Industrial Agentic AI
Reply empowers factories with autonomous reasoning and adaptive execution to overcome legacy data fragmentation
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Towards Cognitive Manufacturing
Manufacturers are experimenting a new operational paradigm defined by Agentic AI. This evolution represents a shift towards systems capable of reasoning, planning, and executing complex tasks within the physical and digital constraints of a factory.
Industrial Agentic AI involves a network of specialised autonomous agents working in concert. These agents bridge the gap between fragmented legacy data silos and modern operational efficiency. By integrating these agentic capabilities on top of core systems, manufacturers can leverage on Cognitive Manufacturing Platforms capable of adaptive, self-optimising execution.
The Main Challenge: Industrial Data Fragmentation
A primary hurdle in empowering factories with AI is the isolation of data. In a typical production environment, applications are divided into silos. Factories often utilise a specific Manufacturing Execution System (MES) and different applications for quality control, machine maintenance, and logistics.
In the last decade, attempting to unify this landscape involved constructing massive data lakes to aggregate and normalise information from every source. This process is frequently slow, resource-intensive, and creates high barriers to entry.
Furthermore, manufacturers rarely possess the source code or full data models of the proprietary software they use (such as third-party SCADA or ERP systems), resulting in “black box” environments where data is difficult to extract and correlate.
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The Architecture: A Graph-Based Cognitive Layer
To address these challenges without requiring a complete replacement of legacy infrastructure, the Reply architecture overlays existing systems using a Dynamic Graph-Based Semantic Data Model to be consumed by a network of AI agents.
Unlike traditional integrations that rely on rigid schemas, this approach employs Graph Databases to map the complex relationships between disparate entities. It connects production lots, machine data, quality reports, and maintenance logs
Because the underlying data models of legacy applications are often unknown, the architecture features a GraphDB Builder that reconstructs these models. This allows agents to semantically understand where to locate specific information across heterogeneous environments.
This semantic layer enables the deployment of MCP (Model Context Protocol) Servers, which provide secure, normalised access to legacy systems, ensuring that agents can retrieve specific data without requiring a complete data migration.
Approaching Cognitive Manufacturing with Reply
Reply’s approach in supporting manufacturers in transition to Agentic AI is focused on the functional integration of data, intelligence, and autonomous action within an industrial context.
From Custom Solutions to Pre-Built AI Applications
Reply promotes a significant shift in the industry is the move from bespoke, single-use models to Pre-built AI Applications. Previously, in fact, AI in manufacturing required custom development for every use case. Now, the integration of cognitive features into Reply platforms like Brick Reply and LEA Reply allows for the deployment of standardised agents.
By standardising the Agentic AI network and the underlying semantic models, manufacturers can deploy robust solutions across multiple plants with minimal customisation. This accelerates the time-to-value and ensures consistent performance across different production sites.
A Reply Project:
Intelligent Quality Investigation
A practical implementation of Reply approach is the "Quality Investigation" application developed by Hermes Reply for a major Consumer Packaged Goods (CPG) manufacturer.
The Problem
- When a customer complaint is received regarding a specific product batch, tracing the root cause is traditionally a manual process requiring days of analysis.
- Engineers must extract quality data, identify production lots, cross-reference machine statuses during that specific window, and check for maintenance interventions or material changes.
The Agentic Solution
- Using a pre-built agentic application, the system correlates these distinct data points automatically.
- An operator initiates the request via a conversational interface.
- The agents then retrace the history of the specific lot, checking the semantic connections between the final product and the manufacturing conditions.
The Result
The system aggregates data on materials, machine states, and environmental variables to produce a comprehensive investigation report in minutes rather than days. This capability transforms a reactive, labour-intensive process into a proactive, automated workflow.
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Frequently Asked Questions
Accelerating the Introduction of Agentic AI in Manufacturing
Reply experience shows that the transition to Agentic AI offers to manufacturers a tangible path to solving longstanding challenges, such as data fragmentation. With a proven track record of deploying cognitive solutions across diverse geographies and varying scales of production, Reply enables manufacturers to move beyond theoretical pilots. Manufacturers are encouraged to experiment with these scalable, pre-built architectures to unlock immediate value, leveraging Reply’s extensive experience in integrating agentic capabilities into complex, legacy-heavy industrial environments.
