AI Agents for Industrial Operations
Industrial operations don't need chatbots. They need agents that observe conditions, query context, decide within defined boundaries and escalate when appropriate. This course designs that architecture for real systems — with traceability, security and operational control.
Operations don't need a chatbot.
They need an agent that acts.
A chatbot answers questions when someone asks them. An agent continuously observes conditions, queries context from the right systems, decides within predefined boundaries and executes actions — or escalates to a human when the autonomy limit is reached. The difference is not in the name: it is in architecture, integration and operational accountability. Confusing both concepts is the first mistake most teams make when embarking on industrial agent projects.
The second mistake is underestimating the complexity of integrating an agent into real systems. Connecting to a process historian, a CMMS, a SCADA or an operational messaging system requires tool design, permissions management, fault tolerance and traceability of every decision. An agent without technical governance in an industrial environment is not a productivity tool — it is a continuity risk. Most agent training offerings do not address that reality.
This course is designed for the hard environment: industrial operations connected to real systems, with security constraints, low error tolerance and the need for complete traceability. The Monday Outcome: participants leave with the design of an agent connected to their operational environment, with an observation architecture, tools, decision rules and escalation mechanism — ready to prototype, not to present on slides.
What you will build
Four modules that progress from the conceptual architecture of industrial agents through to deployment with technical governance and decision traceability in production.
Difference between a conversational assistant, an automated workflow and an autonomous agent with tools. High-value industrial use cases: condition monitoring, work order generation, shift coordination, alarm escalation. Architectures for observation, memory, tools and action. When an agent adds real value and when it is over-engineering.
4 hoursConnection to process historians (OSIsoft PI, Ignition), CMMS systems (SAP PM, Maximo), SCADA, reporting platforms and operational messaging. Agent tool design: what it can read, what it can write, what it can trigger. Minimum permissions management, action limits and secure execution environments in segmented OT networks.
5 hoursRules, thresholds and context for the agent to decide correctly. Agents for alarms, anomalies, predictive inspections and operational coordination. Human-in-the-loop: where the agent acts alone, where it waits for human confirmation and how to design that boundary without it becoming a bottleneck. Priority management and conflict between concurrent conditions.
6 hoursAgent observability: how to monitor that it is operating as designed. Decision logging, complete traceability and rollback capability. Behavioral testing before production: how to simulate real conditions and validate responses before connecting to critical systems. Maturity roadmap for industrial agents: from supervised PoC to autonomous operational asset.
5 hoursWhat you will be able to do
Complete architecture: condition observation from historians or SCADA, action tools over CMMS and messaging systems, decision rules and escalation mechanism configured for your operational environment.
Decision traceability, minimum permissions, autonomy limits and rollback capability. An agent the technical team can audit and the operational team can trust — not a black box connected to critical systems.
Maturity roadmap for industrial agents: how to move from a supervised sandbox agent to an autonomous operational asset with expanded coverage — without losing traceability control or breaking defined security limits.
Who teaches this course
Seven years leading data ecosystems at BCI, work at illumin Canadian ad tech, with stints at the IDB, PDVSA, Falabella and Walmart across five countries. Founded Yaripo with the purpose of closing the gap between AI strategy and real implementation in mid-size organizations with critical operations.
Yaripo brings AI agent design to the hard terrain: industrial operations connected to real systems, with security constraints, low error tolerance and the need for complete traceability of every decision. While most AI agent training offerings work in generic digital contexts — web APIs, productivity assistants, office automation — Yaripo designs for OT/IT environments with process historians, CMMS, SCADA and operational continuity requirements that cannot tolerate unpredictable behavior.
What advanced engineers ask before enrolling
Operations don't need more interfaces.
They need agents that act.
20 hours. 4 modules. An agent connected to real industrial systems — with traceability, governance and operational control from the design.
Enrollment at academia.yaripo.cl · Online asynchronous format · SENCE-eligible training