Domain Aware Agents Need a System of Record

Why AI Agents Need a System of Record

AI agents reason and act based on a summarized snapshot of your business domain — with no contextual memory of how, when, or why that state came to be. Without access to a reliable system of record, agentic AI systems are prone to repeating past mistakes and making decisions on stale or incomplete data.

Event sourcing addresses this gap. By capturing business intent, decisions, and outcomes as chronological, immutable events, event-sourced architecture gives AI agents something they critically lack: durable, queryable memory grounded in the actual history of your enterprise.

What You'll Learn

This session explores how to use event sourcing as an AI agent memory layer — enabling agents to query your evolving enterprise history, derive accurate insights, and improve decision-making in real time with full auditability.

You will leave with practical guidance on how to:

• Design and implement an event-sourced memory layer using the Axoniq technology stack

• Expose that memory layer to agents securely via interfaces such as skills and MCP (Model Context Protocol)

• Scale your event-sourced architecture to handle the high read/write demands that agentic workloads generate

• Support enterprise AI governance by maintaining a complete, tamper-evident record of every agent interaction and decision

Who Should Attend

Whether you are a business leader evaluating AI agent strategies or a technology leader responsible for enterprise architecture, this session offers essential guidance for building AI agents that operate as true domain experts — grounded in the historical record and continuous evolution of your organization.

If you are working on agentic AI initiatives and need your agents to move beyond stateless reasoning, this session is for you.