A 28% reduction in token cost without changing the underlying model immediately caught my attention, mostly because the optimization is happening somewhere that receives less attention than model selection: the agent harness.

The Strands team published benchmark results for its new agent harness, reporting 28% lower cost when using the same Claude or GPT models across six benchmarks, while maintaining similar accuracy. The implementation details are more interesting than the headline number: tool results above roughly 1,500 tokens are truncated, compaction starts after context usage exceeds 85%, and context recovery is handled inside the execution loop. Those mechanisms show how much of agent performance is now an orchestration problem rather than a model selection problem.

This feels particularly relevant with the attention around Jev from TypeSafe AI, which approaches the same problem from another direction by moving structured decisions away from general-purpose LLM generation altogether. We are starting to see the architecture around models become as important as the models themselves. The question is shifting from “which model should we use?” toward “which parts of the system actually need a general-purpose model, and how should intelligence be orchestrated around them?” Context lifecycle, model routing, typed decisions, tool boundaries, failure recovery and token economics are increasingly becoming architectural decisions in their own right.

#AI #AgenticAI #SoftwareArchitecture

https://strandsagents.com/blog/introducing-strands-harness/