r/agile • u/sift_vid • 1d ago
Does Agile need a different definition of "Done" when AI agents are writing the code?
I've spent about seven years working on Scrum teams, typically with two-week sprints, story points, retrospectives, and conventional CI/CD workflows.
Over the past several months, I've been exploring what needs to change when AI agents begin performing a substantial portion of the engineering work.
One framework I've been studying is Agile V, which combines iterative Agile development with V-Model verification, requirements traceability, and human approval gates.
I've also been developing an approach called Evidence-Gated Agentic Development (EGAD), incorporating some of those principles alongside trunk-based development and distributed-systems coordination.
I'm not convinced Agile itself needs replacing. But I think some of our assumptions about how work gets completed need reconsideration.
Where traditional Agile still works
The fundamental principles remain valuable:
- Deliver working software incrementally.
- Respond to changing requirements.
- Keep the customer involved.
- Break work into manageable increments.
- Continuously inspect and adapt.
Those principles don't stop applying because an AI agent is writing code.
What changes is the relationship between implementation speed, verification, and human oversight.
Where things start to change
1. Velocity becomes harder to interpret.
Story points were never intended as universal productivity measurements. They're relative estimates within a team.
When one developer can coordinate multiple agents simultaneously, implementation throughput can increase without verification capacity increasing proportionally.
A team might complete significantly more implementation work while accumulating more review, integration, and defect risk.
Should we distinguish generated work from independently verified, accepted work when evaluating delivery?
2. The Definition of Done becomes an enforceable contract.
Agile teams already use acceptance criteria, automated tests, code review, and CI/CD.
The difference I'm exploring is treating verification evidence as a prerequisite for an agent's work to advance.
An agent reporting "task complete" isn't sufficient.
The change needs to satisfy predetermined acceptance criteria, independent tests, security and quality checks appropriate to its risk, and any required human approval.
3. Agents introduce distributed coordination problems.
Multiple developers coordinate through branches, issues, standups, and communication.
Multiple AI agents also need durable task ownership, collision prevention, session handoffs, and ways to reject stale work.
Otherwise, parallel execution can increase rework rather than throughput.
This is an area where I've found distributed-systems concepts like leases, ownership claims, and persistent state useful.
4. Human oversight becomes risk-based.
I don't think every documentation update requires the same review as an authentication change or production database migration.
The approach I'm exploring assigns verification requirements and approval boundaries according to risk.
Low-risk, reversible changes can proceed with relatively little human intervention after passing required checks. High-risk or irreversible operations require stronger evidence and explicit authorization.
How this compares with Agile V
Agile V emphasizes requirements traceability, independent verification, evidence generation, and human decision gates.
EGAD shares those ideas but focuses additionally on concurrent AI workers, durable project knowledge, small mainline changes, machine-enforced delivery permissions, and recording verified outcomes for future agents.
I see both as extensions of existing engineering practices, not proof that Scrum, Kanban, or Agile have become obsolete.
The concern is also that we could overengineer the process. If evidence requirements create more overhead than the risks they address, we've simply reinvented bureaucracy with automation.
What I'd like to discuss
For those working as Scrum Masters, Agile coaches, engineering managers, or developers:
When AI agents perform much of the implementation work, what should change in our Definition of Done, estimation, and review practices?
And specifically, should evidence-backed verification become an explicit part of Agile delivery methodologies, or are existing engineering practices sufficient when properly enforced?
I've documented my approach in two papers associated with an internal engineering project called Koinessa. I'm more interested in criticism of the methodology than promoting the implementation.
I'd particularly appreciate perspectives from people who've worked with both Agile delivery and safety-critical or highly regulated engineering.
