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Cost-aware multi-agent orchestration coordinates a centralized scheduling brain with multiple parallel agents to process complex tasks. You need it for multi-step work, coding, research, and large task batches where minimizing cost, latency, and delivery risk is critical.

Policy-first model routing assigns tasks to the right agent pool based on predefined policies and runtime model latency awareness. It uses runtime guardrails and a lightweight runner to optimize scheduling, ensuring fast response by replying first then dispatching background agents.

You route large task batches by using a centralized brain to coordinate up to eight tentacle agents working in parallel. The system applies task grading and intelligent routing to split workloads, minimizing latency and delivery risk across the agent pool.

Yes, multi-agent orchestration operates without external dependencies. It uses a built-in lightweight runner and runtime guardrails to manage the centralized brain and tentacle agents, requiring no additional components to schedule and route tasks.

The centralized brain limits orchestration to a maximum of eight tentacle agents. While this minimizes cost and delivery risk for complex tasks, scaling beyond eight agents or handling highly decentralized workloads may exceed the system's coordination capacity.

Yes, multi-agent scheduling is designed for coding, deep research, and multi-step operations. It grades tasks by complexity and routes them to appropriate agents, enabling parallel processing for faster delivery on complex work.