Deep-Research Agent Architecture: Graph Runtime, Checkpointing, Governance, and Benchmarks
A production deep-research agent can outperform human research teams when engineered around machine-native strengths: massive parallel fan-out, tireless tool-use loops, perfect recall, and deterministic replay. This is the architecture pillar — it owns the checkpointed graph runtime, the typed source/evidence/claim data model, role fan-out and fan-in, replay, adversarial source screening, human gates, and benchmarks — and cross-links to its companion for the retrieval and verification math.