Inconsistent performance in AI agents often stems from ambiguity in instructions or excessive task complexity. Refining the prompt (D) to be more explicit, structured, and clear about objectives, constraints, and desired output formats directly reduces ambiguity and improves the reliability of the agent's actions. Decomposing a large, complex task into smaller, more manageable subtasks (C) and assigning them to specialized agents (or handling them sequentially) simplifies the problem space for each step. This "chain-of-thought" or multi-agent approach enhances robustness and consistency by reducing the chance of failure on any single, overly complex step.

A. Removing schema validations and assertions would increase inconsistency by allowing malformed or incorrect data to propagate, leading to more errors.

B. Increasing randomness (e.g., higher temperature) makes the model's output less deterministic and more creative, which is the opposite of promoting consistent task execution.

1. NVIDIA. (2024). NVIDIA NeMo Guardrails Documentation. The documentation emphasizes defining explicit conversational flows and using validation to ensure agent outputs are consistent and adhere to predefined rules, which aligns with refining objectives.

2. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., ... & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv preprint arXiv:2201.11903. This foundational paper demonstrates that breaking down problems into intermediate steps (subtasks) significantly improves the reliability and accuracy of LLM outputs.

3. Wang, L., Ma, W., Zhu, X., et al. (2023). A Survey on Large Language Model based Autonomous Agents. arXiv preprint arXiv:2308.11432. Section 3.2, "Planning," discusses task decomposition as a core capability for agents to handle complex tasks reliably by breaking them into simpler, executable sub-goals.