Budget-aware planning helps agents make economically rational tool-use decisions under cost constraints.
We study budget-constrained tool-augmented agents, where a large language model must solve multi-step tasks by invoking external tools under a strict monetary budget. We formalize this setting as sequential decision making in context space with priced and stochastic tool executions, making direct planning intractable due to massive state-action spaces, high variance of outcomes and prohibitive exploration cost. To address these challenges, we propose INTENT, an inference-time planning framework that leverages an intention-aware hierarchical world model to anticipate future tool usage, risk-calibrated cost, and guide decisions online. Across cost-augmented StableToolBench, INTENT strictly enforces hard budget feasibility while substantially improving task success over baselines, and remains robust under dynamic market shifts such as tool price changes and varying budgets.
Agentic LLMs fail to respect budget constraints even when explicitly informed of costs and remaining funds.
INTENT framework enables cost-aware planning by predicting intention satisfaction rather than exact tool outputs.
Real-world agent deployment requires economically rational decision-making for expensive external tools and APIs.
Classical planning approaches like MCTS are computationally prohibitive for high-dimensional action and state spaces.
Agents struggle with sequential decision-making under stochasticity when tool outcomes are uncertain and costly.
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