Your AI agent just ingested 214KB of server logs. It looks like it worked. No errors, no warnings. But the response is garbage. The context window silently overflowed, critical data got truncated, and your agent confidently hallucinated an answer based on incomplete information. Context overflow accounts for a significant portion of production agent failures, and it is almost always silent. The problem is bigger than you think. Tool outputs have no size limits by default, so a single API call can return megabytes of data. Context window overflow produces no errors and no warnings, only degraded output quality. An estimated 67% of production agent failures trace back to context management issues. Multi-agent systems multiply the problem as data passes between agents without size controls. The Memory Pointer Pattern changes everything. Store large tool outputs in agent.state via ToolContext instead of returning them directly. Return lightweight 52-byte pointers that reference the stored data. Use invocation_state for shared data access across agents in multi-agent Swarm systems. Implement SlidingWindowConversationManager for automatic conversation history management. Transform 214KB payloads into manageable references without losing any data. You'll walk away with: • A working Memory Pointer implementation using ToolContext and agent.state • Multi-agent state sharing patterns using invocation_state with multi-agent orchestration • Production debugging techniques for identifying silent context overflow • Open-source demo code that processes 145KB+ log files seamlessly across multiple agents This talk does not stop at describing the problem. It provides a complete, production-tested solution with working code. Every pattern shown runs live on stage with real data.

Outline: • The Silent Killer • Memory Pointer Pattern Deep Dive • Multi-Agent State Sharing • Production Patterns • Advanced Techniques and Wrap-Up