Task Decomposition Engine

The engine recursively breaks a complex objective into a tree structure of sub-goals. A top-level mission like 'fulfill wave 3 orders' is decomposed into zone picks, transport segments, and packing station queues. This hierarchical breakdown allows the system to reason about dependencies and parallelization opportunities at different levels of abstraction, ensuring that no atomic action is orphaned from its parent objective.

Decomposition is not purely logical; it is constrained by the capability registry. The engine must generate sub-tasks that are physically executable by the available fleet. A 'transport' sub-task is annotated with payload weight and dimensions, which are then matched against the payload capacity and fork type of registered agents. This ensures that a task to move a 1000kg pallet is never assigned to a small-item AMR.

The engine establishes precedence constraints between sub-tasks. It defines that a 'pick' action must complete before a 'place' action can begin. It also resolves spatial conflicts by sequencing tasks that occupy the same zone. This creates a directed acyclic graph (DAG) of tasks, where the critical path determines the theoretical minimum completion time for the entire mission.

Each generated sub-task is designed as an idempotent unit of work. The engine assigns a unique idempotency key to every task, ensuring that if a dispatch command is retried due to a network timeout, the receiving agent executes the action exactly once. This prevents duplicate pallet moves or double-picking of inventory, which is critical for maintaining inventory integrity in a warehouse management system.

The engine subscribes to the fleet state estimator to listen for exceptions. If an agent reports a mechanical failure or a path becomes blocked, the engine does not just fail the mission. It dynamically decomposes the remaining objective into a new set of sub-tasks, preserving completed work. This graceful degradation ensures that a single point of failure does not cascade into a full mission abort.

Beyond agent assignment, the engine decomposes tasks with resource locks. A sub-task to place an item in a storage location includes a temporary reservation on that location to prevent another concurrent task from targeting the same slot. This spatial-semantic locking prevents resource contention and inventory corruption before the physical action even begins.

The stateful execution service that manages the lifecycle of a decomposed plan. It sequences the sub-tasks produced by the decomposition engine, evaluates conditional logic, and handles error propagation. Unlike the decomposition engine which focuses on planning, the workflow engine focuses on orchestration of the resulting directed acyclic graph (DAG).

• Manages task state transitions (pending, active, completed, failed)
• Triggers compensating transactions via the Saga Pattern on failure
• Ensures exactly-once execution semantics using Idempotency Keys

The automated process that informs the decomposition engine what is possible. Before a task can be broken down, the engine must query the Agent Registry to understand available agent attributes.

• Catalogs functional attributes: max payload, lift height, navigation type
• Uses the MassRobotics Interop Standard for vendor-agnostic capability reporting
• Enables constraint-based decomposition by matching sub-task requirements to agent profiles

The real-time assignment system that consumes the sub-tasks generated by the decomposition engine and matches them to the most suitable agent. It operates on a continuous optimization loop, considering current fleet state estimation and battery-aware scheduling constraints.

• Uses auction-based or optimization solver algorithms for assignment
• Re-allocates tasks on agent failure or higher-priority preemption
• Depends on the decomposition engine's output of precedence constraints

The constraint-solving layer that adds physical dimensions to decomposed tasks. A sub-task is not just what to do, but where and when. This component calculates time-windowed reservations for spatial resources like aisles, intersections, and charging stations.

• Prevents deadlock by reserving spatial nodes in a time-space graph
• Integrates with Zone Management Protocols for geofenced task execution
• Feasibility feedback loop: informs the decomposition engine if a temporal plan is impossible

The bidirectional synchronization layer that allows the decomposition engine to plan against a high-fidelity virtual replica of the physical environment. Before committing a plan, the engine can simulate the decomposed task sequence in the digital twin to validate cycle time estimates and detect potential resource conflicts.

• Enables what-if analysis for complex multi-agent task sequences
• Provides ground-truth state synchronization for accurate plan preconditions
• Uses Neural Radiance Fields (NeRFs) for dynamic obstacle representation

The structured recovery system activated when a decomposed sub-task fails. It defines escalation policies and alternative execution branches. The decomposition engine must pre-plan for known failure modes by generating contingency sub-tasks.

• Triggers Real-Time Replanning Engines for dynamic task re-decomposition
• Classifies exceptions: agent fault, environmental blockage, time-out
• Implements the Circuit Breaker pattern to prevent cascading plan failures