AI agent memory should preserve the reasoning behind decisions and the tools called to reach them, so the next agent doesn’t start from zero. While most memory systems log what the agent said and discard the rest, a context graph retains it all to make knowledge actionable.

The Neo4j Agent Memory Service (NAMS) unifies three kinds of memory by modeling short-term messages, long-term POLE+O entities, and reasoning traces as a single queryable context graph, so you know what your agent did and why it did it.

Join our webinar to learn how context graphs:

• Distill successful execution patterns into reusable SKILL.md artifacts that new agents can use on day one

• Provide provenance, grounding, and tenant isolation, so organizations can safely operationalize shared agent knowledge

• Complement vector search to provide richer contextual understanding, not just similarity

We’ll also give you an end-to-end walkthrough from agent interaction to context graph to a distilled, governed skill, so you can enable AI agents that continuously improve, explain their decisions, and scale safely across your organization.