Define how AI participates in software delivery
A working session for engineering organisations adopting AI-assisted delivery. We map where AI enters your lifecycle, what evidence each stage requires and how AI-generated work is tested, reviewed and released.
You leave with
FacilitatorBalaji Nagaraj Kumar · VP, AI Engineering, moringGet started
Tell us the workflow and the constraint. We reply within one business day.
A delivery lifecycle your reviewers can sign off.
A map of where AI enters delivery
Which stages AI participates in today, which are candidates next and which stay human-owned.
Review gates for AI-generated work
What has to be true before AI-generated changes move from one stage to the next.
A test and evaluation strategy
How AI-generated work is tested, what is evaluated automatically and what a human has to confirm.
Release and rollback controls
How changes reach production, what is recorded and how they are reversed when verification fails.
Metrics that reflect delivery, not activity
What you measure to know whether AI-assisted delivery is actually working.
A sequence for the first two quarters
Which changes to make first, what they unblock and who owns each one.
Engineering leadership plus the people who run the pipeline.
The session needs the people who set delivery standards and the people who operate the tooling those standards run on.
Engineering leadership
Owners of delivery standards, review policy and the definition of done.
Platform and DevEx
The teams who run CI, environments, artefact promotion and developer tooling.
Quality and test engineering
Owners of test strategy, coverage expectations and release verification.
Security and compliance
Owners of code provenance, dependency policy and audit requirements.
Turn the conversation into a practical plan
The session is built around your operating environment, the work you want agents to perform and the controls your organisation requires.
Set the objective
Define the workflow, platform requirement or delivery challenge you want to address.
Map the environment
Identify the systems, data, teams and policies involved.
Design the approach
Define what the agents will do, how they will be governed and how the solution reaches production.
Confirm what happens next
Identify the owners, dependencies and sequence required to move into implementation.
What we work through in the session
The delivery lifecycle as it runs today
Stage by stage, including where work waits, who reviews it and what evidence is produced.
Verification of AI-generated work
What is tested automatically, what is evaluated, and what a named human confirms before release.
The path to production
Promotion, release controls, rollback and the record that shows what shipped and why.
Put AI-assisted delivery on a reviewable footing
Bring your current lifecycle. Leave with the gates, the evidence and the sequence to change it.