A planning agent repeatedly chooses unnecessary tool calls, increasing cost and latency. Which improvement is BEST?

The core problem is inefficient decision-making by the planning agent, leading to unnecessary cost and latency. The most direct and effective solution is to refine the agent's decision-making logic. Optimizing tool selection policies, for instance through better prompt engineering or fine-tuning, improves the agent's ability to choose the correct tool for a given task. Adding decision constraints (e.g., using frameworks like NVIDIA NeMo Guardrails) explicitly prevents the agent from making known bad choices, such as calling redundant tools or entering loops. This approach systematically targets the root cause of the inefficiency.

B. Always calling every available tool would maximize cost and latency, making the existing problem significantly worse rather than solving it.

C. Increasing temperature introduces more randomness into the model's outputs, which would likely lead to more erratic and inefficient tool calls, not fewer.

D. Disabling evaluation logging would remove the very data needed to diagnose, understand, and fix the agent's poor performance.

1. NVIDIA NeMo Guardrails Documentation. "Preventing Tool Spamming." The documentation provides examples of defining guardrails to control an LLM's tool usage, directly addressing the problem of unnecessary or repeated tool calls by adding explicit constraints. (Accessed via NVIDIA Developer Zone).

2. Yao, S., et al. (2023). "ReAct: Synergizing Reasoning and Acting in Language Models." This paper explores how to structure agent prompts to improve the synergy between reasoning and tool use (acting), which is a form of optimizing the tool selection policy. arXiv:2210.03629.

3. Stanford University, CS324 Course. "Large Language Models." Course materials on agentic architectures discuss the challenges of planning and tool selection, where optimizing the model's policy is the primary method for improving performance and efficiency.