Orbit: maximize throughput with minimal oversight
I am a lazy solo software engineer who works primarily on weekends.
I have a distaste for repetitive work, operational friction, and disorganization, so I try to minimize these and make the best use of my time. Many of the tools I have built started with one of these pain points.
Orbit is the one I use most, and those pain points sit at the core of its design and philosophy.
If you are a solo engineer commanding multiple agents and looking to optimize your workflow, this post is for you.
Delivery philosophy: optimistic merging
Here is the problem with a solo engineer and a pre-merge review gate: the reviewer is me, and I can only review so much at a time.
Every pull request waits for me, so the queue stalls the moment I step away. Ten agents working in parallel does not help when one human is the bottleneck.
So I moved review after the merge. Agents land work as soon as it is ready. Code review, QA, and security audits run afterward as automated tasks, and routines turn CI failures into new tasks. Anything they find goes back into the backlog. Work keeps flowing, and I review in batches when I am at the keyboard.
Merging is separate from releasing. I control when releases go out, so I can accept temporary defects on my integration branch.
Orbit's delivery philosophy fits in one sentence: land fast, detect fast, and fix fast.
Everything below exists to serve one of those three.
Orbit machinery
Task management
Task management is Orbit's first and central feature. At work, we have Jira and Linear, but these are not designed around solo engineers. Orbit fills this gap.
Tasks are stored locally as files and can be published to a Git repository. Each task captures what to do, why, and how, preserving a history of the intent behind the code.
Land fast: workflows
I use just two workflows day to day.
Drain the backlog
orbit run auto --for 5h --concurrency 10 --complete
This drains the backlog for five hours with up to ten agents working concurrently. The --complete flag merges each pull request as soon as it is ready.
Plan task execution
orbit run task-pilot
A cheaper model, such as Luna, identifies the files each task is likely to modify and records them in its context_files. This helps minimize merge conflicts. The workflow can also run periodically through the task-pilot routine.
Detect fast: auto-tasks
An auto-task is a reusable task created on a schedule or when a condition is met, e.g. every hour or after every three completed tasks. Any repeatable work is a good candidate for an auto-task. Some of Orbit's defaults:
delivery-code-review: runs a code review after every N tasks land.code-review: runs code reviews on a schedule.qa-sweep: validates recently completed features on a schedule.security-review: audits the codebase on a schedule.
Together, these provide post-merge review. They read what just landed and file what they find as new tasks.
Fix fast: orchestrator agent
Those new tasks still need someone to triage them, assign the right agents, hand them out, and deal with whatever gets stuck. That used to be me.
In my experience, AI models such as Astra and Opus can do this with minimal supervision. I open a session, give it Orbit's tools, and hand it the backlog. It assigns tasks, monitors progress, and resolves conflicts and issues on its own.
This lets me focus on high-level planning and decision-making while the agents handle the execution details.
Case study 1: Labor Day
On Labor Day, September 7, 2026, Fable reviewed the codebase and filed 140 defects as tasks. I started a backlog run and handed orchestration over to Astra.
By midnight, a crew of six different models had produced 234 merged pull requests, with 34 merging in the busiest hour.
That covers "land fast." Here is the rest of the ledger, pulled from the Orbit store and GitHub afterward:
| What happened | Count |
|---|---|
Tasks that reached done | 262 |
| Tasks rejected as low value or false alerts | 42 |
| Rebase conflicts | 4 |
| Tasks that needed a second run | 22 |
| Landings blamed for a regression that same evening | 31, all fixed before midnight |
| Regressions found in the following week | 1 |
| Pull requests reverted | 0 |
| Agent time | 75 hours across 347 invocations |
Note that the task completions and merged pull requests do not imply a one-to-one mapping, due to auto-tasks such as code-review.
This approach trades compute for my time. The run consumed 75 agent-hours; whether that trade is worthwhile depends on the value of the time saved.
Case study 2: an agent on-call
On the night of September 11, I handed the Orbit workspace to Opus and went to bed.
The mandate: keep the backlog moving, keep CI green, and be fully ready for release by the morning.
| What happened overnight | Count |
|---|---|
| Pull requests merged | 55 |
Tasks that reached done | 72 |
| Duplicate tasks rejected | 16 |
| Times the integration branch went red | 4, each repaired within an hour |
| Agent time | 17 hours across 121 invocations |
The morning handoff was a written note: what changed, what was restored, what was still unproven. As a bonus point, it had found and patched several critical bugs overnight.
Final thought
If you are a solo engineer, Orbit is built for it: coding agents doing the work, and less of your time spent managing them.
It's free and open-source. Try it on GitHub.