This article walks through one of them, names what the harness takes off your plate, and shows where the same code goes when you need it governed for production. No new framework to learn first. If you've written a FastAPI route, you can read every line.

The fair question to ask of anything in this space is what it saves you from writing. CUGA's answer: the orchestration around a model that you'd otherwise rebuild every time.

It plans before it acts, then executes with a mix of tool calls and generated code (CodeAct). On a long task that runs twenty steps, the thing that breaks most agents is losing track of intermediate results and re-deriving them (often wrong) on the next turn; CUGA holds that state and runs a reflection step that can catch a bad call and re-plan instead of barreling ahead. That machinery is why it has topped agent benchmarks like AppWorld and WebArena rather than something you tune by hand.

You also set the cost/latency tradeoff from config rather than code: Fast, Balanced, and Accurate reasoning modes, with code execution in whatever sandbox you trust (local, Docker/Podman, or E2B cloud). Same agent definition, different dial. That dial matters more than it sounds. Most harnesses assume a frontier model sits underneath and lean on it to recover when a plan goes sideways; CUGA does that work itself. The planning, the reflection step, the variable-tracking that keeps a long run on course — that's the harness carrying load the model would otherwise have to, which is what lets a smaller open-weight model hold up where it normally wouldn't. It's why the hosted apps run on gpt-oss-120b rather than a frontier API. Running the biggest model you can call is the usual bet; CUGA's is that a smaller open one is enough.