From Scripts to Smarts: The transition from workflows to AI agents 

This article is co-authored by Aishwarya Hariharan, Product Marketing Lead at Atomicwork.

‘AI Agents’ is the buzzword of... well, the last few months, and has been liberally applied to every new feature release. Yet many AI agents are simply AI automations masquerading as agents. If you are actively trying to automate processes within your organization, it is important to understand the distinction.

Configuring AI workflows for an agentic use case can lead to unreasonable expectations and disappointing results, as does using AI agents when you want predictable and consistent outcomes that only rule-based automation can solve for.

Rule-based automation are pre-programmed set of rules or instructions—uncompromising, rigid but highly predictable in outcome, and mostly performant. As workflows get more complex and need to handle multiple scenarios, traditional workflows get unwieldy to manage and maintain. Adding or updating part of a workflow becomes error-prone and testing them— a nightmare.

What are AI workflows good at?

An AI workflow, at its core, is a traditional rule-based workflow, but supplemented with AI capabilities. The most prominent AI capabilities baked into a workflow in the last few years have been understanding natural language to trigger workflows, pattern matching, actioning based on user sentiment, or, more recently, populating inputs with GenAI-created content.  

Let’s look at a couple of examples of AI workflows for employee support in an organization.  

Example 1 - Consider an employee onboarding flow

Example 2 - Consider this more complex example of a user trying to reset their password by interacting with a bot

While AI workflows do reduce some steps when compared to a traditional workflow and improve user experience, the problem of designing, building and maintaining unwieldy and complex workflows does not go away. The essence of the workflow is still a deterministic process, and it lacks any autonomous decision-making.

AI agents take the wheel

What fundamentally distinguishes an AI Agent over an AI workflow is autonomy - or the ability to reason and make decisions.  

AI Agents are ideal for complex tasks where:

1. The inputs that need to be processed lack structure, or are fuzzy or
2. Decisions need to be made dynamically, based on scenarios and inputs from previous steps  

Within the constraints and instructions provided to the agent, it can

• Plan, strategise, and break down a complex scenario into subtasks
• Retain a running memory of data and interactions
• Use and interact with multiple tools
• Reason over its own responses, iterate and adapt

Consider an AI Troubleshooting Agent designed to address common IT issues like a slow-running laptop. It can interact with the user’s device to gather symptoms. This input is fairly large and unstructured, and hard for traditional workflows to process (example output below).

Based on further clarification from the user, it can execute diagnostic steps such as terminating unnecessary processes or cleaning up disk space. Throughout the process, it remains the point of contact for the user, providing clear instructions and feedback, and logging all interactions to enhance its future performance.

The AI Troubleshooting Agent, is essentially operating the following way:

1. Planning and strategizing: The agent decomposes the problem into manageable subtasks, some in parallel (gathering symptoms from the device, asking clarifying questions), some sequential (diagnostic actions).
2. Using memory: The agent logs all interactions with the user, which not only helps in the current troubleshooting session but also enhances its capability for future interactions. It also significantly improves user experience by maintaining a multi-turn conversation with the user.
3. Tool usage: In diagnosing and resolving the laptop issue, the agent interacts with various system tools and utilities to perform tasks such as checking system health, managing running processes, and clearing temporary files.
4. Reasoning: Throughout the troubleshooting process, the agent assesses the effectiveness of each step based on user feedback and system responses. If an action doesn’t resolve the issue, the agent iterates with alternative solutions.

The takeaway: Not everything is an AI agent use case

As important as it is to understand this distinction between AI workflows and AI agents, it is also prudent to remember that not all use cases demand an AI agent.  

Many enterprise environments value predictability, and workflows (rule based or infused with AI) are highly relevant. For the most part, it is NOT a good idea to replace an existing workflow which delivers predictable and accurate outcomes with an AI agent.  

AI agents are most successful for use cases that were pretty much impossible to automate until today and required human intervention or judgement. They are ideal when inputs are complex to process, can change and adapting to these changes is crucial to achieving successful outcomes.

The choice between AI workflows and AI agents ultimately boils down to the problem you’re trying to solve. Where workflows were constrained to a task or process at best, AI agents have the opportunity to deliver for an entire job role or function. As enterprises navigate this evolving AI landscape, making this distinction will be critical in leveraging the right tools for the right challenges—and unlocking the true potential of AI in your operations.

If you'd like to evaluate your processes and understand where to incorporate AI agents, we're happy to help. Drop us a note here :)