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Road to NODES | Agents in the Wild: Building AI That Learns

Build a full graph-based agent memory stack in Neo4j — hierarchical context graphs, retrieval and answer playbooks, and dynamic playbook composition — and compare how predefined workflows versus fully autonomous agents interact with that memory differently. Go deep on the Reflector→Curator loop, where human feedback and outcome signals update playbooks over time, and see how routing reasoning steps to specialized models becomes a learned part of that memory.
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