ReAct Framework for Manufacturing AI
Pure LLMs can generate text, but manufacturing needs AI that retrieves real data, executes calculations, and produces verifiable results. Discover how Rishi's ReAct framework turns reasoning into action.
What is ReAct? Reason + Act
ReAct (Reasoning and Acting) is a framework that allows LLMs to not just reason about problems, but to act on them by calling external tools and functions. Instead of generating text based on training data, the LLM can:
- →Query databases to retrieve actual production data
- →Execute code to perform calculations and statistical analysis
- →Call APIs to fetch real-time machine status and sensor data
- →Access documentation to understand machine codes and maintenance procedures
✅ The ReAct Difference:
Instead of guessing, Rishi uses ReAct to retrieve the actual data, analyze it with real calculations, and give you a precise, verifiable answer backed by your shop floor data.
Real Example: Finding the Root Cause
Question: "Why did Line 3 OEE drop below 75% last Tuesday?"
Pure LLM Response:
"OEE can drop due to unplanned downtime, reduced speed, quality defects, material shortages, operator issues, or maintenance activities. You should check your dashboards and reports to identify the specific cause..."
Rishi with ReAct:
THOUGHT:
Need to query OEE data for Line 3 on last Tuesday, break down by availability, performance, and quality.
ACTION:
query_oee_breakdown(line=3, date='2025-12-03')
OBSERVATION:
Availability: 68% (usual: 95%), Performance: 92%, Quality: 98%
THOUGHT:
Availability is the issue. Need to check downtime events for that day.
ACTION:
query_downtime_events(line=3, date='2025-12-03')
OBSERVATION:
Station 4 (Welding) had 3 unplanned stops totaling 142 minutes. Error code: WLD-402.
THOUGHT:
Need to look up error code WLD-402 in maintenance documentation.
ACTION:
lookup_error_code('WLD-402')
OBSERVATION:
WLD-402: Electrode wear sensor fault. Recommended action: Replace electrode and recalibrate sensor.
FINAL ANSWER:
Line 3 OEE dropped to 73% on Tuesday due to 142 minutes of unplanned downtime at the Welding station (Station 4). The root cause was error code WLD-402—an electrode wear sensor fault. The welding electrode needs replacement and sensor recalibration. This accounted for the 27-point drop in availability (68% vs. usual 95%).
Why ReAct Matters for Manufacturing
🎯 Deterministic Answers
Every answer is backed by real data queries and calculations—not probabilistic text generation.
✅ Verifiable Results
You can trace every answer back to the source data and see exactly how Rishi arrived at its conclusion.
⚡ Real-Time Execution
Rishi doesn't rely on outdated training data—it queries live production systems for current information.
🔧 Actionable Insights
Instead of generic suggestions, Rishi provides specific, data-driven recommendations with root cause analysis.
Tools Available in Rishi's ReAct Framework
SQL Query Agent
Generates and executes optimized SQL queries against your production database, with automatic query validation and error handling.
Python Code Executor
Runs statistical analysis, performs complex calculations, generates visualizations, and processes data transformations in a sandboxed environment.
API Integration Layer
Connects to machine controllers, IoT sensors, and industrial platforms to fetch real-time status and historical data.
Documentation Search (RAG)
Searches through machine manuals, maintenance guides, error code databases, and SOPs using vector similarity search.
Visualization Generator
Creates charts, graphs, and dashboards on-demand to visualize trends, correlations, and anomalies in your data.
Alert Configuration
Sets up automated alerts based on thresholds, patterns, or anomalies detected during analysis.
Frequently Asked Questions
How does ReAct prevent hallucinations?
ReAct grounds every answer in actual data retrieval and computation. The LLM can only provide answers based on what the tools return—it cannot make up information. If data isn't available, Rishi says so explicitly rather than guessing.
Can Rishi access my existing databases and systems?
Yes. Rishi's ReAct framework can connect to your SQL databases, time-series databases, APIs, and industrial protocols (OPC-UA, Modbus, etc.). It works with your existing infrastructure without requiring data migration.
How long does it take for Rishi to answer a question?
Simple queries (single data lookup) typically respond in 2-5 seconds. Complex multi-step analyses may take 10-20 seconds. Rishi shows real-time progress as it executes each step of the ReAct loop.
What if the LLM makes a mistake in tool selection?
Rishi includes guardrails that validate tool calls before execution. If a query would access unauthorized data or perform an unsafe operation, it's blocked. Additionally, the orchestrator LLM can self-correct if an initial tool call returns unexpected results.
Can I see the intermediate steps Rishi takes?
Absolutely. Rishi provides full transparency into the ReAct loop—you can see every thought, action, and observation. This makes it easy to verify results and understand the reasoning process.
Does ReAct work for predictive maintenance scenarios?
Yes. Rishi can use ReAct to fetch historical sensor data, execute predictive models, correlate with maintenance logs, and provide risk assessments for equipment failures—all grounded in real data.
How does Rishi handle large datasets?
The SQL Query Agent generates optimized queries with proper indexing, filtering, and aggregation. Instead of loading millions of rows, it retrieves only the necessary data. For heavy computations, the Python executor uses efficient libraries like Pandas and NumPy.
Can non-technical users interact with ReAct-powered Rishi?
Yes! Users don't need to know SQL, Python, or APIs. They simply ask questions in natural language, and Rishi handles all the technical execution behind the scenes. The interface shows the results in plain language with visualizations.
The Bottom Line
Manufacturing doesn't need AI that talks—it needs AI that acts. The ReAct framework transforms Rishi from a conversational interface into a deterministic, verifiable intelligence system that retrieves real data, executes real calculations, and delivers real results.
Instead of guessing based on training data, Rishi uses ReAct to access your shop floor systems, understand your production context, and provide answers you can trust and act on immediately.
Ready to See ReAct in Action?
Experience how Rishi's ReAct framework turns manufacturing questions into precise, data-driven answers.Try Rishi today →