Designing an AI-native delivery system
Shaping a productized software-delivery journey where specialized agents help move a real request from discovery to a reviewed build.
Outcome: A staged Discovery-to-Deploy model with durable state, explicit approvals, and role-scoped AI collaboration.
Context
This product explores how software delivery changes when AI agents can participate across discovery, scoping, planning, implementation, and quality assurance. It is not framed as a chat box that writes code. It is a guided delivery system for turning an ambiguous business request into reviewed, traceable work.
Responsibility
I own the product direction, workflow design, and application architecture. That includes defining what the buyer sees, where approval is required, how agent roles cooperate, how repository context enters the process, and which state must remain durable when long-running work is interrupted.
Product model
The current journey moves through Discovery, Scope, Build, Preview, and Deploy. A project lead remains the buyer-facing owner while product-management, development, QA, and delivery roles work within narrower responsibilities. Scope approval separates problem framing from implementation, and QA follows the final development work rather than running as an unrelated parallel activity.
Architecture decisions
- Keep workflow truth in PostgreSQL; use Redis only for live coordination and projection.
- Share validated contracts across services instead of relying on prompt conventions.
- Store runner context snapshots and artifacts so work can resume after interruption.
- Treat repository access as optional during early discovery and required only when implementation needs it.
- Tie previews and releases to exact build commits so feedback applies to identifiable code.
- Use explicit callbacks, usage records, and release history rather than invisible agent state.
Outcome
The result is a concrete architecture for role-scoped AI collaboration and a buyer-readable delivery journey. It defines approval gates, ticket ownership, retry paths, repository boundaries, live previews, and release semantics without claiming traction the product has not yet earned.
Reflection
AI can shorten implementation loops, but speed alone does not make delivery trustworthy. Durable state, narrow authority, evidence, and human decisions are product features. The architecture is designed around those constraints rather than adding them after automation becomes difficult to control.
Public boundary
This remains active product work. Private repository details, credentials, operating environments, and unshipped claims are intentionally excluded.