Multi-Agent Orchestration
Definition
The coordination of multiple AI agents working together on a shared task, where each agent has a specific role, capabilities, and responsibilities. Multi-agent systems use various coordination patterns: hierarchical (manager-worker), collaborative (peer-to-peer), competitive (market-based), and conversational (negotiation-based). Frameworks like CrewAI, AutoGen, and LangGraph enable multi-agent workflows.
Why It Matters
Multi-agent systems multiply the attack surface. If one agent has a 1% failure rate, five agents coordinating have a compounded failure rate. More agents = more unvalidated tool calls = more risk. The governance challenge grows exponentially with the number of agents, not linearly.
How Exogram Addresses This
Exogram governs every action every agent proposes, regardless of the coordination pattern. Each agent's tool calls are independently evaluated by the deterministic policy engine. Agent A can be blocked while Agent B passes — per-action governance, not per-agent.
Related Terms
Key Takeaways
- → This concept is part of the broader AI governance landscape
- → Production AI requires multiple layers of protection
- → Deterministic enforcement provides zero-error-rate guarantees