worker-integration

Task dispatch to specialized agents is automated by routing triggers to optimal worker-agent pairs, recording latency and success rates to improve future routing decisions. It supports distributed, trigger-driven workflows across research, coding, security, and auditing domains.

Benchmark-aware agent selection evaluates performance metrics to route tasks to the best available agent. It records latency and success rates during task dispatch, using this historical performance data to improve ongoing routing decisions.

Yes, worker-agent mappings are configurable for distributed workflows. You can specify mappings in the frontmatter and examples to define dynamic assignments, establish fallback paths, and maintain visibility into performance metrics.

Orchestrating workers with fallback paths is achieved by applying configurable worker-agent mappings that dynamically assign tasks based on triggers. If an optimal agent fails, the system routes tasks through predefined fallback paths to ensure completion.

Task results are stored using memory-keyed storage, which allows efficient retrieval and tracking of dispatched task outcomes. This mechanism integrates with benchmark-aware agent selection to maintain historical performance data for routing optimization.

No external dependencies are required to track performance metrics between workers and agents. The orchestration mechanism internally records latency and success rates during task dispatch to improve routing decisions without external libraries.