Your AI agent does not crash; it gets stuck. It silently produces wrong results when data overflows the context window. It waits forever when an MCP tool calls a slow API. It calls the same tool 14 times because the response said "more results may be available." None of these failures throw errors. They just waste tokens and time. Three silent failures that cost real money. Context overflow: a tool returns 214KB of logs, the context window fills up, and the agent produces incomplete results with no error. MCP tools hanging: an external API takes 15 seconds and the agent gets a cryptic 424 error. Reasoning loops: ambiguous tool feedback causes 14 retries, burning tokens with zero progress. I will cover the Memory Pointer Pattern (store large data outside context, return a pointer, based on IBM Research), async handleId for MCP (return job IDs immediately, poll for results, based on Octopus Research), and DebounceHook with clear SUCCESS states that block duplicate calls (14 calls to 2). Each fix includes a live demo with before/after metrics. You'll walk away with: • Three production-ready patterns you can implement the same day • Working code with real metrics for each fix • Understanding of which failure mode is causing your agent's problems • Open-source repository with all demos Most agent talks focus on capabilities. This focuses on efficiency: what agents waste.

Outline: • Three Silent Failures • Fix 1: Memory Pointer Pattern • Fix 2: Async HandleId for MCP • Fix 3: DebounceHook + Clear States • Decision Matrix + Resources