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

This post reframes the build-vs-buy calculus for AI agents, emphasizing that they are living systems requiring continuous engineering investment beyond initial production deployment. It details the hidden costs of building in-house, including ongoing engineering time for workflow updates, RAG pipelines, evaluation, safety, latency, and monitoring; compounding performance gaps; operational risks like on-call rotations and incident response; and the expanding infrastructure footprint for GPU orchestration, inference, vector databases, and observability. The post advocates for retaining control over differentiating layers (workflows, domain logic, brand voice) while offloading non-strategic infrastructure tasks. It critiques service-heavy vendor models and promotes a product-first approach with features like Agent Operating Procedures (AOPs), simulations, agent versioning, Watchtower, and Diagnostic Tools to enable rapid iteration and continuous improvement.