AI Workflows vs. AI Agents: Choosing the right solution to supercharge your business

There are varying definitions of what an AI agent is, circulating across the industry, as we all try to grasp this new paradigm shift in how we work today.

Keeping up with the speed of AI innovation and paradigm shifts globally can be overwhelming, especially as funding for AI startups shows no signs of slowing down.

According to BusinessTimes, AI startup funding reached a record-breaking US$97 billion in 2024. With this massive funding, AI companies are compelled to advance innovation at an unimaginable pace, leading to new and groundbreaking AI announcements almost weekly.

In this article, I’ll break down in simple terms what AI workflows and agents are, explain in depth how they differ, and finally, show you how to leverage them to grow your business.

Often mistaken for AI agents, AI workflows are AI systems designed and programmed to complete specific tasks in a particular sequence. These tasks can involve chained Large Language Model (LLM) calls with prompts, each handling a different part of a process.

For example, you could have an AI workflow system that:

This is an automation workflow with augmented LLM calls in between.

The main challenge with AI workflows is their tendency to falter when the system encounters unexpected scenarios or “curveballs” because they are orchestrated to follow a certain flow. Building fail-safe systems can be daunting since predicting every possible disruption in advance is nearly impossible.

AI workflows are ideal for environments and situations where you, the human, maintain significant control over their operation

AI agents are systems designed to perform tasks and actions in either a sequential or non-sequential manner. They can be reactive or autonomous and may use single-agent or multi-agent architectures where tasks are handed off between agents.

There are also vertical agents, tailored for specific industries, and horizontal agents, designed for broader use cases.

Unlike workflows, AI agents are built to adapt, make decisions, and respond to unexpected outcomes. They can learn and re-learn new ways of doing things, all within the boundaries you, the human, set for them.

A critical component of a multi-agent system is retaining the “Human-In-The-Loop” during the orchestration and execution of tasks. This means allowing humans to review, approve, or reject the output of agents, especially when sensitive actions are involved. Incorporating the Human-In-The-Loop helps maintain control and minimizes the risk of potentially damaging consequences for your business.

When creating AI agents, the key steps include:

Deciding whether to use AI agents or workflows for your business depends entirely on your specific use case and needs. For many businesses, AI workflows are sufficient and more cost-effective. On the other hand, AI agents may require higher compute costs and are generally more complex to maintain due to their need for orchestration, and adaptability.

Deciding whether to use AI agents or workflows for your business depends entirely on your specific use case and needs. For many businesses, AI workflows are sufficient and more cost-effective. On the other hand, AI agents may require higher compute costs and are generally more complex to maintain due to their need for orchestration, and adaptability.

Your implementation strategy will depend on the strength of your technology team and your financial budget. Ensure you have identified your key objectives, extracted relevant datasets for the objectives, and prepared the data for AI use. There are numerous low-code, no-code tools, and software frameworks available today to get your business started today with implementation, including:

When building agents or workflows using these AI solutions, be mindful of the associated risks and follow best practices to mitigate against those risks.

Thanks for reading, and Happy AI Transformation!