agent-goal-planner

Goal decomposition breaks complex objectives into manageable sub-goals, while cost optimization evaluates action paths to choose efficient sequences. GOAP planning ensures feasibility by analyzing preconditions and dependencies before generating an executable plan.

To generate an action sequence, provide a high-level objective to the agent. The GOAP planner will assess the current state, decompose the goal, evaluate deterministic actions, and output an optimal, executable plan with monitoring hooks.

Yes, mixed execution combines AI reasoning with deterministic code actions. The planner models strongly-typed state representations and matches actions to available tool capabilities, allowing deterministic execution hooks to run within the adaptive plan.

Domain modeling with strongly-typed state representations is required to evaluate action requirements, predict effects, and match tool group resources. This ensures the planner can accurately assess feasibility and manage tool capabilities.

Adaptive replanning is necessary when execution environments are dynamic and conditions change unpredictably. If action preconditions may fail during execution or continuous learning is needed to update strategies based on outcomes, static sequences will not suffice.