#strands

The most expensive AI agent failures don't throw an error, they hide. One ran at a six-figure-a-year rate for days while every dashboard stayed green, because the signals that catch it, per-session cost and anomalies, are the ones nobody watches. Why agent loops run away, and the two cost instruments your monitoring is missing.

Strands' BedrockModel ships with prompt caching off. Two kwargs turn it on, one per-model gotcha catches you, and a 10-turn driver measures 99.9% / 99.8% hit ratios on Nova Pro and Sonnet 4.6 against an 8,156-token production system prefix. The per-call usage block proves it in seconds, not waiting on CloudWatch.

How to build the Python agent that runs inside AgentCore: Strands SDK setup, prompt caching that cuts costs by 90%, dual-model strategy, tool definitions, and AgentCore Memory integration.