Agentic AI, derived from the word “agent”, is a system that enables AI agents to perform real-world tasks autonomously. This means they can make decisions and take actions to achieve goals without human intervention.
These systems can handle complex, multi-step workflows, and can plan and execute tasks to complete their goals. By bridging decision-making and execution, agentic AI can provide meaningful and conversational support to customers.
How does Agentic AI work?
Agentic AI has a structured process that allows it to make independent but intelligent decisions. It uses decision trees that help it triage issues and determine a course of action that leads to faster resolution times. Here’s an example of an agentic AI decision tree for an AI travel agent:
At first glance, this might look overwhelming so let’s simplify how it works. Consider an example of a customer reaching out with a technical issue. How will agentic AI help resolve this ticket without any human intervention?
Draws on an extensive range of data
First the AI collects data from multiple sources to build context and help make decisions:
- Databases and APIs
- Customer data (behavioral data and conversation history)
- User prompts
Example: A retail customer reaches out about an issue with connecting their new smart watch to their smart phone. Agentic AI will pull the customer purchase history, product information and troubleshooting guidelines, and similar cases that were previously resolved. In this case, the AI might identify that this model of smart watches has been having connectivity issues after the latest software update.
Connects to task-specific AI agents
Agentic AI will determine which customer service path to send the customer on. Depending on the set up, brands can have multiple customer service chatbots that focus on specific issues and use cases, or multiple message flows that can guide customers through resolving their issue.
- Device diagnostics chatbot
- Troubleshooting chatbot
- Customer support chatbot
Example: Agentic AI will seamlessly direct the customer to a troubleshooting chatbot that will give them step by step instructions on how to reboot their smartphone or smart watch.
Finds the optimal solution
With the use of advanced algorithms, the AI system will evaluate the data and decide on the best course of action:
- Run predictive models
- Weighing risks and benefits
- Adapting to changing information or inputs
Example: The agentic AI will determine the best ways to troubleshoot the problem. The AI tool can run decision trees or AI-driven diagnostics and will prioritize the quickest and most effective solution for the customer. In this instance, the intelligent AI determines that re-starting and re-pairing both devices works in 85% of similar cases.
Takes actions if authorized
Before making decisions the agentic AI always checks for approval mechanisms like:
- Human confirmation for critical actions
- Predefined rules prompting automated responses
- Escalating the issue to a human agent
Example: If troubleshooting and device diagnostics still doesn’t solve the issue, the agentic AI system determines the device must be faulty and offers a replacement under warranty. The AI agent can provide a return label and direct the customer to a customer support chatbot that will help them with their replacement.
In a complicated situation like this, a human agent would have intervened after the troubleshooting chatbot failed to solve the issue. But agentic AI doesn’t just respond to customer queries like a traditional chatbot – it actively works to solve the problem. It is reshaping the brand-to-user conversational experience by single handedly resolving complex tasks on behalf of both the user and the brand.
Agentic AI vs. Generative AI
It’s sometimes hard to keep up with all the terms and buzzwords circulating around AI. With a multitude of tools and systems available, how do brands know what to use and when? Generative AI (GenAI) is arguably one of the most popular tools out there, so what is it exactly and how does it compare to agentic AI?
Generative AI
Great for creating new and unique content like text, images, video, spotting patterns
- Level of independence: Medium
- Learns by: Training data and algorithms
- Ideal for: Marketing
- Strengths: Creativity and flexibility in output generation
Agentic AI
Great for making decisions and completing tasks to achieve a goal
- Level of independence: High
- Learns by: Experience and feedback
- Ideal for: Customer service
- Strengths: Autonomous, completing tasks
So, while GenAI might be great for generating new content for marketing emails, ads, or even creative responses for chatbots, agentic AI focuses more on solving customer issues and reducing the workload on overloaded human agents. They are each highly useful for their particular use cases – both with the aim of speeding up processes and operations and easing the workload on employees while keeping the customer satisfied.
Agentic AI vs. Rule-based chatbot
So, GenAI might be better suited for creative tasks and not administrative problem solving in customer service, for that we need a different AI. Up until now, most brands have relied on basic rule-based chatbots to help solve customer service queries. And while this solution might a great option in some cases, it can’t bring the customer experience to the same level of ease as agentic AI can.
70%
of brands use chatbots
74%
still heavily rely on human agents to resolve majority of issues
Let’s compare agentic AI to a traditional chatbot used for customer service:
Agentic AI
Best for decision making and solving complex tasks
- Human intervention: Minimal
- Strengths: Independence, adaptive, goal-driven
- Customer service uses: Proactive issue resolution, personalized retention, multi-step troubleshooting
- Key difference: Thinks, adapts, takes autonomous action
Rule-based bot
Best for resolving simple repetitive tasks
- Human intervention: High
- Strengths: Stability, reliability, predictability of answers
- Customer service uses: FAQs, order tracking, appointment booking
- Key difference: Follows strict scripts and predefined flows
The traditional chatbot is great for straightforward and simple use cases that customers can rely onto get fast answers. Agentic AI takes the customer experience to the next level by understanding how to resolve more complex problems, proactively identifying opportunities and risks, and working towards a goal for the customer. This is the tool that would allow brands to level up their Conversational CX Maturity, by truly relying on AI assistants while providing smooth experiences to the customer.
Benefits of agentic AI
Agentic AI is a tool that has tangible benefits for enterprise level, and medium sized businesses:
For enterprises
- Enhanced customer experience: AI agents deliver consistent, personalized interactions at scale, boosting customer satisfaction and retention.
- Operational cost reductions: Automating complex workflows reduces dependency on human agents and trims operational expenses.
- Compliance and data residency: A strong AI infrastructure ensures data is processed within compliant, regional data centers, meeting even the strictest regulatory requirements.
For medium sized business
- Scalability without overheads: These businesses can offer 24/7 customer support and manage growing customer bases without needing large teams.
- Improved productivity: Automation reduces time spent on repetitive tasks, allowing teams to focus on strategic priorities.
- Faster time-to-market: Deploy pre-built, composable AI solutions to offer services like intelligent FAQs, lead qualification, and conversational shopping.
Use cases for agentic AI
Troubleshooting
To reduce customer effort and speed up time to resolution, agentic AI can quickly determine technical issues and how to resolve them based on input and contextual data it can source. They can triage support tickets to be redirected to the right AI chatbot or agent for better technical support.
Proactive support
Agentic AI can help brands get ahead of any issues by identifying potential problems like late delivery, network outages, and even identify dormant customers that are at risk of churning.
Warranty and claims submission
Agentic AI can offload time consuming warranty or claims submissions from human agents by guiding customers through the process and ensuring their insurance claims or warranties are properly filed and submitted.
Personalized retention
To keep customers interested and loyal, brands can launch personalized retention campaigns that customers can actually interact with. For example, a streaming service AI notices a customer hasn’t used their subscription in months. It reaches out, offering a personalized discount or content recommendations based on past viewing habits.
Order modifications
It can happen that customers want to change their order or make modifications to their delivery after they place an order. Agentic AI would allow for the customer to request any changes over chat and can send confirmations that the modifications have been updated instead of burdening call center agents with repetitive work.
Conversational commerce:
AI agents can leverage conversational commerce to deliver hyper-personalized shopping experiences by proactively recommending products, guiding users through purchasing processes, and executing transactions—all based on their understanding of the customer from their personal data.
Challenges around agentic AI implementation
Like with any AI there are risks and challenges that need to be considered and mitigated to ensure you can offer customers the best and most secure experiences.
Autonomy
While autonomous decision making is a strength of agentic AI, it can also be a major challenge. At the end of the day, agentic AI can still be prone to the same challenges as other AI solutions like hallucinations.
Solution: It’s critical that brands create a balance between AI and human interactions and always keep a human in the loop to monitor decision making.
Privacy
Agentic AI sources tons of data, including customer data. Brands need to put customer data privacy at the forefront of their AI strategy, to put in safeguards around data collection and usage.
Solution: Work with an experienced AI provider that understands the ins and outs of communication compliance to ensure messages remain compliant with regional regulations.
Integration
Many brands use a wide range of platforms and systems to launch customer communications, and often they are outdated legacy systems that make it difficult to integrate AI.
Solution: Start gradually modernizing your infrastructure with AI-friendly platforms and working providers who offer top of the line support for integration.
User trust
Customers can still be quite doubtful when it comes to interacting with AI chatbots. They might not trust that their issue is being actively resolved or that their information is safe.
Solution: Ensure your AI is trained to offer clear and concise information that gives customers peace of mind and always include an option to speak with a human agent if needed to improve trust in the conversation.
What might the future of CX look like with agentic AI?
We are currently living in a future that maybe we didn’t expect to come so fast. AI can think, make autonomous decisions, and even understand human language, intent, and emotion. But an intelligent AI agent isn’t the end of this evolution. We expect to see Agentic AI grow and develop in a few ways.
Explainable AI (XAI)
To counter the hesitation users might have with interacting with intelligent AI agents, explainable AI can become a game changer. As we give AI more responsibility, transparency becomes critical. XAI will enable AI agents to give foolproof explanations on why they made decisions.
For example, a banking AI rejects a loan but is also able to justify it by replying “Your credit utilization is 80%, which is above the approval threshold of 50%”. This will build trust, reduce bias concerns and provide clear explanations behind decisions.
Self-optimization
Agentic AI can already learn from experience and feedback, but self-optimization of responses is the future. The AI would be able to recognize that multiple customers drop off at a certain point in their conversation, determine the reason why, and adjust the flow or responses to improve the conversation – all without human intervention.
Superapp collaboration
Superapps are making it easier for brands to connect with customers and give users a one-stop shop for all their needs. Agentic AI could be used to optimize the customer journey within a superapp. For example, a customer uses their superapp to order a pizza on a Friday night at 8pm to their home address. They have been inactive on their steaming platform for a few weeks, so agentic AI pushes a notification from the streaming provider on the same superapp that a new release is out tonight that they might enjoy based on their history. So, agentic AI can provide cross-platform collaboration on superapps, that are becoming increasingly popular.
Agentic AI FAQs
Agentic is derived from the word “agent” and refers to anything that has the ability to act independently, make decisions, and influence outcomes rather than just responding passively.
- Agentic AI focuses on solving issues, making independent decisions, and learns from a wide source of data, feedback and its own experiences. It’s often used in customer service use cases for business.
- GenAI creates new and creative content like images, text, videos, etc. It learns from large language models and looks for patterns that enable it to generate new content based on existing content. It’s mostly used in creative instances like in marketing.
- Agentic AI can triage customer support tickets, manage message flows, and make decisions, making it ideal for complex customer support use cases.
- Traditional chatbots follow scripts and predefined flows making them useful in repetitive or simple use cases.
ChatGPT is not an agentic AI as it does not have autonomous decision-making, long-term goal pursuit, or the ability to act independently beyond responding to user prompts.
ChatGPT is reactive, meaning it only responds to prompts but does not initiate actions or set its own goals. Although, some AI systems or AI agents that use ChatGPT with external tools aim to be more agentic by automating multi-step processes.
Agentic AI, like any AI, still requires monitoring and intervention from humans to ensure compliancy and data safety measures are being respected. So, while agentic AI might take tasks from human agents, it is not meant to replace them entirely. In fact, it allows brands to do more with the people they have instead of replacing or removing the need for human agents.
Ethics in AI is crucial to its success in business applications. Here’s a few things brands can do to ensure agentic AI remains ethical:
- Ensure AI decisions and actions are understandable and traceable
- Keep humans in the loop, especially for critical decisions
- Continuously test for and reduce biases in AI behavior
- Design AI to pursue goals aligned with ethical and societal norms
- Implement mechanisms to prevent unintended responses
- Work closely with AI providers that help with message compliancy
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