# CircleCI Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/circleci
> Markdown URL: https://aitinkerers.org/technologies/circleci.md
> Technology record last updated: 2026-03-05T10:09:36Z
> Generated: 2026-09-20T17:48:58Z

CircleCI is the industry-leading Continuous Integration/Continuous Delivery (CI/CD) platform, automating software build, test, and deployment with intelligent, scalable pipelines.

CircleCI provides a powerful CI/CD platform, automating the entire software development lifecycle from commit to delivery. The platform operates on a 'Configuration as Code' model, where the entire process is defined in a declarative `config.yml` file within the repository's `.circleci` directory. It integrates directly with Version Control Systems (VCS) like GitHub and Bitbucket, triggering a **Pipeline** on every code change. This Pipeline orchestrates **Workflows** to coordinate parallel **Jobs**, running steps like unit tests, security scans, and deployments across Docker, Linux, macOS, or Windows executors. Developers leverage features like caching, parallelism, and reusable configuration **Orbs** to achieve faster feedback cycles and confidently ship quality code at scale.

- Official technology site: https://circleci.com
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Lessons Learned Building CircleCI's first production agent](https://sf.aitinkerers.org/talks/rsvp_B0cEB9hQsbM)

CTO and co-founder at CircleCI

- Event context: AI HyperEngineering: Claude/Amp Maxxing, Background Agents, CI/CD — 2025-08-22 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_B0cEB9hQsbM

### [Agent testing with transactions](https://seattle.aitinkerers.org/talks/rsvp_l7Lv36QkvdA)

As the agent logic and graph becomes more complicated, the need of establish a stable baseline to iteratively add capabilities and improve quality of agents is critical to those who are like us at Otto building AI agent to do transactions. We take a similar approach as the one proposed in the τ -bench in this paper (https://arxiv.org/pdf/2406.12045) that’s probabilistic oriented rather than deterministic oriented, yet focusing on testing our own scenarios rather than a benchmark of general capability. Would like to share some early learnings and thoughts around stability, setup and point of testing after testing a handful of choices (Pytest vs Puppeteer/Selenium, Github Action vs CircleCI vs Azure DevOps, end-to-end testing vs langGraph node testing).

- Event context: AI Tinkerers Seattle - December 2024 Meetup — 2024-12-12 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_l7Lv36QkvdA

## Related Technologies

- [GitHub Actions](https://aitinkerers.org/technologies/github-actions) ([Markdown](https://aitinkerers.org/technologies/github-actions.md)) — 20 public demos
- [LLMs](https://aitinkerers.org/technologies/llms) ([Markdown](https://aitinkerers.org/technologies/llms.md)) — 83 public demos
- [MCP](https://aitinkerers.org/technologies/mcp) ([Markdown](https://aitinkerers.org/technologies/mcp.md)) — 129 public demos
- [Puppeteer](https://aitinkerers.org/technologies/puppeteer) ([Markdown](https://aitinkerers.org/technologies/puppeteer.md)) — 7 public demos
- [Pytest](https://aitinkerers.org/technologies/pytest) ([Markdown](https://aitinkerers.org/technologies/pytest.md)) — 6 public demos
- [Selenium](https://aitinkerers.org/technologies/selenium) ([Markdown](https://aitinkerers.org/technologies/selenium.md)) — 7 public demos