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Building AI Products–Part I: Back-End Architecture
—Part I: Back-end Architecture In 2023, we launched an AI-powered Chief of Staff for engineering leaders—an assistant that unified information across team tools and tracked critical project developments. Within a year, we attracted 10,000 users, outperforming even deep-pocketed incumbents such as Salesforce and Slack AI.
Not only did this impedance mismatch cause a lot of pain while writing and maintaining the code, but agentic systems are so far away from the ubiquitous 12-factor model that attempting to leverage existing microservice tooling became an exercise in fitting square pegs into round holes. Agents naturally align with OOP principles: they maintain encapsulated state (their memory), expose methods (their tools and decision-making capabilities via inference pipelines), and communicate through message passing. We started with the naive approach most agentic systems use: running every message through an inference pipeline to extract context, make decisions, and take actions via tool calling.
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