Industries managing complex physical operations face a persistent paradox: as infrastructure grows more sophisticated, the expertise required to maintain it becomes increasingly specialized, yet skilled technicians remain scarce. Persistent labor shortages tell the story of our failure to get more trained technicians. We suggest the correct question is: How do we dramatically amplify our existing technicians by putting a new tool in their toolbox? 

The Deceptively Simple Premise

Consider how human technicians learn their craft. You onboard them with comprehensive documentation, give them access to system diagnostics, teach them to interpret sensor data and control system outputs, and walk them through procedures for common failures. Over time, they build intuition about failure modes and develop systematic approaches to troubleshooting.

Our AI agent with sufficient context can guide technicians through that same diagnostic reasoning. When a technician arrives at a malfunctioning automated guided vehicle, unresponsive PLC, or failing conveyor system, the AI can instantly synthesize current readings and error logs, maintenance history, similar failures across the fleet, troubleshooting procedures, and safety considerations.

The premise is simple: if a technician can diagnose and repair physical systems with proper training and context, our agent can very efficiently guide any technician through the same process. But between that premise and reliable deployment lie two fundamental challenges about getting our agent read in on your specific context.

Challenge One: Integration with Physical Systems

When a technician approaches a malfunctioning system (whether it's an autonomous mobile robot, a manufacturing line controlled by PLCs, or a SCADA-monitored industrial process) they need real-time information about its physical state. What were the sensor readings before it stopped? What error codes has it logged? What's its power status, temperature profiles, and control system state?

This diagnostic data lives in operational systems: cloud platforms ingesting telemetry, SCADA interfaces monitoring industrial processes, historians logging PLC data, fleet management software tracking equipment health, and distributed control systems orchestrating complex operations. For our agent to effectively guide a technician, it needs seamless access to this operational data.

Here's where years of infrastructure investment become critical. Platforms like Formant have spent a decade solving the unglamorous but essential problem of reliably connecting software to heterogeneous physical infrastructure: robotic systems, industrial control hardware, SCADA networks, proprietary automation equipment, and sensor deployments.

Consider what comprehensive integration means in practice. When a technician reports failures in an automated manufacturing cell, our agent needs to pull 48 hours of PLC data logs, check control sequences, review recent process parameter changes, analyze power consumption patterns, and compare performance metrics to similar installations. Each data source might use different protocols (Modbus, OPC UA, MQTT, proprietary APIs) with varying formats and access methods.

Without this integration layer, technicians work blind, with the agent offering only generic advice from the AI model training data rather than specific guidance based on actual system state. The alternative is building integrations from scratch for each deployment, a process that takes years and rarely achieves production-grade robustness.

Beyond Reactive Repairs: Predictive Intelligence

The true power of AI agent assisted maintenance extends beyond fixing what's already broken. With continuous access to operational telemetry and historical patterns, these systems can identify potential issues before they impact operations.

An agent analyzing real-time data might notice that a motor's vibration signature is gradually changing over weeks, still within acceptable ranges but following a pattern that preceded bearing failures in similar equipment. It can alert maintenance teams to schedule preventive service during planned downtime rather than waiting for emergency breakdowns during peak production.

Similarly, for SCADA-monitored processes, the system might detect that a valve's response time is slowly degrading, or that a temperature controller requires increasing adjustments to maintain setpoint. These are early indicators of impending failure, difficult for technicians to spot across hundreds of data points but readily apparent to an agent with comprehensive historical trends.

This shift from reactive to proactive maintenance fundamentally changes operational economics. Instead of rushing to fix failures that halt production, technicians address emerging issues during convenient maintenance windows. The agent  reports "based on current trends, this component will likely fail within two weeks. Here's the optimal time to replace it based on your operational schedule."

Challenge Two: Context Engineering at Scale

A maintenance technician doesn't just need the current system state—they need comprehensive knowledge about how to interpret that state and what actions to take. This knowledge lives in enormous, distributed repositories accumulated over years of operations.

An effective agent needs simultaneous access to:

• Equipment documentation including technical specifications, wiring diagrams, P&IDs, and ladder logic

• Procedural knowledge with step-by-step repair workflows, startup/shutdown sequences, and calibration protocols

• Troubleshooting trees that guide from symptoms to root causes

• Historical incident data showing past failures and resolutions across similar equipment

• Parts information with replacement procedures and inventory locations

• Safety protocols specific to each repair scenario, including lockout-tagout procedures

This knowledge exists in massive, heterogeneous knowledge bases: equipment manuals, SCADA alarm documentation, PLC program comments, internal runbooks, incident reports from CMMS systems, photos and videos from past repairs, and tribal knowledge captured in communication channels.

Consider a real scenario: A technician reports intermittent jams in a packaging line. The agent needs to retrieve specifications for normal conveyor speeds and sensor timing, access maintenance history, check if other lines showed comparable behavior, understand what products are running and whether they've changed, provide diagnostic steps for checking sensor alignment and mechanical timing, and guide remediation based on whether it's a sensor issue, mechanical wear, or control logic problem.

Each piece of information could be in different formats: PDFs, SCADA databases, structured maintenance records, images, videos, PLC documentation, across different systems. Implementation requires sophisticated text chunking, vector embeddings, and semantic search that can handle the scale and diversity of industrial knowledge bases.

The critical challenge is maintaining context that spans both universal engineering principles and site-specific implementations. How do you ensure the agent provides the right procedure? The generic OEM procedure, or the facility's modified version accounting for their specific production environment? This requires structuring knowledge to enable precise retrieval based on equipment type, customer context, and failure symptoms.

The Architecture and Practice

Building our AI agent for field technicians requires integrating several components: a reasoning layer that interprets questions and determines diagnostic paths grounded in physical reality, a memory layer maintaining repair histories and procedural workflows, an integration layer connecting to SCADA systems and maintenance databases, a retrieval layer searching documentation contextualized to specific equipment, and a safety layer ensuring technicians are never guided toward unsafe actions.

We guide our customers to start with structured, high-value scenarios where diagnostic paths are predictable: motor replacements, sensor calibration, PLC program updates. Implement progressive autonomy where the agent initially suggests steps that technicians validate until trust building can unlock more direct task solving by our agent. We provide comprehensive logging of both agent reasoning and repair outcomes. Most critically built a practice to capture tribal knowledge systematically through photos, videos, and annotations during repairs.

The Strategic Advantage

Three trends are converging: AI agent capabilities have reached reliability thresholds for field deployment, integration infrastructure has matured, and context engineering has evolved from research concept to practical approach.

But organizations with robust system integration and comprehensive knowledge bases have a distinct advantage. Building reliable integration infrastructure for physical operations takes years. Every control protocol supported, every sensor type integrated, every edge case handled adds to the foundation. Now that AI capabilities matured, platforms with this integration layer already in place could immediately leverage it for intelligent assistance, while others faced extensive infrastructure development.

The Physical AI Transformation Ahead

Junior technicians gain access to senior-level diagnostic reasoning, guided through complex troubleshooting that previously required years of experience. Faster diagnosis means shorter repair cycles and reduced downtime. Proactive identification of emerging issues enables scheduled maintenance instead of emergency repairs, fundamentally improving system reliability and operational efficiency.

Our agents learn from repairs to preserve institutional knowledge that would otherwise be lost when senior technicians retire. This captured expertise becomes accessible to every technician, democratizing deep understanding that traditionally took decades to develop.

The transition to agent augmented maintenance isn't a distant future. It's happening now. But success requires robust integration with physical systems and comprehensive access to knowledge bases. Organizations that built this infrastructure find themselves well-positioned to deploy intelligent assistance at scale.

The future of industrial maintenance isn't replacing technicians with AI. It's augmenting every technician with instant access to comprehensive expertise, real-time system state, and predictive insights that prevent failures before they occur. The unglamorous infrastructure work of connecting software to physical systems becomes the essential foundation for this AI powered transformation of physical operations.

If you'd like to empower your people to run Physical AI assisted physical operations then we'd love to hear from you.