Multi-Agent LinkedIn Content Orchestration & Production Pipeline

• Role: Full-Stack & AI Systems Architect • Deliverables: Distributed Agent Pipeline, BullMQ Queue Workers, Webhook Orchestrator, React Visualizer • Timeline: 4 Weeks

OVERVIEW Fully automated AI publishing tools dilute brand authority by producing generic, ungrounded content. This project was built to solve the scalability bottleneck for technical founders: an autonomous 6-stage multi-agent pipeline that drafts, formats, and generates visual slide decks end-to-end while keeping a human decision-maker at the critical approval gate.

THE PROBLEM & DIAGNOSIS Single-Prompt Fragility: A single LLM prompt cannot simultaneously handle trend extraction, technical validation, tone matching, and SVG slide generation.

The Hallucination / Tone Drift Risk: Autonomous posting without human oversight creates brand liability.

Decoupled Pipeline Failures: When one task fails in a monolithic chain, the entire run collapses without restart capabilities.

KEY ARCHITECTURAL DECISIONS • Decision 01: Multi-Agent Queue Architecture Engineered six specialized agents, each deployed as an isolated BullMQ worker on Redis (Researcher, Writer, Technical Reviewer, Visual Designer, Formatter, Publisher). • Decision 02: Human-in-the-Loop (HITL) Gate Implemented a strict state machine where the pipeline pauses at the final staging phase, providing an interactive UI for editing, approval, or regeneration before publishing. • Decision 03: Automated SVG Carousel Generation Created a programmatic design agent that parses technical takeaways and generates clean, dark-mode SVG/PNG carousel slides ready for social sharing.

MEASURABLE RESULTS & IMPACT • 80% reduction in time required to produce high-authority technical slide decks. • 100% isolation of agent failures with BullMQ automated retries and dead-letter queues.

TECH STACK TypeScript, Next.js, Node.js, BullMQ, Redis, Groq API, Claude API, Tailwind CSS.