Multi-Agent Systems

Multi-agent systems (MAS) are architectures where multiple autonomous AI agents work together to accomplish complex tasks. Each agent has specialized capabilities and a defined role, and they communicate and coordinate to achieve goals that would be difficult or impossible for a single agent. This mirrors how human expert teams operate — with specialists collaborating rather than generalists working alone.

In security testing, multi-agent architectures enable more thorough assessments by allowing simultaneous specialization. A reconnaissance agent can focus on mapping attack surface while a pentester agent tests known endpoints, with a coordination agent ensuring comprehensive coverage and a reporting agent documenting findings in real-time. This parallel specialization produces better results than sequential single-agent approaches.

How APVISO uses this: APVISO's four-agent architecture is a prime example of multi-agent systems applied to security. The recon, pentester, lead, and reporter agents communicate via MCP tools, sharing intelligence in real-time. The lead agent dynamically coordinates strategy based on what other agents discover — producing more thorough results than any single-agent approach.