The Industrial Internet of Things (IIoT), through distributed systems, autonomous control, and real-time intelligence, has transformed manufacturing, energy, and logistics. Traditional RPA, mainly developed for rule-based business operations, is deficient in the changing industrial environment, where a system must respond, adjust, and upgrade in milliseconds. RPA has not been developed to handle sudden changes in real-time conditions. It also cannot deal with continuous sensor data or make fast, intelligent choices. As industrial systems get more complex and need quicker responses, these weaknesses in RPA become more noticeable.
In this article, Rakesh Nakod, Principal Engineer, explores why RPA fails in IIoT and how Agentic AI produces a more powerful solution.
Que: Why is traditional Robotic Process Automation (RPA) considered inadequate for modern Industrial IoT (IIoT) environments, and what limitations make it unsuitable for real-time, and adaptive industrial operations?
Ans: Robotic Process Automation (RPA) emerged as a groundbreaking tool for automating repetitive business processes in structured environments. However, as the Industrial Internet of Things (IIoT) ecosystem evolves, the demand for intelligence, adaptability, and real-time decision-making far exceeds the capabilities of traditional RPA systems. In environments characterized by distributed sensors, dynamic workflows, and high data volumes, RPA shows serious limitations as below:
1. Rule-Based Nature
RPA operates using predefined rules and workflows. This rigid approach is unsuitable for IIoT systems that encounter ever-changing variables, sensor inputs, and unpredictable real-world conditions. RPA cannot learn from past data or infer beyond its programmed logic.
2. Inability to Handle Real-Time Sensor Data
Industrial IoT devices produce massive streams of real-time data requiring immediate interpretation and response. RPA was not designed for real-time analytics or control. It processes structured data in batches, making it ineffective in scenarios that demand instantaneous action.
3. Centralized Architecture & Latency Issues
Most RPA platforms are centrally orchestrated, which introduces latency and reduces responsiveness, especially when managing distributed industrial devices or edge nodes. In critical applications like machine health monitoring, this lag can lead to delayed decisions or even equipment failure.
4. Lack of Contextual Awareness
RPA bots lack situational understanding. They cannot interpret environmental or operational context, such as changing machine conditions, variations in sensor behaviour, or anomalies in workflows. This makes them brittle and error-prone in dynamic industrial setups.
5. Scalability Bottlenecks
Scaling RPA across IIoT infrastructures involves configuring and managing hundreds or thousands of bots. This manual overhead does not align with the autonomous nature of smart factories, where systems need to scale organically and self-adapt to changes.
Que: Why is Agentic AI more suitable than traditional automation or RPA for managing complex, real-time industrial IoT environments?
Ans: Agentic AI represents a transformative shift from static automation to intelligent autonomy. These agents are capable of reasoning, learning, decision-making, and collaboration, making them ideal for the fast-paced and complex nature of industrial IoT.
1. Cognitive Capabilities
Agentic AI agents can interpret unstructured data, apply logic, use memory, and make context-driven decisions. This enables them to deal with diverse IIoT inputs such as sensor signals, image data, or machine logs, and perform multi-modal reasoning.
2. Adaptive Learning
Unlike RPA, which requires manual reprogramming, Agentic AI systems learn from experience. They adapt to new data patterns, operating conditions, and emerging issues. This continuous learning is essential in industrial environments where conditions are rarely static.
3. Real-Time Decision Making
With edge computing and streaming data pipelines, Agentic AI can operate in near real-time. Agents analyse live sensor feeds, predict issues (e.g., equipment degradation), and take corrective actions without human intervention.
4. Complex Workflow Automation
Agentic systems support intricate workflows involving sequential, parallel, and conditional tasks. For instance, agents can orchestrate quality inspection, energy load distribution, and maintenance scheduling while coordinating across multiple devices.
5. Integration and Orchestration
Agentic AI acts as an intelligent layer over heterogeneous systems, including legacy PLCs, MES, and ERP systems. It autonomously integrates, coordinates, and optimizes operations across these platforms, enabling a unified, intelligent factory floor.
Que: Technical stack comparison: How does Agentic AI offer greater adaptability and scalability than RPA in Industrial IoT environments?
|
Feature |
RPA |
Agentic AI |
|
Data Handling |
Structured only |
Structured + Unstructured |
|
Architecture |
Centralized |
Distributed & Modular |
|
Intelligence |
Rule-Based |
Goal-Oriented & Learning |
|
Scalability |
Manual scaling |
Autonomous micro-agent scaling |
|
Context Awareness |
None |
High (real-time environment sensing) |
|
Learning |
Static |
Adaptive & Continuous |
|
Control Layer |
UI-level automation |
API + system-wide orchestration |
Architecture Comparison: RPA vs. Agentic AI
Que: How do the technical characteristics of Agentic AI enable smarter, faster decision-making in Industrial IoT systems?
Ans:
- Goal-Driven Behaviour & Decision-Making: Agentic systems operate on defined objectives, making autonomous decisions to fulfil them under varying conditions.
- Contextual Awareness: Agents interpret streaming sensor data, understand operational context, and adapt behaviour accordingly.
- Real-Time Edge Control: Agents reside at the edge for immediate decision-making, reducing latency and cloud dependency.
- Modular Micro-Agent Framework: Enables agents to specialize in tasks and collaborate across the ecosystem.
- Federated Learning: Agents learn and update models locally and share improvements across nodes without transferring raw data.
- Continuous Optimization: Agents refine their models over time, learning from successes and failures in their environment.
Que: How does Agentic AI enable real-time decision-making and operational efficiency across diverse industrial use cases?
Ans:
1. Smart Manufacturing
Agentic AI manages production lines, detects inefficiencies, and autonomously balances workloads between stations. It also adjusts operations based on sensor feedback and machine health.
2. Predictive Maintenance
Agents monitor vibration, temperature, and acoustic data to predict equipment failure before it happens. They schedule maintenance and trigger alerts for spare parts replacement.
3. Remote Asset Monitoring
In oil rigs or renewable energy plants, edge agents monitor asset health, optimize performance, and initiate repairs—without relying on central servers or cloud access.
4. Warehouse Automation
Autonomous agents coordinate mobile robots for picking and packing. They manage traffic flow, assign tasks dynamically, and adjust for delays or malfunctions.
5. Energy Optimization
Agents monitor energy usage and production (e.g., solar panels, grid power) to balance load, reduce consumption, and optimize cost in real time.
About the author:
Rakesh Nakod is a Principal Engineer at MosChip. He has 13 years of experience in IT industry with proficiency in AI and has experience in developing and deploying AI solutions across computer vision, NLP, audio intelligence, and document mining. He also has vast experience in developing AI-based enterprise solutions and strives to solve real-world problems with AI. He is an avid food lover, passionate about sharing knowledge, and enjoys gaming and playing cricket in his free time.