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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.
By understanding the characteristics that define agentic AI workflows, you can design systems that maximize efficiency and effectiveness.
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.
As tasks become more complex, AI systems must deliver higher reliability, speed, and safety. While agentic AI can improve over time, its learning depends on structured feedback, better data, and iterative refinements, similar to how employees develop knowledge through experience and training.
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.
Agentic workflows succeed in complex environments by interpreting different inputs, handling ambiguity, and adjusting their actions as needed. Unlike traditional automation, they can learn from new situations, apply reasoning, and operate with more independence in dynamic conditions.
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. Adaptable AI not only operates more efficiently but also handles complexity better, making it a valuable asset in dynamic environments.
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.
For example, AI managing office schedules can automatically resolve conflicts, prioritize tasks, and send confirmations without needing approval at every step. For more sensitive actions, like making payments, the AI might still request user input, but most tasks are handled autonomously.
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?
Agentic AI workflows unlock new levels of efficiency, adaptability, and scalability for businesses. By allowing AI systems to act with minimal supervision, companies can reduce operational friction and improve service delivery.
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. By handling repetitive tasks, agentic AI also frees up employees to focus on more strategic work, reducing decision fatigue and improving overall productivity.
What are the risks of agentic AI workflows?
Businesses that want to innovate with agentic AI are understandably cautious about reliability, security, and unexpected risks. However, having ways to monitor AI, catch errors, and allow human oversight is key to making it work successfully.
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.
This could be risky in situations where AI systems are entrusted with sensitive tasks such as making financial transactions. It could potentially lead to situations where AI takes actions that could harm users or businesses, either due to mistakes or the lack of proper guardrails.
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.
Developers can make agentic AI safer by setting clear default behaviors. These defaults are rules the AI follows when users don’t provide specific instructions. For example, an AI helping with trip planning doesn’t need all the details. It can follow basic rules like “don’t spend the user’s money without permission” or “choose the least disruptive option.”
Users should still have the ability to override these defaults when necessary, but the AI should also be programmed to refuse harmful actions, even if requested. This ensures the system stays safe and reliable.
3 potential use cases for agentic AI workflows
Agentic AI workflows are helping businesses automate tasks and improve efficiency. Here are three examples of how agentic systems can be used.
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.
In this approach, the AI agent first asks the customer to describe the damage. Instead of relying on text alone, the AI generates a secure link via an API call to a third-party LLM-powered tool, allowing the customer to upload photos or videos. Another LLM then analyzes the images and summarizes the issue automatically.
By combining multiple AI systems, businesses can speed up refund approvals, reduce manual reviews, and create a smoother customer experience.
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.
Agentic AI can help by automating tasks like appointment scheduling, sending reminders, and rescheduling based on doctor availability and patient preferences. It can also follow up after appointments to confirm treatments, remind patients of upcoming visits, and track recovery progress.
By automating these processes, AI reduces the workload for administrative staff and helps ensure that patients receive timely attention, improving efficiency and patient satisfaction.
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.
By eliminating the need to wait on hold or complete forms, the AI creates a faster, more seamless experience. This allows bank staff to focus on more complex matters, ultimately improving customer satisfaction.
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.
The real power of agentic AI is about knowing when and how to deploy it into your existing infrastructure. Speak to our team today about how PolyAI can help you implement the world’s most lifelike and adaptable AI agent to deliver effortless CX at scale.
