AI agents have recently taken center stage in the conversation around generative AI. Depending on who you ask, they’re either about to revolutionize the workplace or they’re just a glorified way to run a few LLM prompts in a row.
So, what’s the reality?
Industrial organizations depend on reliable, safety-critical systems. In these settings, it’s understandable that the hype around agents that do your grocery order, summarize tweets or manage calendars doesn’t resonate. But what would be useful is something like this:
“Go look at maintenance logs from all of our production lines, identify recurring equipment failures, and draft a prioritized action plan for the engineering team to review.”
That’s the kind of task AI agents promise to tackle, but how does that actually work?
AI agents in a nutshell
Agents are a very new concept, and jury is still somewhat out on how to precisely define what counts as an agent. For most, an AI agent is a system designed to take a high-level goal and work toward it autonomously. It does this by breaking the goal into smaller tasks, executing those tasks (e.g., querying databases, calling APIs, writing reports), and adjusting its actions based on intermediate results. It’s more than just automation, and more flexible than a static pipeline of prompts.
This approach is fundamentally different from workflow automation, which follows a fixed script. And it goes a step beyond prompt chaining, which can’t easily adjust to unexpected results or data structures. In theory, agents bring goal-driven reasoning into the mix.
But industrial organizations don’t run on theory. They run on systems with uptime targets, security controls, and compliance requirements. So where could agents fit in?
When agents are the way to go
Agents shine in a narrow space: they are good for tasks that are complex enough to benefit from adaptive reasoning, but not so critical that errors could cause real harm. Put simply: if it’s a simple task, automate it; if it’s mission-critical, don’t let an LLM near it.
The tasks should also be repetitive. Building a custom agent is not a small or simple process, and if a task is performed only once or just a few times a year, the time and resources invested in developing a custom agent may ultimately outweigh any potential benefits.
Finding use cases like this is often not intuitive or simple, but they do exist. They could include tasks like early-stage analysis, internal reporting, pre-processing for human review, and assisting in tool usage.
Seeing beyond the hype
There are two main issues that hinder the adaptation of AI agents especially in an industrial context. First, it is impossible to make an AI system that is 100 % correct 100 % of the time. The very nature of AI means that there will always be at least the possibility of error. There is a good reason why most of the current, well-working AI agents are coding agents; the process of writing code is too complex to automate, but if the test process (with human oversight!) is done properly, the cost of error is very low; faulty code can be rewritten before it is pushed to production and thus no harm is done.
The second issue with agents comes from the fact that we cannot just tell an AI to go fetch information from somewhere. That data lives in many places: on-prem systems, custom databases, old ERPs, SharePoint folders, and more. It’s often siloed, messy, and context-specific. Plugging an agent into that environment and expecting seamless results is wishful thinking.
A successful deployment will usually involve custom connectors and validation logic, data cleaning and structuring and careful scoping of what the agent is allowed to do. There is a lot of infrastructure to set up, especially so if cloud-based tools and APIs are not an option.
This all requires a lot of good old-fashioned software development work. What might seem simple in the time of agent-hype could turn out to be a pretty big project. If the resulting agent saves a couple hours of tedious work every few months, it probably is simply not a cost-effective solution. However, dismissing agents outright would be short-sighted. The core idea, autonomous systems that understand goals, plan actions, and adapt along the way, is a powerful one. Even modest improvements in recurring tasks in reporting, analysis, or admin workflows can free up expert time and that’s real business value.
Right tool for the right job
Most likely, agents won’t end up replacing engineers, planners, or analysts. But they can support them by speeding up data retrieval, guiding analysis, and automating tedious prep work.
Used wisely, AI agents can become a valuable part of the industrial toolkit. The key is not to chase hype, but to identify the right kind of recurring, low-risk, complex tasks where agents can reliably reduce routine work. That’s where they can deliver real, measurable value without compromising the stability and predictability industrial operations rely on. As with anything, it is important to figure out what tool is the correct one for the task at hand.
Want to explore whether AI agents could help streamline your internal workflows?
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