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2 changes: 1 addition & 1 deletion agentic_ai/agents/agent_framework/STATE_MANAGEMENT.md
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Expand Up @@ -408,7 +408,7 @@ class SessionMemoryCheckpointStorage(CheckpointStorage):

2. **Team agent**
- `MagenticOrchestratorExecutor` automatically snapshots each participant’s `chat_history`.
- Checkpoints include outstanding tool requests (`RequestInfoExecutor` state).
- Checkpoints include outstanding tool requests and plan-review state managed by the orchestrator via `WorkflowEvent(type="request_info")`.
- Ensure all custom executors implement `restore_state` to interpret snapshots after schema changes.

3. **Human-in-the-loop**
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Expand Up @@ -128,7 +128,7 @@ When stalling, the manager calls `replan()`, which uses [`ORCHESTRATOR_TASK_LEDG
1. Registers **participants** (`participants()`), each either a `BaseAgent` or another `Executor`.
2. Configures the **manager** (`with_standard_manager()`).
3. Optionally enables **plan review**, **checkpointing**, and **unified callbacks**.
4. Builds a [`Workflow`](reference/agent-framework/python/packages/main/agent_framework/_workflow/_workflow.py) graph: orchestrator (start node), optional `RequestInfoExecutor` for plan review, and bidirectional edges to each agent executor.
4. Builds a [`Workflow`](reference/agent-framework/python/packages/main/agent_framework/_workflow/_workflow.py) graph: orchestrator (start node) with optional plan review enabled via `enable_plan_review=True`, and bidirectional edges to each agent executor. Plan review is handled internally by the orchestrator, which emits `WorkflowEvent(type="request_info")` events containing `MagenticPlanReviewRequest` data.

### 4.2 Workflow Context & State

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