The Agentic AI Revolution in Manufacturing
Explore how Rishi's orchestrator-agent architecture powered by fine-tuned LLMs and specialized agents delivers precise, validated manufacturing insights with zero hallucinations.
The Single-Model Bottleneck
For the past few years, the conversation around AI in manufacturing has centered on one question: "Which LLM should we use?" GPT-4? Claude? Gemini? The assumption was always that you'd pick one powerful model and let it handle everything—from answering operator questions to analyzing production bottlenecks to generating maintenance reports.
But here's the problem: No single model, no matter how powerful, can be an expert in everything. When you ask a general-purpose LLM to calculate OEE, it might give you a plausible-sounding answer. When you ask it to interpret PLC error codes, it might generate something that looks right. But how do you know it's correct?
Enter Agentic AI: The Specialist Team Approach
Agentic AI flips the script entirely. Instead of one generalist model trying to do everything, you have:
An Orchestrator
A fine-tuned LLM that understands manufacturing context and routes queries to the right specialist agents.
Specialist Agents
Purpose-built agents, each expert in their narrow domain—OEE calculation, anomaly detection, report generation, code interpretation, and more.
Think of it like a hospital. When you walk in with a problem, you don't see one "super doctor" who knows everything about everything. You see a general practitioner who diagnoses your issue and refers you to a specialist—a cardiologist, an orthopedist, a radiologist. Each expert does their job flawlessly within their domain.
Real-World Example: "Why did Line 3 stop at 2:47 AM?"
Traditional LLM Approach:
The model generates a generic answer: "Line 3 may have stopped due to a sensor error or material shortage. Please check the logs."
❌ Vague, unverified, and forces operators to do manual investigation.
Agentic AI Approach (Rishi):
- Orchestrator receives the query and identifies it as a downtime analysis task.
- Data Retrieval Agent fetches PLC logs, machine sensor data, and operator notes from 2:45-2:50 AM.
- Anomaly Detection Agent identifies a sudden voltage drop on Station 5 at 2:46:53.
- Root Cause Agent cross-references with maintenance history and finds a recurring electrical connector issue.
- Orchestrator compiles the findings into a crisp answer: "Line 3 stopped at 2:47 AM due to voltage instability at Station 5, caused by a loose electrical connector (documented issue in Maintenance Log #MT-4521). Recommended action: Replace connector per SOP-EL-09."
✅ Precise, traceable, actionable—backed by actual machine data.
How Rishi Implements Agentic AI
At Rishi , we've built a multi-agent system where each agent is a specialist, and the orchestrator is a manufacturing-aware LLM fine-tuned on years of shop floor data. Here's how our agent team works:
1. The Orchestrator (Fine-Tuned LLM)
Trained on manufacturing terminology, KPIs, and workflows. It doesn't try to answer questions itself—it routes them to the right agent and synthesizes their responses into clear, human-readable answers.
2. Data Query Agent
Specialized in SQL generation and database querying using Rishi's native tools and RAG pipeline. When you ask "What was yesterday's scrap rate?", this agent writes optimized queries, fetches data from your production systems, and returns validated results with full context.
Results are visualized through interactive dashboards with inbuilt components including graphs, tables, trend charts, and the ability to export data into Excel or CSV formats for further analysis.
3. Anomaly Detection Agent
Uses ML models (not just LLMs) to spot deviations in cycle time, sensor readings, and production patterns. It flags issues before operators even notice them.
4. Code Interpreter Agent
Translates PLC ladder logic, machine error codes, and sensor signals into plain English. It understands that "E1042" on a Fanuc controller means something very specific—not just a generic error.
5. Report Generation Agent
Automatically compiles shift reports, downtime summaries, and maintenance logs. It doesn't just generate text—it pulls real data, calculates metrics, and formats everything according to your templates.
6. Maintenance Knowledge Agent
Trained on equipment manuals, SOP documents, and historical maintenance records. When a machine fails, it suggests troubleshooting steps based on your plant's actual history, not generic advice.
Why Agentic AI Wins in Manufacturing
✅ Zero Hallucinations
Every answer is traceable to a specific data source. No guesswork, no "plausible-sounding" nonsense.
✅ Domain Expertise
Each agent is optimized for its specific task. A query agent doesn't try to do anomaly detection—it just fetches data extremely well.
✅ Scalability
Need a new capability? Add a new agent. No need to retrain the entire system or switch LLMs.
✅ Explainability
You can see exactly which agents were involved in answering your question and what data they used.
✅ Reliability
If one agent fails or produces an error, the orchestrator can handle fallback logic or alert you to the issue—no silent failures.
The Future: Multi-Modal Agentic Systems
The next evolution is already underway. Imagine agents that can:
- Listen to machine sounds and detect bearing failures before they appear in vibration data.
- Watch video feeds from shop floor cameras and flag safety violations or quality defects in real-time.
- Respond to voice commands from operators without requiring them to type on tablets.
At Rishi, we're actively building these multi-modal capabilities. Our vision is a shop floor where agents don't just answer questions—they actively monitor, predict, and intervene to keep production running smoothly.
The Bottom Line
The age of the "one AI to rule them all" is over. Manufacturing is too complex, too critical, and too data-rich to be handled by a single generalist model.
Agentic AI is the only path forward—where specialized agents, each best-in-class at their task, work together under an intelligent orchestrator to deliver answers that are accurate, traceable, and actionable.
Ready to see agentic AI in action on your shop floor?
Try Rishi Today →References
- 1. Linecraft AI Platform: https://linecraft.ai
- 2. Rishi Industrial AI: https://rishi.linecraft.ai
- 3. Research on Multi-Agent Systems in Manufacturing, IEEE 2024
- 4. "The Future of AI in Industry 4.0", Manufacturing Technology Insights 2025