The Context Engineering Flywheel: Practical Patterns for Reliable Agents - Live Coding with Oracle AI Database

Most LLM apps fail for the same reason: context. Not the prompt — the selection, structure, and lifecycle of the information you feed the model every turn. In this code-heavy, hands-on webinar, we’ll define context engineering and agent harnesses, then implement a set of practical design patterns you can reuse to build more reliable agentic LLM applications.

We’ll walk through the Context Engineering Flywheel end-to-end:

• Context retrieval/selection: choosing the right signals (memory, tools, business data) for the task
• Context organization: structuring knowledge into usable “memory units” with metadata
• Context composition: assembling prompts + retrieved context into repeatable templates
  
• Context reduction: summarization/compaction strategies to prevent drift and keep costs down
  
• Context offloading: persisting state, decisions, and traces outside the context window
  
• Sandbox execution: safely running code as part of the agent workflow and feeding results back into context
  

Along the way, we’ll show how a database can act as the Memory Core for these patterns—supporting storage, retrieval, indexing, and lifecycle management—using Oracle AI Database as the foundation for the application. You’ll leave with concrete definitions, working code, and a blueprint for building agents that stay grounded, consistent, and production-ready.

Duration: 1 hour