Beyond automation: The power of agentic AI workflows

Delivering excellent customer service over the phone starts with effective listening. A voice assistant must accurately hear and understand what...

Tom Haynes Content Lead / Content Marketing Lead / Senior Content Marketing Manager

Apr 4, 2025

8 min

Agentic AI represents a step beyond traditional assistive AI, like co-pilots. It is the next growth spurt in this ever-evolving technology, a move towards more independent systems that can act on their own.

This shift is inspired by the concept of human "agency"—the ability to make decisions, take actions, and influence outcomes.

Human agency involves four key aspects:

Forethought: Setting goals and preparing for challenges
Implementation: Taking action and following through on plans
Self-management: Staying focused and consistent
Learning and adapting: Adjusting strategies based on results

Agentic AI works in a similar way. It’s like a personal assistant that understands your preferences and takes care of tasks, so you focus on other things. It may check-in for clarification on more complex tasks but generally works independently and adapts based on your feedback.

For example, an agentic AI system can manage calendars, schedule meetings, prioritize tasks, and resolve conflicts, all without constant input. But where this technology separates itself is through its ability to continuously learn and advance, such as asking for approval for sensitive actions, like making payments.

What is an agentic AI workflow?

An agentic AI workflow is a series of tasks completed by an AI agent with minimal human supervision and can be used for consumer tasks like booking flights or making reservations.

Now, it’s being used in businesses as a "technological assistant" in the workplace. For example, in customer service, an AI agent can autonomously handle an entire return process. When a customer has a return, the AI agent listens, verifies the customer’s identity, and understands the specifics of the return. It then processes the request, generates a return label, schedules a pick-up, and sends a confirmation email.

To do this, AI agents analyze data, make decisions, take action, and adapt based on new data and the results of previous actions.

Traditional vs. Agentic AI systems

AI is quickly moving from traditional automation to more autonomous processes that need less manual oversight. Traditional, rule-based systems rely on decision trees or "if-this-then-that" logic and follow fixed scripts to perform tasks.

In the case of chatbots , each conversation is pre-programmed with specific pathways, meaning that for every potential query, there must be a predefined rule or action. While this works in theory, customer interactions are dynamic, and people often veer off-script, asking unexpected questions or introducing nuances that a rigid system can’t handle.

Unlike traditional workflows, which require a human to give prompts and feedback throughout the process, agentic AI workflows allow agents to act autonomously—searching, analyzing, and taking action to complete tasks.

When implemented effectively, AI agents can take on repetitive and manual tasks with little supervision, helping businesses scale without increasing headcount.

What makes a workflow “agentic”?

Agentic AI workflows stand out from traditional systems by their ability to act autonomously, reducing the need for human input and supervision.

A workflow is considered "agentic" when it involves AI systems that:

Perform tasks autonomously to achieve simple or complex goals.
Adapt to changes and learn from results to improve over time.
Operate in diverse and unpredictable environments with minimal oversight.

Goal complexity

Agentic workflows handle tasks beyond simple repetition, that require problem-solving and adaptability. Goal complexity refers to both the difficulty of a task and the breadth of objectives an AI agent must achieve.

Simple goals involve well-defined tasks with limited variability, such as scheduling meetings, managing reminders, or booking travel. These tasks follow clear rules and require minimal adaptation.
Complex goals require handling dynamic conditions, large datasets, and strategic decision-making, such as optimizing supply chain logistics across regions or managing customer service at scale. These tasks demand real-time adjustments and prioritization.

Environmental complexity

The environment where AI operates affects its ability to make decisions and adapt. Environmental complexity refers to the variety and unpredictability of factors an AI system encounters while completing tasks.

Predictable environments: These follow clear patterns, like production lines. AI can schedule material orders, monitor machinery for maintenance, and optimize workflows based on consistent inputs and expected outcomes.
Unpredictable environments: Customer service, for example, involves unexpected scenarios. AI agents must handle a wide range of customer queries, including ones they haven’t encountered before. This requires real-time adaptability to manage diverse and unpredictable inputs effectively.

Adaptability in agentic AI

Adaptability is key for agentic AI systems, enabling them to adjust to new or unexpected situations. While some AI systems refine their performance through continuous learning, many improve through periodic updates based on new data and feedback. For instance, customer service AI agents might initially struggle with unfamiliar inquiries. Over time, with supervised updates or improved training, it can refine its responses and better anticipate similar challenges.

Independent execution

Independent execution is what sets agentic AI workflows apart. This means the AI can complete tasks and achieve goals without constant human oversight. It makes decisions and takes actions on its own, allowing for efficient, scalable task completion.

Want to dig further into the observability of agentic AI? Our podcast episode, Deep Learning with PolyAI: What the #$%& is agentic AI anyway? , goes in on this technology’s hive structure and so much more.

What are the benefits of agentic AI workflows?

1. Increased efficiency

Unlike traditional tools that assist rather than act, agentic AI can handle tasks autonomously. Whether handling custom inquiries, processing transactions, or managing logistics, these workflows minimize manual effort and free up resources for higher-value work.

2. Improved customer experience

In a customer service setting, agentic AI enhances customer experience by reducing wait times, personalizing interactions, and resolving issues faster. Instead of frustrating IVR menus or slow response times, AI agents can engage in natural conversations, anticipate needs, and take action instantly for a more enjoyable and efficient end-to-end customer service experience.

3. Improved user experience

Agentic AI simplifies decision-making by reducing users' cognitive load. Instead of presenting endless options, it can filter through possibilities, refine results, and even take action, whether booking travel, adjusting schedules, or resolving customer service issues, delivering faster, more seamless experiences.

4. Scalability without additional headcount

Agentic AI can handle multiple tasks simultaneously, processing large volumes of data and actions in parallel. This means businesses can scale operations without adding headcount, whether it’s generating thousands of quotes, processing claims, or optimizing supply chain logistics.

5. Cost saving and digital transformation

Automating complex workflows reduces the need for manual intervention, cutting costs and accelerating digital transformation. Companies no longer have to build expensive, bespoke integrations; instead, agentic AI can interact with existing systems, streamlining operations without requiring an extensive overhaul.

6. Improved decision making

Businesses can make better decisions by analyzing large amounts of data in real time and adapting to changing conditions. Unlike traditional automation, which follows fixed rules, agentic AI can adjust its actions based on new information. This leads to better outcomes, like smarter pricing, faster risk detection, and more efficient logistics.

What are the risks of agentic AI workflows?

Loss of control

As agentic AI systems become more autonomous, there's a risk of losing control over the decisions and actions these systems take on behalf of individuals or organizations if they aren’t properly monitored.

Unpredictability and lack of transparency

The more complex the AI system becomes, the harder can be to predict or understand its actions. This lack of transparency, combined with the AI’s ability to act on its own without human input, poses a risk in terms of understanding why certain decisions were made.

Autonomy with oversight

Like any new technology, agentic AI has risks. While it can make decisions or automate processes, to prevent unwanted behavior, enterprises must set clear boundaries that let AI operate independently while staying within strict business rules.

3 potential use cases for agentic AI workflows

1. Retail use: Streamlining refunds for damaged products

For retailers, handling refunds for damaged products can be slow and manual and often requires customers to fill out forms and wait for approval. With agentic AI, the process becomes faster and more seamless.

2. Healthcare: Automating patient scheduling and follow-ups

Healthcare providers often face challenges with scheduling appointments and following up with patients, especially as demand increases. Manual scheduling can lead to long wait times, which may discourage patients from reaching out.

86% of patients say a positive experience is their top factor when choosing a healthcare provider.

3. Banking: Automating account inquiries and transaction history requests

Customers often face long wait times and manual verification when inquiring about account details or transaction history. Agentic AI can simplify this by offering quick, accurate responses. When a customer calls, an AI agent can verify their identity and provide account details, transaction updates, or assistance with issues like lost cards without needing a human agent.

Building a balanced strategy for implementing smart agentic AI workflows

Agentic AI represents a real shift in how businesses think about automation. Instead of tools that wait for instructions, these systems act on their own to analyze information, make decisions, and learn as they go. When implemented well, agentic AI workflows can reduce manual work, improve customer and user experiences, and help organizations scale faster without adding headcount. But with this autonomy comes responsibility. Clear rules, thoughtful oversight, and continuous improvement are key to unlocking the benefits while managing the risks.

Speak to our team today