Multi-agent architecture

A design in which several specialised AI agents share context and divide work across tasks such as investigation, threat hunting, and intelligence analysis. This increases parallelism and coverage, but it also creates a governance challenge because multiple machine identities may have overlapping access.

Expanded Definition

Multi-agent architecture is an operating pattern for agentic AI systems in which several autonomous software entities coordinate through shared context, task decomposition, and tool access. Rather than one general-purpose agent handling every step, the system assigns narrow responsibilities such as planning, retrieval, triage, execution, or review. That division can improve speed and coverage, but it also increases the number of identities, permissions, and trust relationships that must be governed.

In security terms, the architecture matters because each agent can act with execution authority, which means the environment must treat the ensemble as a set of machine identities rather than a single chatbot. Guidance is still evolving across vendors, and no single standard governs this yet, so security teams often anchor design choices in frameworks such as the OWASP Top 10 for Agentic Applications 2026 and the NIST AI Risk Management Framework while they define internal control boundaries.

The most common misapplication is treating a multi-agent system as a single trusted application, which occurs when teams reuse one service account or shared token across agents with different roles.

Examples and Use Cases

Implementing multi-agent architecture rigorously often introduces coordination overhead, requiring organisations to weigh improved task coverage against more complex identity, logging, and approval controls.

• A threat-hunting workflow uses one agent to collect alerts, another to enrich indicators, and a third to draft analyst notes before a human approves any response action.
• An investigation assistant assigns one agent to query SIEM data, another to correlate endpoint telemetry, and another to summarize findings for incident handlers.
• An intelligence-analysis stack uses specialised agents for open-source collection, source validation, and report drafting, with shared context stored in a governed workspace.
• An internal automation system deploys one agent to open tickets, another to retrieve secrets, and a reviewer agent to verify the proposed change before execution.
• A governance pilot uses CSA MAESTRO agentic AI threat modeling framework alongside design reviews to map how agents exchange prompts, tool outputs, and credentials.

These patterns are increasingly discussed in relation to the OWASP Agentic AI Top 10, especially where delegation, tool abuse, and cross-agent trust are the real risks.

Why It Matters for Security Teams

Multi-agent architecture changes the attack surface because compromise in one agent can propagate through shared context, overbroad permissions, or poorly isolated tool chains. Security teams need to know which agent can call which tool, which data it may access, and whether a downstream agent can inherit untrusted instructions from an upstream one. That is where identity governance becomes central: each agent should have a distinct, auditable non-human identity, narrowly scoped secrets, and explicit approval rules for high-impact actions.

This is also where adversarial AI thinking becomes practical. The MITRE ATLAS adversarial AI threat matrix helps teams reason about manipulation paths, while the Anthropic report on an AI-orchestrated cyber espionage campaign shows how delegated automation can be abused when control boundaries are weak.

Organisations typically encounter the blast-radius problem only after an agent misroutes a tool call, at which point multi-agent architecture becomes operationally unavoidable to redesign.

Related resources from NHI Mgmt Group

• AI Agent Authentication
• How should security teams implement agent-to-agent authentication in multi-agent systems?
• Why do multi agent systems create more identity risk than single AI assistants?
• Why do multi-hop AI agent workflows create more risk than single-agent automation?