IT teams and decision-makers are constantly searching for ways to streamline operations, optimize performance, and improve customer satisfaction. Specialized teams may focus on infrastructure security or maintaining compliance with data privacy regulations. All teams have operational goals that require them to perform multiple essential tasks and processes.
Many manual tasks can be partially or fully automated to reduce the strain on team members and improve productivity. In some cases, automation can increase accuracy by eliminating human error. Automation tools have been part of the IT landscape for decades. Legacy mainframe operators could perform multiple tasks necessary to shut down or start an operating system with a single call to an automation script.
AI technology has matured to the point where AI agents can perform many tasks that were previously done by humans or traditional automation solutions. At first glance, the difference between an AI agent and traditional automation may seem trivial or like a marketing ploy. But key differences between the two concepts may make one a much more effective option for solving a business problem.
The core difference is in how each type of solution makes decisions and handles unfamiliar situations. An automated process typically follows a predetermined set of steps or rules. It may not be able to make progress when faced with unusual circumstances.
AI agents have some degree of autonomy and can make decisions. This autonomy may help an AI agent handle unique situations. However, this may not always be desired for a specific IT or business process.
Characteristics of Traditional Automation Solutions
IT teams have long used traditional automation solutions such as scripts and workflow tools. These tools typically share the following attributes.
- They are rule-based and follow explicit, defined logic. For example, backup automation may restart a backup job for a targeted asset if it has failed for specific reasons.
- Automation solutions are predictable and deterministic. Running a script with the same input will always return the same output. This predictability makes them easy to test and audit before being used in a production environment.
- Automation is a rigid process that can break or fail when it encounters conditions its rules don’t address.
- A script or automation tool executes its steps blindly and does not reason. It does not understand why it is performing a task, just that it must follow its instructions.
Characteristics of AI Agents
AI agents are a relatively new phenomenon and differ significantly from traditional automation. Users of AI agents must understand the attributes they share.
- AI agents are goal-based and more flexible than an automated process. They can exercise judgment to make contextual decisions to reach their goal rather than following strict, predefined steps.
- AI agents can reason to address unexpected or ambiguous situations. They can change their approach to a task and autonomously find a new solution when the initial plan does not work.
- The agents are less predictable than traditional automation tools. Their exact behavior and outputs can vary, even with the same input.
- An agent can control multiple tools and logically determine which one to use in a given situation.
Comparing AI Agents and Traditional Automation Solutions
The table below summarizes key differences between AI agents and traditional automation. Decision-makers should carefully evaluate their operational needs and the characteristics of these solutions when selecting the right option for their business.
| Aspect | AI Agent | Traditional Automation |
| Flexibility | Can handle novel situations autonomously. | May break when faced with unexpected situations. |
| Predictability | Agents can produce varying output from the same data. | Highly predictable processes. |
| Setup and configuration | The agent is given a goal and figures out how to complete it. | Automated tasks must be explicitly coded to perform the desired activities. |
| Cost | Higher compute costs. | Low compute costs. |
| Speed | Slower execution due to reasoning and modifying initial plans. | Fast process execution. |
| Resilience | Can handle change but may display unpredictable behavior. | Fails when the environment changes without manual updates. |
| Data handling | Can handle structured and unstructured data. | Best for structured data. |
| Governance | Agents may require human monitoring and oversight to ensure they remain within defined guardrails. | Minimal oversight is required after an automation tool is configured and implemented. |
Use cases for AI Agents and Traditional Automation
Let’s examine some specific use cases to see which of these two solutions is most effective. In many cases, combining both can provide enhanced functionality.
Traditional automation is inexpensive and predictable when you can specify the logic before execution. It makes sense in situations that rely on structured data and high-volume, rule-based tasks, including:
- Copying data between fixed-format systems, such as logging invoice data from customer forms into an accounting database;
- Scheduling batch jobs for tasks like backups and file transfer;
- Replying to repetitive, standard prompts on legacy systems.
AI Agents cost more in compute resources and may return less predictable results. However, they are excellent at handling unstructured data and tasks with unknown steps, such as:
- Reviewing and categorizing emails and support tickets with free-form text;
- Extracting data from complex documents such as medical records and contracts;
- Conversing with users as in tech support bots;
- Managing workflows where next steps depend on newly discovered information;
- Investigating complex problems that require synthesizing data from multiple sources.
Some systems leverage both technologies by using an AI agent to determine what must be done and then directing traditional automation tools to perform specific tasks.
VAST’s Stance on AI Agents and Traditional Automation
At VAST, we take a practical, pragmatic approach to these two productivity-enhancing options. We want to support our customers with the best solution for their unique business needs, and not have them jump on a technological bandwagon just to keep up with the competition. Our extensive experience and strategic partnerships with industry leaders give us the perspective to help you find the right solutions for your business.
Our team promotes traditional automation in our offerings, such as Cloud Backup-as-a-Service and Disaster Recovery-as-a-Service. We also work with solutions such as Huntress, which uses advanced AI agents to provide strong endpoint protection.
Get in touch with our team and learn how we can help you implement the right technology to support your business.
