This webinar is for AI Engineers and Developers who are currently building and shipping agent-based systems, and who have hit the wall where prompt engineering and a single LLM call stop being enough.
Agent harness implementations are inherently complex. They span multiple interdependent components including model routing, tool orchestration, context management, evaluation loops, and persistence. Among these, memory is not optional. It is the non-negotiable substrate that separates impressive demos from agents that hold up in production. Without a deliberate memory layer, agents forget user preferences across sessions, lose task state mid-execution, repeat expensive tool calls, and drift further from intent the longer they run. With one, they compound context, learn from prior runs, and behave consistently at scale.
In this session, you will learn what actually makes up an effective agent harness, how to design one for your specific use case, why memory sits at its architectural core, and which Oracle primitives map cleanly onto each component of the stack. We will cover concrete implementation patterns, the trade-offs between storage backends, and how to evolve a harness as your application matures from prototype to production.
You will walk away knowing how to:
- Integrate Oracle AI Agent Memory into your existing agent harness, regardless of your framework of choice (LangChain, LlamaIndex, custom, or otherwise)
- Design a hybrid memory substrate that combines database and filesystem layers, and understand when to reach for each
- Implement harness patterns matched to distinct application modes and use cases, from single-turn assistants to long-running autonomous agents
Duration: 1 hour
