AI Agents vs. LLMs: A Guide for Engineering Leaders

If you’re an engineering leader at a large manufacturing company, you’ve probably been asked to evaluate and adopt AI tools within your team to improve productivity. In the process, you may have encountered different terminology that describes the types of AI tools available. One term you’ve probably heard a lot: AI agents or agentic workflows. But what makes something an AI agent? And what qualifies as an agentic workflow? In this post, we’ll explain the difference between AI agents for engineering and other types of AI tools and provide real examples of how different types of AI can be used in engineering workflows.

What is an AI agent for engineering?

At a practical level, an AI agent for engineering is a system that can perform a multi-step task with minimal ongoing input from a human user. Instead of requiring an engineer to prompt the system at every step, an AI agent:

• Is configured with system-level instructions in the background
• Understands how to execute a task end to end
• Produces repeatable, predictable outputs
• Can be optimized and improved over time through testing and feedback

A good example is an AI agent designed to perform engineering drawing reviews. A human engineer doesn’t need to explain every check that an agentic drawing review system should perform. Instead, the agent already understands how to carry out that review based on predefined rules, standards, and reasoning steps.

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What Do AI Agents Actually Do in Engineering Workflows?

AI agents are particularly well-suited to high-volume, meticulous engineering tasks where consistency matters and small errors can have downstream consequences. Consider a final drawing review before a drawing is released.

In this scenario, an AI agent might work like this:

• An engineer uploads a drawing and initiates a pre-release review.
• The agent runs a predefined checklist without needing additional prompts.
• It performs multiple checks in sequence, such as:
  
  • Verifying that title block information (part numbers, revisions, revision history) is complete and accurate.
  • Cross-checking material callouts across all pages to ensure the same part isn’t specified with different materials in different locations.
  • Scanning notes for ambiguity around material specs, surface finish, or manufacturing requirements.
• When issues are found, the agent creates markups directly on the drawing, highlighting the area of concern and explaining what the issue is, why it matters, and what action is recommended

When the agentic workflow is complete, a human engineer reviews the suggestions, accepts or rejects them, and makes final decisions. Over time, the agent can improve by learning from a human feedback loop, producing more relevant and accurate results with each iteration. This is a good example of how AI agents support engineering judgment rather than replacing it.

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How AI Agents Are Different from LLMs

To understand why AI agents matter, it helps to contrast them with large language models (LLMs) like those behind ChatGPT-style tools.

An LLM is very good at:

• Generating and refining text
• Explaining concepts
• Summarizing information
• Answering questions based on patterns in data

But an LLM accessed through a chat interface is typically:

• Reactive (it responds only when prompted)
• Stateless or short-lived in memory
• Limited to the data you explicitly provide in that interaction

AI agents, on the other hand, operate inside a structured production environment. That environment allows agents to:

• Access specialized engineering data types, such as 3D CAD or drawings
• Retain system-level context and organizational rules over time
• Perform multi-step reasoning without repeated prompting
• Deliver consistent outputs across many users and many requests

For example, AutoReview is an agentic peer checker that’s embedded within CoLab. It can reason directly about 3D CAD because the platform converts CAD data into a format the agent can understand. Trying to do the same thing with a generic chatbot would require screenshots or images, which strips away a lot of critical detail and nuance.

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What Makes an Engineering Workflow “Agentic”?

You’ll often hear the terms AI agent and agentic workflow used interchangeably. Technically, an agentic workflow is more complex than an AI agent, often signalling multistep processes or multiagent architectures. However, in practice, that distinction isn’t especially important for most engineering leaders.

What matters more is the difference between a one-off AI interaction–which is what most users get when using chat interfaces–and a system that reliably executes a workflow. Agentic workflows typically involve:

• One or more agents executing related tasks
• Access to organizational data, standards, and guidelines
• Multiple steps performed back-to-back

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When to Invest in AI Agents Over Generic LLMs

Adopting AI agents requires more upfront evaluation and integration than using a generic LLM through a chat interface. But that investment comes with an important trade-off.

With AI agents:

• Outputs can be refined and improved continuously
• Performance becomes more consistent over time
• Engineering teams can delegate more of the workflow to the system with confidence

When agents are developed and maintained by a software provider, improvements compound even faster. Providers can test outputs, collect feedback across customers, and tune agents to be more accurate and relevant as part of ongoing product development. This makes AI agents especially valuable for tasks that are high volume, detail-oriented, sensitive to variation in human execution, and governed by clear standards or checklists.

Drawing review is a great example. Meticulously checking every page for title block errors, material inconsistencies, or ambiguous notes is critical, but it’s also exactly the kind of work where human approaches vary. An AI agent can apply the same standards, the same checks, and the same level of scrutiny every time. Over time, that consistency can outperform even experienced humans for certain classes of work.

Want to go deeper?

If you’re evaluating how AI agents could support your engineering workflows, CoLab partners with engineering teams to design and implement AI strategies that fit real-world processes. Learn more about how CoLab approaches applied AI for engineering teams.