Large Language Model (LLM) agents increasingly orchestrate multiple external tools—including APIs, code functions, Model Context Protocol (MCP) servers, plugins, and sub-agents—to accomplish complex objectives. Evaluating these agents requires temporally coherent data across all tool dependencies, yet production environments feature independently versioned tools, data retention policies, and evolving sub-agent reasoning that make reproducible evaluation fundamentally difficult. Existing agent benchmarks do not face these issues, as they provide static, self-contained environments, leaving a critical gap between benchmark evaluation and production reliability. This paper makes three contributions. First, it introduces a dependency type spectrum classifying agent tool dependencies from stateless APIs to LLM-based sub-agents by their drift characteristics and snapshot fidelity, formalizing the qualitative difference between data drift and reasoning drift. Second, it presents a taxonomy of four temporal challenges—tool drift, temporal incoherence, forward-looking data gaps, and privacy-constrained reproducibility—with a formal analysis of why standard inference-time logging is insufficient for agent evaluation. Third, it proposes design patterns for synthetic point-in-time snapshot generation and validates them experimentally using a simulated incident root-cause analysis agent, demonstrating that temporal incoherence reduces diagnostic accuracy from 100% to 40% and that synthetic snapshot restoration recovers it to 80%.

Evaluation LLM Agents Point-in-Time Data Sub-Agent Reasoning Synthetic Data Temporal Coherence Tool Dependencies