Agents continuously investigate production, find where it can improve, and propose how. Every cycle, the plant gets better.
Agents investigate production continuously — spotting quality drift, downtime patterns, and yield gaps, forming hypotheses, and proposing what to do. Your experts review, sharpen, and approve.
Sight Machine agents don't just use tools — they build them, in a real development environment, as they investigate.
Sight Machine ships agent apps for the problems every plant faces. Scheduling Optimization is an example. Built for high-mix manufacturers with Microsoft Research, this agent dynamically studies demand, supply, and current shop floor performance to adjust optimal schedules.
Inside a full development environment, agents construct their own apps as they investigate. When a question recurs, the agent writes a reusable app, registers it with itself, and reaches for it whenever a similar question comes up. Your library grows as you use the platform.
Your data scientists, engineers, and partners have their own models, scripts, and analyses — built in Databricks, Python notebooks, or anywhere else. Register them with the agent, describe when to use them, and they become part of the agent's toolkit.
Live views show the current state of a line with the agent's recommendation inline — a plain-language alert with exactly what to do next. Not a notification buried in a dashboard: a direct prompt to the operator, in the tools they already use.
Every problem solved teaches the platform — agents build tools, the semantic model grows, institutional knowledge becomes permanent. Three weeks in, your plant is finding more output, faster, with less effort.