But in real-world projects, some basic questions remain:
* What plan did this change follow?
* Was the intent reviewed before execution?
* What evidence do we have beyond “it works”?
* When something fails, does the lesson get captured structurally?
AgentTeams adds a lightweight governance layer on top of AI-driven development.
Instead of just marking tasks as “Done”, it creates a traceable execution chain:
1. Plan
Every task starts as an explicit plan.
Priority, risk notes, and review status are recorded before execution.
2. Completion Report
When work finishes, a report is generated automatically.
It includes:
* Number of files changed
* Execution time
* A quality score (e.g., 82 / 95)
* Verification summary
3. Post-mortem
If something goes wrong, root cause and preventive actions are recorded and linked back to the original plan and conventions.
On the dashboard, you can see in real time:
* How many plans are in progress
* How many are waiting for review
* How many were completed in the last 7 days
* How many reports failed or were partially completed
* How many post-mortems are still open
The goal isn’t automation.
It’s accountability and traceability for AI work.
AgentTeams is currently in beta.
If you’re running AI-assisted development workflows, I’d love critical feedback from real usage.
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Show HN: AgentTeams – Traceable AI coding workflows · HackerTrans
But in real-world projects, some basic questions remain:
* What plan did this change follow? * Was the intent reviewed before execution? * What evidence do we have beyond “it works”? * When something fails, does the lesson get captured structurally?
AgentTeams adds a lightweight governance layer on top of AI-driven development.
Instead of just marking tasks as “Done”, it creates a traceable execution chain:
1. Plan Every task starts as an explicit plan. Priority, risk notes, and review status are recorded before execution.
2. Completion Report When work finishes, a report is generated automatically. It includes:
* Number of files changed * Execution time * A quality score (e.g., 82 / 95) * Verification summary
3. Post-mortem If something goes wrong, root cause and preventive actions are recorded and linked back to the original plan and conventions.
On the dashboard, you can see in real time:
* How many plans are in progress * How many are waiting for review * How many were completed in the last 7 days * How many reports failed or were partially completed * How many post-mortems are still open
The goal isn’t automation. It’s accountability and traceability for AI work.
AgentTeams is currently in beta. If you’re running AI-assisted development workflows, I’d love critical feedback from real usage.