Agentic orchestration is the governance layer that coordinates multiple autonomous agents to plan, decide, and collaborate toward shared objectives.
It manages intent-driven systems rather than sequencing fixed workflows. The system operates through specific mechanisms and components.
Dynamic task management assigns tasks, resolves dependencies, and enforces policies like security and compliance to ensure coherence in multi-agent systems.
The architecture includes several core components. The orchestrator acts as the central brain, routing requests, managing state, invoking agents, and handling error recovery. Semantic Kernel is an example of this. An agent registry and factory handle discovery, lifecycle management, and spawning of specialized agents.
Communication protocols use standardized interfaces like the Model Context Protocol for tool access and agent-to-agent coordination. A memory layer uses persistent storage such as vector databases or Redis to manage conversation history and tiered context.
Routing and planning employ classifiers and planners to decompose tasks, determine intent, and sequence actions. Governance and observability implement budget caps, human-in-the-loop approval checkpoints, and tracing execution to maintain reliable behavior.
The system often employs hierarchical or decentralized patterns to provide observability and state management across complex, distributed environments.
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
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