Deep Research Agents: The Architecture Nobody Talks About #1

Deep Research agents are widely misunderstood as LLMs with web access, but they are actually control systems with distinct architectural layers: a planner, a research loop, an evidence layer, a synthesis step, and ideally a verification pass. The post breaks down the lifecycle common to nearly all such systems and highlights five key design decisions — single vs. multi-agent, upfront vs. incremental planning, memory management, retrieval strategy, and termination conditions. The most underappreciated and immature part of these systems is evidence handling: citation faithfulness, contradiction resolution, deduplication, and provenance tracking are poorly enforced across the industry. Current benchmarks fail to capture these gaps, measuring final answer accuracy rather than reasoning soundness or citation integrity. The post argues that Deep Research is evolving from a model capability into an engineering discipline, where the hard, unglamorous work of evidence and verification determines real-world trustworthiness.