AI agents are never done: The new build-vs-buy calculus | Decagon

What this post added

This post elaborates on the continuous engineering effort required for AI agents beyond initial production deployment. It details the technical challenges and trade-offs involved in building and maintaining AI agents, including fine-tuning models, optimizing latency, ensuring reliability, evaluating providers, and implementing safety guardrails. It frames the build-vs-buy decision within the context of ongoing engineering investment, highlighting the costs associated with engineering time, performance gaps, operational risks, and infrastructure maintenance. The post emphasizes the importance of owning differentiating layers like workflows and domain logic while offloading infrastructure concerns. It critiques service-heavy vendor models and advocates for a product-first approach that enables direct user control and visibility into agent performance, referencing Decagon's specific product features that support continuous iteration and optimization.