Case study · Agent harness

The harness I built when my framework ran out of road.

A cloud-native agent harness that matches each step to the cheapest capable model, survives provider outages, and holds no secrets. It runs Offworld in production.

Founder, Kipple Labs · Running in production, not yet publicly released

~250xCost reduction on Offworld
$0.39Daily operating cost, down from $100
AnyModel provider, hot-swappable
0Secrets held by the harness

The problem

Three walls.

I built Offworld on OpenClaw and hit three limits. Its security posture was weak, a real problem for anything long-term or enterprise. It required a local daemon, when I wanted something cloud-hosted and always on. And the interface was painful: hard setup, a limited GUI, and multi-agent work routed through Discord, where agents sitting in an open chatroom burned tokens just listening. When Anthropic's API went down, everything stopped, and all the context was stuck with it.

The approach

Match the model to the judgment the step needs.

Kaiger is named for the Kiger Mustang herd and Kiger Gorge on Steens Mountain, in the Owyhee high desert. Two mechanics carry it.

Contextual model switching

Each step runs on the cheapest model that can do it, chosen automatically by task difficulty, with manual override. Different agents run different models.

Model-agnostic hot-swapping

When a provider's API goes down, Kaiger switches models and keeps the context, so work continues.

No secrets in the harness

Credentials and enforcement live in Owyhee. Kaiger runs agents and picks models; Owyhee decides what each agent may touch, at every tool call.

The proof

The cost ladder.

Offworld started as an open newsroom: five agents on Opus in one shared room, discussing pitches and drafts. The open discussion worked. Agents caught each other's hallucinations, factual errors, and logical fallacies. It also cost $100 a day. The goal was to keep that error-catching and stop paying frontier prices for every step. Each step down came from one idea: once a task needs less judgment, it can run on a cheaper model.

StepChangeDaily cost
1Five agents in an open room, all on Opus$100
2Two-role loop: the journalist pitches, the editor challenges and assigns, the journalist researches and drafts, the editor critiques, fixes go back$50
3The narrower, more mechanical steps move from Opus to Sonnet$15
4Writing moves to an open-weights model; a deterministic script checks facts and source links, which the open model sometimes dropped or got wrong$0.39

The result

39 cents a day at the same quality bar.

The lesson I took from it: structure the work so judgment is concentrated in a few places, match each step to the cheapest model that can do it, and use plain code wherever the check is mechanical. Kaiger runs Offworld in production today and is what I build and test everything else on.

Part of the Kipple Labs stack

Offworld is the publication. Kaiger runs its agents and picks their models. Owyhee holds their credentials and enforces what each one may do. AXIS gives them an identity any company can verify.

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Owyhee AI Control Plane