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Agentic AI
Planning & Control Flow
Partial Global Planning
Each agent maintains a partial view of others' plans and incrementally merges local plans into a shared partial global plan, interleaving coordination with execution.
Intent & Description
🎯 Intent
Each agent maintains a partial view of others’ plans and incrementally merges local plans into a shared partial global plan, interleaving coordination with execution.
📋 Context
A multi-agent system coordinates on a problem where a complete global plan is impractical to compute — the problem is too large, the world is non-stationary, or agents only learn what they need to coordinate as they go. Waiting for a global plan to complete before any agent acts is unworkable.
💡 Solution
- Each agent runs a planner that produces both local actions and partial-global-plan fragments. - Agents periodically exchange fragments with constraint-neighbours; merging produces consistent shared plan structure for the parts they care about. - When new observations or revisions arrive, the affected fragment is updated and shared again. - The team never holds a complete global plan; it holds a sufficient partial one. Execution and planning interleave.
Real-world Use Case
- Multi-agent problem too large for a single global planner.
- World is non-stationary; plans must keep revising as conditions change.
- Coordination benefits exceed fragment-exchange communication cost.
Source
Advantages
- Coordinated behaviour without the cost of a complete global plan.
- Resilient to non-stationary worlds — revisions are local fragment updates.
- Scales beyond what a single planner could handle.
Disadvantages
- Fragment merging is non-trivial; conflicting fragments need a resolution rule.
- Some coordination cases require global structure the fragments don’’t capture.
- Thrashing on rapid revisions can degrade into pure local planning.