Agentic workflows for quality
Agentic AI workflows for quality streamline defect tracking by integrating inspection records and non-conformance reports, enabling a quality engineer to quickly identify root causes and initiate corrective actions, such as updating batch genealogy or notifying suppliers, to prevent future defects.
A dashboard that nobody opens, filled with quality metrics that are not acted upon, is a common sight in many organisations, a testament to the fact that simply having data is not enough to drive meaningful change.
What separates an agent from a chatbot
An agentic AI workflow for quality is distinct from a chatbot in that it uses tools to take multi-step action, rather than simply answering a single question. This is made possible by the agent's ability to maintain state and work towards a goal, allowing it to interact with various systems and drive quality improvements. A chatbot, on the other hand, is limited to providing information and lacks the capability to take action.
For instance, a quality engineer using an agentic AI workflow can initiate a series of actions, such as retrieving inspection records, analyzing non-conformance reports, and updating batch genealogy, all in pursuit of identifying and addressing quality issues. This level of autonomy and tool use is what sets an agent apart from a chatbot.
Where a human approval step belongs
While agentic AI workflows can automate many tasks, human approval is still a crucial step in the process, particularly when it comes to quality decisions. Removing this step prematurely can lead to unintended consequences, as AI systems lack the nuance and judgment that human overseers bring to the table. A human approval step ensures that decisions are vetted and that the organisation is not exposed to undue risk.
Most people get wrong the idea that automation and human approval are mutually exclusive, when in fact they are complementary. By retaining a human approval step, organisations can reap the benefits of automation while still maintaining control and oversight.
The quality data this depends on
Agentic AI workflows for quality rely on a range of data sources, including inspection records, non-conformance reports, batch genealogy, and customer complaints. This data provides the foundation for the agent's decision-making and action-taking, enabling it to identify trends, detect anomalies, and take corrective action. The quality and accuracy of this data are paramount, as they directly impact the effectiveness of the agent's interventions.
A shift supervisor, for example, can use an agentic AI workflow to analyze inspection records and identify patterns of non-conformance, which can then inform decisions about production planning and quality control.
What it changes about defects and CAPA records
Agentic AI workflows can significantly impact the way organisations handle defects and Corrective and Preventive Action (CAPA) records. By enabling the rapid identification and tracing of defects back to a batch or supplier, these workflows can help organisations address quality issues more effectively. Additionally, by ensuring that CAPA records are acted upon and not simply filed away, agentic AI workflows can help organisations close the loop on quality improvements.
Defects that are traced late to a batch or supplier can have serious consequences, including costly rework, scrap, and reputational damage. Agentic AI workflows can help mitigate these risks by facilitating prompt action and ensuring that quality issues are addressed in a timely manner.
Measured with key performance indicators
The effectiveness of agentic AI workflows for quality can be measured using a range of key performance indicators, including first pass yield, defects per unit, and time to root cause. These metrics provide insight into the workflow's ability to drive quality improvements and identify areas for further optimisation. By tracking these metrics, organisations can refine their agentic AI workflows and ensure that they are achieving their intended goals.
For example, a category manager can use these metrics to compare the performance of different suppliers and make informed decisions about partnering with new vendors.
What this does not do
Agentic AI workflows for quality are not a replacement for human judgment and expertise, but rather a tool to augment and support these capabilities. They are not suitable for situations where the rules and processes are not well-defined or are subject to frequent change. In such cases, the use of agentic AI workflows may lead to unintended consequences or require excessive reconfiguration.
Organisations should carefully consider the limitations and potential pitfalls of agentic AI workflows before implementing them, and ensure that they are used in a way that complements and enhances human capabilities, rather than replacing them.
Frequently asked questions
How do agentic AI workflows improve quality control?
Agentic AI workflows improve quality control by automating defect tracking and corrective actions, enabling organisations to quickly identify and address quality issues.
What data sources do agentic AI workflows for quality rely on?
Agentic AI workflows for quality rely on data sources such as inspection records, non-conformance reports, batch genealogy, and customer complaints.
Can agentic AI workflows replace human judgment in quality decisions?
No, agentic AI workflows are not a replacement for human judgment, but rather a tool to augment and support human capabilities in making quality decisions.
How can organisations measure the effectiveness of agentic AI workflows for quality?
Organisations can measure the effectiveness of agentic AI workflows for quality using key performance indicators such as first pass yield, defects per unit, and time to root cause.