# Harness Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/harness
> Markdown URL: https://aitinkerers.org/technologies/harness.md
> Technology record last updated: 2026-04-29T10:21:18Z
> Generated: 2026-09-20T16:36:18Z

Harness is the end-to-end platform for automated software delivery, focusing on CI/CD, cloud cost management, and feature flags.

Harness streamlines the entire software lifecycle by integrating AI-driven pipelines with automated governance. The platform offers specialized modules like Continuous Delivery, Infrastructure as Code Management, and Cloud Cost Management to eliminate manual toil. Engineering teams at companies like United Airlines and Cisco use Harness to reduce deployment times from weeks to minutes while maintaining strict compliance through automated canary and blue/green deployments.

- Official technology site: https://www.harness.io
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Harness e Inferência: O que são, o que comem e como gerenciá-los](https://saopaulo.aitinkerers.org/talks/rsvp_h90U6SZeMDc)

Have you ever said "Good Morning to your AI?", Kimchi address the problem that you don't need to have the best model to answer you a simple Good Morning. Kimchi is an Open-source tool that allows you to manage the Harness, it gives you the control of all the aspects , since which is the best/cost efficiency model to execute every task.

- Event context: AI Tinkerers SP - Meetup de Junho — 2026-06-25 — São Paulo
- Public talk page: https://saopaulo.aitinkerers.org/talks/rsvp_h90U6SZeMDc

### [What Is Your Coding Agent Actually Searching?](https://columbus.aitinkerers.org/talks/rsvp_t4IMPq7lJnc)

I built SearchBench, a harness for running controlled evaluation rounds over coding-agent search behavior. A SearchBench round takes real bug-localization tasks, gives an agent access to a repository, and checks whether it found the files that were actually changed in the human fix. The harness compares an incumbent search strategy against one or more challengers, then writes a static evidence bundle with exact-hit, hop-distance, token-usage, failure, and report artifacts. For the demo, I’ll run a small live ablation round over three cases. I’ll reuse cached Bash/native-search results as the incumbent, then run a few IC challenger policies in parallel. The question is: when IC succeeds or fails, was the important factor anchor quality or graph lookahead? I’ll show the actual workflow: preflight cost prediction, run planning, parallel execution, actual spend, cost-prediction error, generated bundle artifacts, and a short report explaining what changed. One thing I want to show explicitly is how much information the harness can derive from a small, bounded run: exact hits, hop distance, token usage, failure modes, and the next optimization target.

- Event context: AI Tinkerers - Columbus June Meetup — 2026-06-01 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_t4IMPq7lJnc

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