task-distributor

Distribute tasks across multiple agents by matching required capabilities to available agents and optimizing token economics. This capability-based routing ensures tasks go to the best-suited agents while meeting performance SLAs.

Load balance a heterogeneous agent fleet by dynamically scaling worker pools and routing tasks based on real-time agent state. This maximizes throughput while controlling runtime costs across mixed-capability agents.

Queue management in multi-agent systems handles priorities, deadlines, and fault tolerance using backoff strategies. This ensures reliable task completion when agents fail or become unavailable during distribution.

Optimize token economics during task routing by applying cost-aware distribution algorithms. The system balances cost optimization against required performance SLAs to maximize throughput with optimal resource use.

Dynamic scaling and observability are needed when coordinating a large workload across a heterogeneous agent fleet. Auto-scaling adjusts pools based on demand while exposed metrics inform ongoing routing decisions.

Route tasks to available agents by evaluating real-time state, required capabilities, and queue priorities. The system manages deadlines and retries with backoff to ensure tasks complete within required timeframes.