Compounding AI Outputs: Building a Memory for Your System
· 4 min read
In the early stages of AI adoption, most teams treat AI agents as isolated tools: you give a prompt, get a result, and then the context vanishes. This "Task → Prompt → Result → Forget" cycle is inefficient because it fails to capture the intelligence generated during the interaction.
To truly leverage AI in product development, we must shift to a system where outputs are compounded. This means designing workflows where the insight from one agent becomes the context for the next, creating a shared "Memory Layer" that accumulates value over time.