Articles
Guides, engineering notes, and honest comparisons on coordinating multi-agent coding teams.
Use case
- Use case
Cross-machine coordination
Run AI coding agents on different developers' machines against one master. Coordination happens at the intent layer while plans form, not at merge time.
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Intent-level conflict detection
Catch coordination failures before code is written. The master matches each plan to concepts and flags overlap at the intent layer, not at the diff.
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Semantic concept catalogue
Concepts replace files as the unit of meaning. A living, domain-tagged registry the master owns and evolves with every accepted intent.
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Plan-stage incident response
Freeze a swarm of agents mid-flight, replay the intent timeline, and revert across machines, not just inside one repo.
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Living shared world model
New agents read the concept catalogue, not the whole codebase. Tribal knowledge becomes a structured registry that ranking keeps honest at scale.
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Audit-grade by construction
Every line of agent code traces back to a signed intent. Verdicts and tool calls land on a timeline you can replay and export.
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Comparison
- Comparison
Tetherlab vs git worktree per agent
Git worktrees give each agent its own working directory and prevent file clobbering. They do not coordinate intent or cross machines. Here is where each fits.
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Tetherlab vs Cursor 2.0 multi-agent
Cursor 2.0 runs up to eight parallel agents using git worktrees or remote machines. Tetherlab coordinates agents across machines at the intent layer. They solve different problems.
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Tetherlab vs Augment Intent
Augment Intent orchestrates agents from a spec with a coordinator, implementors, and a verifier. Tetherlab coordinates around a persistent shared concept registry. A careful, sourced comparison.
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Tetherlab vs CodeRabbit
CodeRabbit reviews pull requests after code is written. Tetherlab catches conflicts before a line is written. Different stages, and they coexist well.
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