What are AI Agents?
AI agents are intelligent software systems designed to perceive their environment, reason about it, make decisions, and take actions to achieve specific objectives autonomously. They can operate based on predefined condition-action rules, maintain an internal model of the environment, consider the expected utility of each possible action, learn from experiences, or model human-like behavior. The result is computers that can complete tasks like humans.
The functionality of an AI agent can be broken down into 5 key steps:
1. Perceiving the Environment: gathering information using sensors or inputs from other data sources
2. Processing Input Data: organizing the data, creating a knowledge base, or making internal representations that the agent can understand
3. Decision Making: applying machine learning to make an informed decision based on live inputs and environment
4. Planning and Execution: developing and executing a step-by-step strategy while optimizing resource allocation and considering various limitations and priorities to reach the goal
5. Learning and Improvement: learning from experience to improve performance and adapt to new situations and environments
By breaking the functionality of AI agents into parts, we can understand how they would operate within specific use cases. While agents are highly adaptable and the opportunity for application are nearly endless, manufacturing automation a domain of particularly high value for AI agents.
Characteristics of Manufacturing Automation in 2024
Manufacturing automation is the use of technology, machinery, and equipment to automate production processes and systems in various industries to increase efficiency, productivity, and cost-savings.
There are several characteristics of manufacturing automation in 2024 that make it ripe for disruption through AI agents.
Expanded use of IoT: an increasingly large portion of the devices on any manufacturing floor are collecting data, connected to the internet, and steaming that data to a centralized source.
Increased prevalence of robotics: increasingly collaborative and general purpose robotics have been deployed to fill shortages resulting from tight labor market.
Emergence of Highly-sophisticated digital twins: improved data quality and modeling capabilities has increased manufacturers ability to represent physical processes digitally.
Embracing of Data Standardization: the industrial manufacturing sector is embracing standardized product content classification, such as ETIM and ECLASS.
Automation drivers in 2024 and functionality of AI agents
Each of the driving trends around manufacturing automation in 2024, described above, has notable implications on the use of AI agents in this space.
1. Perceiving the Environment: Adoption of internet connected hardware (IoT) has dramatically increased the amount of data generated by a manufacturing process. This data is the “fuel” that will make AI agents more effective decision makers, allowing agents to better know the environment.
2. Processing Input Data: While industrial data is historically a source of headache for data scientists, efforts towards data standardization and next generation platforms for data unification such as Bronco AI have made data processing a more feasible task.
3. Decision Making: digital twins or virtual models of physical processes can act as the “mental model” for an AI agent. Such representations of the physical world will make agents less dependent on sensors, of varying accuracy, and more aware of the world around them by digital proxy.
4. Planning and Execution: Increasingly sophisticated robotics will ultimately become a more capable mechanism by which AI agents (as software) interact with the world and thus execute on the proceeding analysis and planning.
5. Learning and Improvement: In a similar vein as Decision Making, digital twins have virtualized the process of learning and improvement of adjustments to processes can be simulated and measured.
Use Cases
Driven by the alignment between manufacturing trends and core functionality of AI agents, there are a number of high-value use cases for agents in the context of manufacturing optimization. Of particular value are opportunities in which the process can be chained in a series of steps, such that the automation and integration of each step is more valuable than each individual parts of a process.
Some example of these include:
Process Optimization: Agents can act like the most intelligent of process controllers, continuously monitoring data from live processes, updating their internal state, and even simulating optimizations before selecting adjustments to maximize output or efficiency. Basetwo AI, fueled by physics-based hybrid modeling, represents the best in class in this area.
Production Scheduling: Agents can take into account the current state of a production system such as the machine availability, labor resources, and material supply, be guided towards an objective, and make adjustments responsive to real-time inputs such as equipment failures or urgent orders.
Defect Detection and Remediation: Agents can be integrated into production lines to monitor and analyze processes in real-time, programmed with the desired state of a system, and take actions to maintain the desired state including adjusting machine settings or performing corrective actions.
Ultimately, while there are major barriers towards technology adoption all over legacy industries, the wind blows in the back of AI agents’ adoption in industrial manufacturing.