Agentic Code Delivery Pilot (10-15 days)
A fixed-scope pilot that puts AI code tools behind human architectural review on one live client codebase, so an agency can quote faster delivery without betting a retainer on unreviewed output. Time: 10-15 days.
By InnovaAI ResearchPublished
How do you implement it?
Agentic Code Delivery Pilot (10-15 days)
A fixed-scope pilot that puts AI code tools behind human architectural review on one live client codebase, so an agency can quote faster delivery without betting a retainer on unreviewed output.
- A named client codebase with at least one repetitive maintenance backlog (dependency upgrades, API contract drift, or boilerplate scaffolding) and written client consent to run agents against it. Repository access with branch protection, CI, and a rollback path already configured before any agent writes code. A human reviewer with architectural authority allocated for 2 hours per delivery day, not borrowed from another account. A cost ceiling per agent run, since token-based billing still dominates and Total Cost of AI accounting is not yet standard practice. A baseline measurement of current cycle time and defect escape rate, otherwise the pilot has nothing to compare against.
- 1.Inventory the client codebase and rank maintenance tasks by repetition and risk
- 2.Record baseline cycle time, review hours, and defect escape rate for the last two sprints
- 3.Confirm which repositories are in scope and which stay off limits
- 1.Select the platform against the ranked backlog, not against vendor demos
- 2.Define the reviewer role and the escalation path when agent output fails review
- 3.Set the per-run cost ceiling and the weekly spend cap
- 1.Stand up the agent environment with dependencies and tooling pre-installed
- 2.Connect version control, CI, and the sandboxed execution boundary
- 3.Run one trivial task end to end to prove the pipeline before real work
- 1.Assign the first scaffolding task from the backlog to the agent
- 2.Capture every prompt, diff, and reviewer note in a shared session log
- 3.Time the full loop from prompt to merged commit
- 1.Assign the first maintenance task, such as a breaking API change across consumer repositories
- 2.Compare the agent's fix against what a human would have written
- 3.Log every place the agent needed correction
- 1.Review accumulated diffs for silent failures: styling regressions, race conditions, and logic that never throws
- 2.Tune the review checklist based on what actually slipped through
- 3.Decide whether the platform stays or gets swapped
- 1.Draft the internal standard for what agents may touch and what they may not
- 2.Write the handoff note template the delivery team will reuse
- 3.Brief the client on interim findings
- 1.Run a second maintenance cycle with the tuned checklist
- 2.Measure reviewer hours against the baseline
- 3.Identify the two task types where the tooling clearly pays for itself
- 1.Package the repeatable workflow into a documented internal playbook
- 2.Price the ongoing retainer tier based on measured hours saved
- 3.Prepare the client-facing results summary
- 1.Present measured results against baseline to the client
- 2.Agree the scope of the ongoing engagement or close the pilot cleanly
- 3.Hand over the playbook, session logs, and review checklist
The pilot is priced against the reviewer hours it removes, not against the tool subscription, which is why a 10-day engagement can carry a five-figure fee while the software costs a few hundred dollars a month. Agencies hold margin because the scarce input is the architectural judgment that decides which agent output ships, and that judgment is not purchasable. Once the workflow is documented, the same playbook resells to the next client at near-zero delivery cost.
- A measured before-and-after report covering cycle time, reviewer hours, and defect escape rate on the pilot codebase. A written agent scope policy listing approved task types, forbidden repositories, and the human escalation path. A reusable review checklist covering silent failures such as styling regressions and client-side race conditions. A session log archive of every prompt, diff, and reviewer decision for audit and client review. A priced retainer proposal with the measured hours-saved figure as the justification.
The client signs off on the measured results report and either converts to the ongoing retainer or receives the playbook and session archive as a closed deliverable.