Automation, AI workflows, and AI agents: when to use what?

Understanding the three tiers of intelligent automation: from rules to reasoning

Businesses are increasingly looking at AI for automation, but AI itself is not optimal for every process, let alone a fully autonomous AI agent. Before jumping on the AI bandwagon, it's crucial to understand the differences between traditional automation, AI workflows, and AI agents. Each has its strengths, weaknesses, and ideal use cases. In this article, I'll introduce you to each. 

In 2025, everyone is talking about AI Agentic features and solutions. But before we delve deeper into the topic of automation, let's first look at a high-level comparison between these concepts.

What are the differences?

• 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 execute predefined, rule-based tasks automatically
• 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 are automations that call LLMs like ChatGPT via API for one or more steps
• 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 are programs designed to perform non-deterministic tasks autonomously. 

Which tasks can each handle best?

• 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 shine with pre-defined deterministic tasks
• 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 are great for deterministic tasks requiring some flexibility
• 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 should be used to handle non-deterministic, adaptive tasks. 
  

What are their strengths?

• 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 deliver outcomes reliably and are fast to execute
• 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 are great for pattern recognition and handling complex rules
• 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 are best when you expect new variables and scenarios.

What are their respective weaknesses?

• 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 are limited to tasks explicitly programmed and cannot adapt
• 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 require data to train models and are usually harder to debug
• 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 are less reliable and may produce unpredictable outcomes. 
  

The three levels of automation

1. Traditional automation: predictable, rule-based execution

Automation refers to predefined, rule-based tasks executed automatically without AI. These systems follow strict logic and workflows without deviation. Think of robotic process automation (RPA), where a software mimics human actions in structured tasks like data entry or report generation.

Most manufacturing organizations are optimizing with the goal to iterate and produce mechanical versions faster than any of their competition. This is mainly due to the philosophy of:

• Autonomous thinking of the management to join and reshape part of the process
• Joining the process together for “machine that builds the machine".

These are, in general, processes that can be done asynchronously, independently, with only a “handover” process in between.

Requirements: A central check that all requirements are in place to execute automation.

Key features of automations:

• Predefined rules: operates based on pre-set triggers and actions
• Linear processes: executes tasks step-by-step
• Limited flexibility: cannot adapt to unexpected changes
• Integration-friendly: connects with multiple platforms and tools.

Real-world examples of automations: 

1. Email marketing campaigns: automatically sending welcome emails to new subscribers
2. Task reminders: triggering reminders for overdue tasks in project management tools
3. Invoice processing: generating and sending invoices without manual input.
   

2. AI workflows: automation with intelligence

AI workflows introduce AI models, such as large language models (LLMs), into automated processes. Instead of following rigid logic, AI workflows can handle pattern recognition, unstructured data, and complex decision-making within a structured flow. For example, this could be a customer service chatbot that responds based on predefined decision trees but leverages a LLM for open-ended queries.

In generalization the workflows are a limited set of automations that are orchestrated by a core engine. Between 2017 - 2019, I built my own NLP (natural language processing) engine with the technology concepts used at that time (Word2Vector), and utilized this exact concept: core engine that organizes different flow-points to build dynamic discussions, still within certain predefined workflows.

Some components in modern playbooks are:

1. Prompt change 

2. Routing

3. Parallelization

4. Orchestrator-worker

5. Evaluator

Requirements: A predefined set of flows that can be chosen freely by AI logic.

Key features of AI workflows:

• Dynamic decision-making: adapts processes based on data patterns
• Multiple integrations: links several tools and platforms
• Data-driven: learns from historical data to improve performance
• Scalable: easily handles growing data and process demands.

Real-world examples of AI workflows

1. Customer support systems: routing tickets based on urgency and type
2. Lead scoring systems: prioritizing leads based on likelihood to convert
3. Fraud detection systems: identifying suspicious transactions and flagging them for review.

3. AI agents: autonomous and adaptive decision-making

AI agents go beyond automation and workflows by reasoning, planning, and acting autonomously. Unlike workflows that follow pre-set paths, agents can adjust to new information, create their own strategies, and execute complex tasks with minimal human input. Think of an AI-driven research assistant that gathers information, evaluates sources, and refines its responses iteratively.

A key component is an action-feedback loop that is running until all the tests are passed. The key here is to define what tests are needed and executed for a system to run the autonomous loop.

Requirements: Test cases to run the autonomous loop against.

Key features of AI agents:

• Autonomous behavior: operates without constant human input
• Machine learning: continuously learns and improves performance
• Conversational AI: uses NLP for communication
• Real-time adaptation: adjusts actions based on new data.

Real-world examples of AI agents:

1. Chatbots: providing customer service and answering queries
2. Virtual assistants: managing schedules and setting reminders
3. AI customer support: handling inquiries via phone or chat in real-time.

When to use which?

• If the task is repetitive, rule-based, and doesn’t require AI: use traditional automation. It’s fast, reliable, and easy to maintain
• If some flexibility is needed but the process remains structured: use AI workflows to introduce pattern recognition and intelligent decision-making
• If the task is complex, evolving, and requires adaptation: consider AI agents, but be prepared for unpredictability and ongoing refinement.

Especially when evaluating Agentic AI, consider:

1. Does it make decisions autonomously, or is it limited to following predefined rules?
2. Can it adapt to new or unexpected scenarios?
3. Does it dynamically compose skills to handle complex, multi-step tasks?
4. Is it capable of learning and improving through interaction?

The hype versus reality

Everyone wants AI agents now, and when wondering if you should build an AI agent, just say yes! The world is moving towards those and a well working autonomous workflow is a life saver in many situations when your flow and focus is broken constantly. Its upsides can impact your business massively.

It just is not that easy to complete, and most likely it will require some extra work from the implementation team. 

At Recordly, we believe in leveraging the right technology for the right job. Whether it’s simple automation, AI-enhanced workflows, or cutting-edge AI agents; the goal remains the same: smarter, more efficient processes that drive business value. And the best part: we can help you figure all that out. 

Sources: 

• https://www.anthropic.com/engineering/building-effective-agents. 
• https://skphd.medium.com/automation-ai-workflow-and-ai-agents-understanding-the-differences-and-their-role-in-modern-7a7227d94bf0. 
• https://www.linkedin.com/pulse/automations-vs-ai-workflows-agents-understanding-key-tyler-mcgregory-w1c8e/. 
• https://medium.com/@falkgottlob/many-ai-agents-are-actually-ai-workflows-or-automations-in-disguise-7e377017df98.