Case study / 03
Turn an agent idea into an engineering-ready blueprint.
A guided product that structures capabilities, data, permissions, risks, tests, and delivery work before implementation.
- Role
- Product Architect & AI Engineer
- Scope
- Product strategy · Generation workflow · Data contracts · Human review
- Status
- Working prototype
- Year
- 2026
01 / CONTEXT
The problem
Convert ambiguous agent requests into specifications teams can estimate, review, test, and secure.
02 / RESPONSE
The response
Progressive clarification feeds a typed Django and Pydantic pipeline. Provider abstraction, deterministic tests, and human review keep output inspectable.
My contribution
My scope covers the product flow, generation workflow, data contracts and human review. A typed blueprint connects the Next.js workspace with the Django and Pydantic pipeline. Provider abstraction keeps cloud and local models behind one boundary; deterministic generation supports repeatable checks. Human review keeps unresolved permissions, data boundaries and delivery risks visible before engineering handoff.
Evidence & limitations
AgentForge is a working prototype. The intake, workspace, review and integration screenshots document the interface. They demonstrate the product flow, but do not establish generation accuracy, production adoption or measured time savings. Blueprint outputs still require review before implementation.
Case study updated:
03 / EXPERIENCE
Product flow
- 01
Describe
Capture the agent goal and context.
- 02
Clarify
Resolve actors, data, and boundaries.
- 03
Generate
Build a structured, scored blueprint.
- 04
Review
Validate decisions and unresolved risks.
- 05
Export
Hand engineering an actionable package.
04 / PRODUCT
Product evidence
05 / SYSTEM
System architecture
A typed blueprint is the contract between discovery, generation, review, and export.
06 / TRADE-OFFS
Architecture decisions
Structured generation
Pydantic contracts produce reviewable artifacts.
Provider abstraction
Cloud and local models share one boundary.
Human validation
Automation stops before engineering commitment.
07 / QUALITY
Security & reliability
- Deterministic mode makes generation testable.
- Quality scoring surfaces incomplete sections.
- Audit records preserve decisions.
08 / EVIDENCE
Outcomes & evidence
- A shared artifact connecting intent and delivery.
- Permissions and data boundaries become explicit early.
- Exports support planning, testing, and handoff.
09 / STACK