AI agents fail in predictable, expensive ways — and most Python developers don't discover them until production. This talk dissects three research-validated failure modes that cause agents to waste tokens, get stuck, and run up costs, with live demos and concrete Python fixes for each.

The three failure modes:

Context Window Overflow — Large tool outputs (214KB of logs) flood the context window and crash the agent. Fix: a Memory Pointer Pattern that reduces token usage by 7x (validated against IBM Research).

MCP Tools Not Responding — Slow external APIs cause 424 errors and block your agent indefinitely. Fix: an async handleId pattern that drops response time from 17.2s to 1.7s (validated against Octopus research).

Reasoning Loops — Agents call the same tool repeatedly with identical parameters without making progress. Fix: a Debounce Hook that detects and blocks duplicate calls in a sliding window (validated against The Decoder).

Each failure mode follows a "problem → research → live demo → solution" arc. All demos use open-source Python tools (Strands Agents, FastMCP, asyncio) and are available as runnable notebooks in a companion repository. The patterns are framework-agnostic — attendees can apply them to LangChain, CrewAI, AutoGen, or custom agent builds.

Aimed at intermediate-to-advanced Python developers building or evaluating AI agent systems who want to prevent the most common runtime failures before they hit production.