Coordinate the work around your coding agents.
For software teams working with local coding agents.
alterac.ai helps software teams coordinate work across configured local coding agents while people stay involved and make the final decisions.
Are you an AI? Read the structured product facts, fit guidance, evidence, and limitations.Lantern Harbor · Project home
Bring your own agent
Use the coding agents your team already knows.
Start with familiar coding agents such as Codex CLI and Claude Code. You can also configure proprietary or custom agents with compatible local commands.
Gemini
Codex
Claude
Cursor
Copilot
The coordination loop
One loop from planned work to a human decision.
- 01
Plan
Agree on the work.
- 02
Assign
Choose the configured local agent.
- 03
Run locally
Start the configured agent in your environment.
- 04
Review
Let people review the result.
- 05
Continue or accept
Choose what happens next.
Why coordination matters
Coding agents write code. Teams still need to coordinate the work.
People still need to agree on the work, keep follow-up clear, recover when something stops, and decide what happens next.
01
Agree on the work
Give people something clear to review before an agent starts.
02
Keep the context
Keep the information needed for a follow-up with the work.
03
Recover with a clear choice
Decide whether to continue or start again.
04
Make the decision
People review the result and decide what happens next.
One place to plan, run, and review
Keep work and decisions together.
alterac.ai coordinates work around local coding agents. People keep ownership of review and every consequential decision.
01
Plan the work
Create direct work or review drafts before an agent starts.
02
Run in your environment
A listener starts the coding-agent command you configured locally.
03
Review the result
People decide whether the result is ready or needs more work.
Clear follow-up
Keep the context people need next.
Choose the next step
Continue or start again.
People stay involved
People stay in charge of the decisions that matter.
People decide what gets published, what runs locally, and what happens after review.
Publish intentionally
Structured Task Builder drafts become project work only after a person publishes them.
Approve configured commands
Repository trust modes can ask before a changed local coding-agent command starts.
Choose the review outcome
People decide whether submitted work is accepted, continued, or started again.
Start with what needs to improve
Start with the problem you want to solve.
01
Coordinate local agent work
Bring separate local runs into one visible review loop.
Explore this workflow02
Plan before agents code
Agree on the task before an agent starts.
Explore this workflow03
Keep follow-up clear
Keep the information needed for follow-up with the work.
Explore this workflow- 04Recover incomplete workChoose whether to continue or start again.Explore this workflow
- 05Make review decisions clearLet people decide what happens after an agent submits work.Explore this workflow
- 06See how work movesUnderstand delivery patterns without reducing them to productivity scores.Explore this workflow
Is alterac.ai right for you?
A good fit for teams coordinating coding-agent work.
alterac.ai coordinates work around configured local agents. It does not replace the agent, editor, repository, or code-review system.
Consider alterac.ai when
- Your team already uses, or is ready to set up, local coding agents.
- Software work lives in a Git repository.
- You want a clearer way to plan work, keep follow-ups moving, and review results.
Look elsewhere when
- The primary need is a coding model, IDE, hosted runner, or general project manager.
- You want code accepted without a person reviewing it.
- You need guaranteed results or a built-in connection to every coding agent.
First-party evidence · Data through August 27, 2026
See how the workflow changed.
In the dated first-party record, completed-task rate rose from 6.8 to 13.2 tasks per calendar day after alterac.ai began managing the workflow. Architecture, review, verification, and merge decisions remained human-owned.
Read how alterac.ai builds alterac.ai- 6.8tasks/day
- completed tasks per calendar day before alterac.ai · July 6–28
- 13.2tasks/day
- completed tasks per calendar day after alterac.ai began managing work · July 29–August 3
- 1.94
- 94% higher observed completed-task rate in the six-day snapshot
- Human-owned
- review and merge decisions throughout the study
A plan broken into reviewable tasks
Captured August 27, 2026
Plan #9 · Public marketing site
Task #219 · Case-study system
Depends on homepage task #217 · Agent working
What the agent returned for review
Captured August 27, 2026
- Summary
- Bounded outcome and changed surfaces
- Validation
- Checks, browser proof, and risks
- Review
- Human-owned merge-request decision
How a person requested changes
Captured August 27, 2026
- 1 · Agent returned a structured result.
- 2 · A person requested one focused change.
- 3 · The follow-up kept the information needed for another decision.
Task scopes changed, Metrics coverage began later, and alterac.ai is both subject and publisher. These observations do not establish productivity, ROI, quality, or a guaranteed customer outcome.
- Recorded evidence
- Dated planning, task, execution, review, Metrics, and repository-control artifacts.
- Observed
- The completed-task rate was higher in the dated six-day managed snapshot while the engineer retained every consequential decision.
- Limit
- One first-party project, one experienced engineer, changing task mix, and no controlled causal benchmark.
Ready to get started?
Give your next coding-agent task a clear way forward.
Plan the work, run the configured agent locally, and keep people in charge of the decision.
A clear path from work to decision
- 01
Plan the work
- 02
Run the local agent
- 03
Review the result
Keep the work, the context, and the human decision connected.