Multi-Agent AI Content Pipeline Build

I built a 6-stage multi-agent AI content pipeline for a 21-client digital marketing agency that needed to scale content production without losing research quality, brand voice, or editorial oversight.

The system runs inside a Streamlit app and supports both human-only and AI-enhanced workflows, so a team can choose how much automation belongs in each project.

Brief Interpreter — turns a messy human brief into a structured JSON blueprint.

Research Collector — builds a research pack and flags anything that needs verification.

Outline Architect — creates the SEO/content structure before drafting starts.

Draft Writer — writes from the blueprint, research, and outline without inventing facts.

Voice Harmonizer — tightens the draft for brand voice, clarity, and compliance rules.

QA Reviewer — checks intent match, outline compliance, factual integrity, voice consistency, and publish readiness.

The pipeline is prompt-driven and modular. Each stage has its own system prompt, and the app exposes stage skipping so a human can rerun only the parts that need work.

The build also includes brand voice pre-flight guidance, prompt version control, JSON handoffs between agents, clean Markdown output, and QA checks for unsupported claims, placeholder leakage, and tone drift.

The result is not a one-click content machine. It is a controlled production workflow: research first, structure second, drafting third, voice alignment fourth, and QA before anything reaches a client or publication queue.

That makes the system useful for agency-scale production where speed matters, but human judgment still has to stay in the loop.