Most agent frameworks still need you in the loop. This one is designed so you configure it once and walk away.

Most AI agent tools today are interactive. You stay in the driver’s seat — approve the tool call, review the diff, confirm the action. That’s useful for hands-on work, but it leaves a big gap: the long tail of recurring background tasks (research digests, monitoring, PR reviews, security scans, briefings, etc.).

Aeon takes the opposite approach. It’s an open-source autonomous agent framework built around the idea of “configure once, forget forever.”

Key design choices:

- Zero infrastructure — It runs entirely on GitHub Actions. Fork the repo, set up aeon.yml + secrets, and the scheduler handles the rest. Public repos get free minutes.

- Skills are just Markdown files — No plugin SDK or compile step. A skill is frontmatter + a prompt. The agent reads it at runtime. There are dozens of built-in ones (research, monitoring, code review, deploys, self-improvement, etc.) and you can write your own by writing a prompt.

- True unattended operation — Scheduled runs, persistent memory across runs, reactive triggers, and quality scoring after every execution.

- Self-healing loop — Outputs get scored. If a skill fails repeatedly, a repair skill diagnoses and patches it. There’s also a heartbeat that audits the whole fleet.

- Identity + direction files — SOUL.md (voice/worldview) and STRATEGY.md (north-star metric + priorities) act as the agent’s permanent context so every skill stays aligned without constant prompting.

It positions itself as the framework for the work you want done while you’re not there , rather than another interactive coding assistant.

Repo: https://github.com/aeonfun/aeon Site: https://www.aeon.fun X: u/aeonframework

Curious what people here think about the trade-offs of fully unattended agents vs. the more common human-in-the-loop designs. Has anyone experimented with similar “set it and forget it” setups, or do you prefer keeping tighter control?