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TL;DR: AI Patterns for Enterprise Automation
The Core Idea: Building production AI systems requires breaking complex business processes into four pattern categories rather than treating AI as a monolithic solution.
The 4 Pattern Categories
- UI/UX: Chat isn't always best. Embedding AI in existing workflows often delivers more value
- Triggers: How AI gets invoked (human prompts, events, schedules, or other AI agents)
- Workflow: How tasks connect (sequential, parallel, loops, dynamic planning)
- Execution: Mix deterministic tasks (eg. API calls) with creative ones (eg. content generation)
Key Insights
- Chat interfaces fail at scale for high-volume transactions and compliance work
- Agent-to-agent communication (like microservices for AI) enables horizontal scaling
- AI review loops can get content to 90% ready, reducing human effort from hours to minutes
- Start simple: Master sequential patterns before attempting self-planning AI
The Payoff
Small, focused AI agents working together outperform monolithic systems. Map your processes to these patterns, start with the simplest implementation that works, then scale.
Bottom line: The gap between AI demos and production isn't just technology—it's about choosing the right patterns for your business needs.
