As AI workflows grow, so does entropy: context bloat, stale memory, conflicting instructions, noisy tools, and degraded outputs over time. This talk introduces AI Gardening as a practical operating model for keeping agentic systems healthy: pruning context, shaping memory, isolating tasks, designing reset boundaries, and controlling prompt-state sprawl before it turns into drift and unreliability.

What would be covered:
• Why agent quality drops as context accumulates
• prompt-state sprawl and hidden coupling
• pruning, compaction, reset, and scoped-memory patterns
• context engineering vs prompt hacking
• how to keep long-running AI workflows maintainable