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Multi-agent session tracking and accurate activity attribution are managed by coordinating features, spikes, and tracks using an SDK. It activates at session start to capture exploration and automatically applies delegation rules to ensure proper progress recording.

Orchestrating parallel tasks across AI agents requires SDK-backed delegation rules to coordinate execution. This approach manages multi-agent work by enforcing specific delegation protocols and recording step completion progress directly within the project environment.

Drift detection during multi-agent project work is handled by reviewing active feature warnings and adjusting activity attribution. You inspect status via the Python SDK, identify drift warnings, and create new features or adjust tracks to realign the project.

A Python SDK is required to inspect status, create features, and start work within the session tracking environment. You use it to mark steps complete as you finish them and review active features to manage project progression effectively.

Automatic spikes for exploration and planning should be created at session start to capture initial project activity. This ensures accurate activity attribution and documentation integration before formally starting features and parallel multi-agent coordination.

This multi-agent orchestration approach suits complex project coordination by managing parallel task execution and enforcing delegation rules. It scales collaboration across features, spikes, and tracks while providing drift detection and SDK-backed progress reporting.