• Written by Azhar Aliyar, Director

Can we automate the process of identifying potential roadblocks in our research and development projects, or are we stuck with manual checks and human intuition? This question has sparked debate in many meetings, and the answer lies in the development of agentic AI workflows for research and development.

What separates an agent from a chatbot

An agent is distinct from a chatbot in that it can take multi-step action, using various tools and systems to achieve a goal. This is made possible by the agent's ability to maintain state and use it to inform its decisions. In the context of research and development, an agentic AI workflow can use tools like simulation software and data analytics platforms to identify potential issues and provide recommendations for mitigation.

A chatbot, on the other hand, is limited to answering a single question or providing a simple response. While chatbots can be useful for providing information or answering frequently asked questions, they lack the ability to take proactive action or use tools to achieve a goal. This limitation makes them less suitable for complex tasks like research and development.

Where a human approval step belongs

Many people believe that automating research and development processes means removing human approval steps altogether. However, this is usually premature, as human oversight is still necessary to ensure that the agent's actions align with the organization's goals and values. A human approval step can provide a critical check on the agent's decisions, preventing potential mistakes or missteps.

In fact, removing human approval steps can be downright dangerous, as it can lead to unintended consequences or unforeseen outcomes. By retaining a human approval step, organizations can ensure that their agentic AI workflows are both effective and responsible.

The research and development data this depends on

Agentic AI workflows for research and development depend on a range of data sources, including test results and failure analyses, design review records, timesheets and allocation, and supplier assessments. This data provides the agent with the information it needs to identify potential issues and provide recommendations for mitigation.

For example, a shift supervisor checks the maintenance log to identify potential issues with equipment, while the category manager compares two suppliers to determine which one is more reliable. By analyzing this data, the agent can identify patterns and trends that may not be immediately apparent to human researchers or developers.

As organizations continue to generate and collect more data, the potential for agentic AI workflows to drive innovation and improvement in research and development will only continue to grow. To learn more about how agentic AI workflows can support your research and development efforts, visit our research and development page.

What it changes about failures and effort

Agentic AI workflows can help organizations avoid rediscovering the same failures across multiple programs, by identifying and mitigating potential issues before they become major problems. This can save significant time and effort, as researchers and developers will not have to repeat the same mistakes or relearn the same lessons.

Furthermore, agentic AI workflows can help make invisible effort visible against project stage, by providing a clear and transparent view of the work being done and the resources being used. This can help organizations optimize their resource allocation and improve their overall efficiency.

Measured with key metrics

To evaluate the effectiveness of agentic AI workflows for research and development, organizations can use a range of key metrics, including failures found before validation, effort against stage, and estimate revision count. These metrics provide a clear and objective measure of the workflow's performance, and can help organizations identify areas for improvement.

By tracking these metrics, organizations can refine and optimize their agentic AI workflows, ensuring that they are achieving the desired outcomes and driving real value for the organization. For more information on how to implement and optimize agentic AI workflows, contact us today.

What this does not do

While agentic AI workflows can be a powerful tool for driving innovation and improvement in research and development, they are not a silver bullet. They will not replace human researchers and developers, but rather augment and support their work. Nor will they provide a complete solution to every problem or challenge, but rather help organizations identify and mitigate potential issues.

In some cases, agentic AI workflows may not be the right choice, such as when the problem or challenge is highly complex or nuanced, or when human judgment and expertise are essential. In these cases, organizations may need to rely on other approaches or solutions, such as human-led research and development or traditional project management methodologies.