Al agents are running on infrastructure built for humans. Every state check runs 9 shell commands.
Every cold start re-discovers context from scratch.
It's wasteful by design.
An agentic JSON-native OS fixes it. Benchmarks across 5 real scenarios:
Semantic search vs grep + cat: 91% fewer tokens
Agent pickup vs cold log parsing: 83% fewer tokens
State polling vs shell commands: 57% fewer tokens
Overall: 68.5% reduction
Benchmark is fully reproducible: python3 tools/ bench_compare.py
Plugs into Claude Code via MCP, runs local inference through Ollama, MIT licensed.
Would love feedback from people actually running agentic workflows.
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