Case Study 04 · Agentic AI
Agentic AI / Production Systems
Building a structured content intelligence system that connects production workflows, performance signals, evaluation, and human-in-the-loop decision making.
Role
Technical Implementer & Product Lead
Focus
Agentic Workflows · Evaluation · Automation
Status
Active system · Ongoing development
The Challenge
AI can generate content quickly, but generation alone does not create an intelligent production system. Content operations need structured state, measurable feedback, evaluation criteria, and clear decision boundaries before autonomous actions can be introduced responsibly.
My Role & Constraints
I designed and implemented the production architecture for Sibyl AI, translating an existing content workflow into a structured operational system. The approach prioritizes observable data, explicit workflow states, measurable evaluation, and human approval before expanding autonomous execution.
What I Implemented
01 / Operational Data Architecture
Built an Airtable-based operational layer that turns content production into structured records rather than disconnected creative assets. The data model provides a foundation for tracking content, workflow state, publishing activity, and downstream performance.
02 / Production Workflow & State
Structured the production lifecycle around explicit workflow states so content can move through the system consistently. This creates traceability across production and establishes clear points where automation, evaluation, or human review can operate.
03 / Performance Feedback Architecture
Designed the system so publishing and performance signals can be connected back to individual content records, creating the feedback layer required to evaluate patterns across creative decisions and future production cycles.
04 / Evaluation & Recommendation Logic
Defined an evaluation layer that can use structured performance signals to inform recommendations for subsequent content. The architecture separates observation, evaluation, and recommendation so decisions remain inspectable rather than hidden inside a single automated process.
05 / Human-in-the-Loop Controls
Designed approval boundaries into the workflow so the system can progressively support more autonomous recommendations without automatically turning every AI-generated decision into an external action. Human review remains an explicit control point for consequential production decisions.
System Architecture
The architecture creates a continuous operational loop:
Content → Structured Queue → Publishing → Performance Signals → Evaluation → Recommendation → Human Approval → Next Action
This separates content generation from decision making and provides the structured data, evaluation layer, and approval controls needed for progressively more agentic operation.
Outcome
Transformed a manual content-production process into a structured production system with a persistent operational data layer, defined workflow states, feedback architecture, and explicit human-control boundaries.
The architecture is being expanded progressively as performance data and evaluation logic mature, allowing automation to be introduced where it is measurable, observable, and appropriate.