Elizabeth Fuentes Leone argues that larger context windows alone do not solve agent reliability. Using an application-log retrieval example, she distinguishes externalizing large data, selecting relevant information, compressing conversation history and isolating context between agents.
The talk walks through Strands Agents conversation-management strategies, then distinguishes short-term conversation history, long-term vector memory and graph-based relational memory. Memory pointers keep bulky tool results in storage while allowing an agent to retrieve the underlying data when it is actually needed.
For multi-agent workflows, she recommends sharing references rather than flooding every agent with the same context. She also demonstrates explicit tool-call limits and asynchronous job-status tools to prevent unbounded loops or long external requests from blocking an agent.
Her closing cautions cover context stuffing, lossy summarization and unnecessary context propagation. The practical emphasis is on supplying the information a task requires while retaining clear tool responses and controlled execution.
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