Engineering Blog
The Value Framework in Perceptive agents
Part 2 of the Pedestal AI Knowledge Series Abstract In Part 1 we described five common challenges in enterprise AI deployments—reliability, accuracy, performance, cost and "validate"ability—and argued they share a single architectural origin: knowledge is disparate and intertwined with instructions and context, leading to attention dilution. In this second installment we examine four approaches enterprises commonly try to close the gap: long‑context prompts, LoRA‑based fine‑tuning, prompt cach
From "Task"ative agents to Perceptive agents
Part 1 of the Pedestal AI Knowledge Series Abstract Enterprise AI has a stubborn ceiling. Frontier models now handle general tasks with astonishing fluency, yet the agents built on top of them keep stalling at 70–80% accuracy on the operational work that actually runs a business. This is not a model problem; it is a knowledge problem — and specifically a problem of where knowledge lives. This post opens a three-part examination of the gap, beginning with the problem itself. We describe what we