delegation turns routine traffic into a privilege escalation path. The same operating model appears in the MITRE ATLAS adversarial AI threat matrix, where agent interactions become part of the attack surface.
Why It Matters in NHI Security
Agent-to-agent traffic becomes a security boundary because compromise rarely stays confined to one agent. If one agent can forward tasks, secrets, or context to another without tight controls, attackers can pivot through the workflow, exfiltrate data, or trigger unauthorized actions. That is why NHI Management Group treats these exchanges as governance-relevant identity events, not just transport events.
This matters especially because NHI exposure is already widespread: NHI Mgmt Group reports that 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, and 92% of organisations expose NHIs to third parties. In agentic environments, those risks expand when one agent can become the proxy for many others. Pairing NIST AI Risk Management Framework guidance with the CSA MAESTRO agentic AI threat modeling framework helps teams map trust boundaries, delegation rules, and audit requirements before an incident creates them.
Organisations typically encounter the operational impact only after a delegated agent sends sensitive data, invokes the wrong tool, or amplifies a compromised credential chain, at which point agent-to-agent traffic becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | NHI-02 | Agent delegation and inter-agent calls create identity and secret exposure risk. |
| NIST AI RMF | Defines AI risk governance for autonomy, oversight, and accountability in agent workflows. | |
| CSA MAESTRO | Covers threat modeling for agentic systems with multi-agent communication paths. |
Map agent-to-agent exchanges to risk controls, then review delegation, monitoring, and escalation paths.
