AI coding agents can write code, modify files, and report that a task is complete.
But a successful-looking response doesn't necessarily answer some important questions:
These questions led me to build IntentChord.
IntentChord is a local-first, human-governed development workflow that connects an owner's intent to workspace context, confirmed scope, agent-specific execution contracts, evidence-based review, and a final human decision.
Its central principle is simple:
An AI agent's report is not the same as verified execution evidence, and an agent's claim of success is not the owner's approval.
The repository includes:
The project is still evolving and is not presented as a production-certified system.
I've made the source public because I'd like to develop IntentChord with other developers.
I'm particularly interested in contributions involving code quality, testing, developer onboarding, architecture discussions, and improvements to the human-in-the-loop workflow.
There are already beginner-friendly and documentation tasks available in GitHub Issues.
**Repository:** [https://github.com/hesham-m/IntentChord](https://github.com/hesham-m/IntentChord)
**Contribution opportunities:** [https://github.com/hesham-m/IntentChord/issues](https://github.com/hesham-m/IntentChord/issues)
The project is source-available under PolyForm Noncommercial 1.0.0, rather than an OSI-approved open-source license. Please review the repository's license and contribution guidelines before contributing.
I'd be interested to hear how other developers handle agent authority, scope changes, and evidence verification in their AI-assisted development workflows.
What would you improve or challenge in this approach?