Designing an AI-Assisted Project Delivery System
AI agents for task creation, structured workflows, human-in-the-loop review, and sprint execution for consulting and software delivery.
- Type
- Research
- Status
- Research
- Tags
- AI · Agents · Delivery
Problem
Consulting and software delivery move slowly because coordination is manual. Task creation, sprint planning, status reporting, quality checks — each engagement rebuilds these from scratch. AI helps in pockets, but the gains don't compound across projects.
Challenge
How do you thread AI agents into delivery workflows without removing human judgment at the gates that matter — prioritisation, scope decisions, quality sign-off?
Proposed solution
An operating system where AI agents propose tasks, plans, and quality checks, while humans approve at defined gates. The system records delivery patterns across engagements so the next project starts further down the learning curve.
Technology used
Business value
Not yet measured. Working hypothesis: 30–50% reduction in coordination overhead for a typical engagement, with quality maintained or improved at the human-review gates.
Current status
Research. Currently designing the gate model and the agent contract.
Lessons learned
AI-assisted works at task generation and quality-check stages. AI-driven does not yet work at the prioritisation stage — humans still own that call, and pretending otherwise produces plans that look right but feel wrong. Internal research finding.
Future roadmap
Cross-engagement learning, automated quality gates, pluggable agent providers, client-facing read-only views.