handoff

AI agent context transfer is managed by saving session logs, lessons, and preferences before loading the target agent's identity to continue tasks without context loss. It follows a defined flow to memorize state, print a summary, load the recipient, and announce the transition.

Memory preservation during an agent switch requires a memory-first handoff that saves the active session context and updates the target agent's dashboard state. This ensures the Checker agent resumes the task with full knowledge of the Builder's progress.

Task transitions for agents like Planner and Fetcher are coordinated by transferring active work context within a single session. The process memorizes session logs and preferences before loading the target agent, ensuring seamless continuation without losing history.

The AI agent handoff process works through a 4-step flow: memorize context before the handoff, print a concise summary for traceability, load the target agent's identity and memory, and announce the transition. This updates dashboards and preserves all active work context.

No specific dependencies are required to manage context loss during AI agent collaborations. The context management process operates independently to save session logs and transfer active work context directly between agents within the same session.

Session memory loss after transitioning to a new task role occurs when active work context is not explicitly memorized before the switch. Without saving session logs, lessons, and preferences, the target agent cannot load the previous identity and dashboard state needed for seamless continuation.