Avoiding AI Debt as a Team

TL; DR: Reusing Agile Artifacts to Avoid Accumulating AI Debt

In this video from the 76th Hands-on Agile Meetup, I walk you through the A3 Delegation System and show how it helps avoid AI debt by borrowing artifacts and practices from Agile, such as the Definition of Done and Retrospectives. 

What the Video on the A3 Delegation System Addresses

1. AI Debt and the Delegation Lifecycle: The delegation, documentation, and ownership failures you already know from Agile reappear the moment an AI workflow runs unattended and undocumented, and the six-stage Delegation Lifecycle gives you a place to catch each one before it reaches a customer.
2. The A3 Framework, Assist, Automate, Avoid: Every task lands in one of three boxes, Assist (AI drafts, you own the outcome), Automate (AI executes on explicit rules with an audit cadence), or Avoid (work that stays human because failure damages trust), and the real risk is Assist quietly drifting into Automate because the output has looked fine for four weeks.
3. Model Routing by Cost Tier: Good enough is a deliberate decision with an owner, not a default, so you match each task class to local, mid-tier hosted, or frontier models by cost and stakes, and you keep proprietary process data out of the big labs because that data is your actual moat.
4. The AI Definition of Done per Task Class: One Definition of Done for everything works in Scrum, but breaks under AI, so you write a separate one for each task class across four levels: verification, provenance disclosure, data hygiene, and sufficiency tier.
5. The Delegation Audit and Governance Trail: Drift happens slowly, and approving a draft in four seconds is rubber-stamping, not reviewing, so you run a monthly audit for output drift, sufficiency drift, reversibility, and review creep, and the artifacts you create along the way answer any CFO, CTO, or procurement due diligence question without extra reporting work.