Learn how Agentic AI is using real-time data and advanced artificial intelligence to improve customer service, boosting productivity, delivering personalised support, and openingnew revenue opportunities.
In today's time, customer expectations are higher than ever. They want immediate responses, consistent experiences across channels, and solutions that feel human, even when they're not. Traditional customer support models, built on static rule-based chatbots or overwhelmed human agents, struggle to keep up.
Agentic AI represents a new generation of artificial intelligence designed to act autonomously, plan multi-step workflows, and make decisions based on context, intent, and real-time data, unlike traditional systems that simply respond to predefined input. Agentic AI can proactively engage customers, resolve issues end-to-end, and learn from every interaction.
According to Gartner's 2025, Agentic AI Will Autonomously Resolve 80% of Common Customer Service Issues Without Human Intervention by 2029
"You're designing systems that don't just respond, but act autonomously with context, judgement, and accountability." says Apurv Agrawal, Co-founder and CEO of SquadStack.
In this guide, we'll explore what agentic AI means, how it's different from traditional customer support AI, and why forward-thinking businesses are rapidly adopting it to stay ahead of rising expectations in 2025 and beyond.
What is Agentic AI?
Agentic AI is an advanced form of artificial intelligence designed to move beyond simple task execution or content generation. Unlike traditional AI systems that wait for human prompts, agentic AI is built to function autonomously, perceive its environment, set goals, plan actions, and execute them to achieve specific outcomes. At its core, Agentic AI behaves more like a digital agent than a tool. It doesn't just help users, it acts freely to perform tasks, resolve problems, and make real-time decisions. This evolution significantly shifts from reactive systems to proactive and self-directed AI.
One of the defining features of agentic AI is its ability to retain memory and context over prolonged interactions, allowing it to learn from past experiences and adjust its behaviour dynamically. For example, in a customer service situation, an agentic AI doesn't just respond to FAQs; it identifies issues like billing errors or service delays before customers report them, investigates the root cause, engages the appropriate systems via APIs, and resolves the problem. It then follows up with the customer to confirm resolution, creating a seamless, end-to-end experience.
Types of Agentic AI
Agentic AI comes in many varieties, each serving a particular function and specific levels of autonomy in custom service and beyond. Knowing the varieties can help businesses develop the correct AI solution that best suits their needs.
Reactive Agentic AI
Reactive agentic AI responds directly to customer inputs but doesn't maintain long-term context or plan beyond the immediate interaction. It's more advanced than basic rule-based chatbots, using natural language understanding to handle queries dynamically.
- Use Case: Answering FAQs and processing simple transactions like password resets.
- Example: An AI that helps you check your bank balance after authenticating your identity.
Deliberative Agentic AI
This type of AI agent possesses memory and planning capabilities, allowing it to manage multi-turn conversations and execute multi-step tasks. It can remember past interactions and tailor responses accordingly, creating a more personalised experience.
- Use Case: Handling complex customer service requests, like troubleshooting technical issues or booking appointments.
- Example: An AI agent that walks users through troubleshooting their internet connection over several messages.
Collaborative Agentic AI
Collaborative agentic AI works alongside human agents, augmenting their capabilities rather than replacing them. It handles routine tasks and provides real-time recommendations or insights, helping human agents be more efficient and effective..
- Use Case: Assisting AI call centre agents by suggesting the best actions or providing customer history during live calls.
- Example: An AI assistant that listens to customer calls and surfaces relevant data to the agent instantly.
Autonomous Agentic AI
The most advanced form, autonomous agentic AI, operates with minimal human oversight. It proactively identifies customer needs, takes initiative to solve problems, and even negotiates or upsells services based on real-time data.
- Use Case: End-to-end management of customer service cases, from issue detection to resolution and follow-up.
- Example: An AI system that monitors service outages, proactively contacts affected customers, offers compensation, and schedules technician visits without human intervention.
Summary Table for Types
This table breaks down the different types of Agentic AI based on their capabilities, autonomy, and practical use cases across customer service and support operations:
How Agentic AI Works: Step-by-Step Breakdown
Agentic AI goes beyond responding to commands, it currently plans and executes tasks to complete a goal. This ability comes from a complex, organised workflow that blends artificial intelligence with human-like agency. Let's break down how it works in a real-world customer service scenario.
Step 1: Input Gathering
Agentic AI begins by listening to or receiving customer input. This input can come from various sources, some of which are:
- A chat message
- A voice call transcript
- A form submission
- Backend alerts or triggers (e.g., order delay detected)
Using Natural Language Processing (NLP), the AI interprets the intent behind the message and extracts key details like names, dates, and issue types.
Example: A customer says, "My internet is down, and I've already tried restarting the router."
→ The AI understands the intent: Report technical issue, and extracts: Tried restarting already.
Step 2: Context Building and Memory Access
Agentic AI accesses past interactions, account details, and real-time data to build context, which includes:
- Past tickets or chats with the customer
- CRM data (name, location, plan, previous issues)
- Product/service usage data
Agentic AI uses retrieval-augmented generation (RAG) or vector search to pull relevant knowledge from internal databases, documentation, or knowledge bases.
Example: The AI sees that this customer had a similar issue last month and that a firmware update was pushed two days ago.
Step 3: Planning a Multi-Step Resolution
Unlike rule based bots, agentic AI creates a step-by-step plan to reach the end goal. This uses AI planning algorithms or reinforcement learning models that evaluate multiple options and simulate outcomes.
The AI can:
- Choose what action to take first
- Decide if human intervention is needed.
- Prioritise urgency based on customer history or issue severity.
Example: The AI first decides to run a diagnostic, then escalates to technical support if the line check fails.
Step 4: Decision-Making and Task Execution
Next, the AI autonomously takes action and this could mean:
- Running API calls to trigger backend processes (e.g., diagnostics)
- Sending status updates to the customer
- Rescheduling services or initiating refunds
- Forwarding to a human with full context
Example: The AI runs a check and finds a local outage. It informs the customer, logs the case, and schedules an SMS update in 2 hours.
Step 5: Continuous Loop Feedback
Agentic AI evaluates the outcome of its actions and learns from success or failure. It collects feedback in real time through:
- Customer responses ("Thanks, that helped!" or "Still not working.”)
- Internal system feedback (e.g., task failed, error code triggered)
- Agent corrections (if escalated)
The AI refines future decisions using feedback, part of its reinforcement learning framework.
Example: If several customers report that restarting a router doesn't help after a specific update, the AI stops recommending it first and flags the issue internally.
Agentic AI doesn't just follow a flow it thinks, decides, adapts, and learns across every interaction. This makes it ideal for managing complex, high-volume customer service operations with precision and empathy.
What are the Top Agentic AI Benefits and Use Cases?
Agentic AI is not just a technological upgrade, it's a strategic enabler that helps businesses automate complex tasks, personalise customer experiences, and drive operational excellence. Let's explain its key benefits and how leading industries use it today.
Key Benefits of Agentic AI
Agentic AI unlocks smarter, faster, and more scalable support by blending autonomy with contextual understanding. Here are the top benefits businesses can expect.
24/7 Autonomous Support at Scale
Agentic AI can handle thousands of customer interactions simultaneously, across time zones, without breaks or delays.
- Impact: Reduces first response time to near zero.
- Stat: According to a 2024 Deloitte report, 64% of customers now expect businesses to be available and responsive 24/7.
Higher First-Contact Resolution (FCR)
By intelligently resolving issues without human handoffs, agentic AI increases FCR rates.
- Impact: Improves customer satisfaction and reduces operational costs.
- Stat: Companies using autonomous AI agents have reported up to a 30% increase in FCR, per Gartner.
Reduced Agent Workload and Burnout
Agentic AI handles repetitive and routine queries, freeing human agents to focus on high-value, emotionally complex tasks.
- Impact: Boosts employee satisfaction and reduces churn.
- Expert Insight: "Agentic AI doesn't replace your team, it amplifies their impact," AI Operations Lead at CX FutureLab.
Personalised and Context-Aware Responses
Agentic systems maintain memory and use customer data to deliver hyper-personalised interactions across channels.
- Impact: Builds trust and improves long-term loyalty.
- Example: Recommending relevant products or resolving repeat issues based on history.
Cost Efficiency at Enterprise Scale
With fewer agents to manage routine interactions, organisations save significantly on headcount and overhead.
- Impact: Depending on automation depth, customer service costs can be reduced by 20–40%.
- Stat: A 2025 McKinsey analysis found that AI-driven customer operations could save global businesses up to $1 trillion annually.
Top Agentic AI Use Cases in the Real World
These are some ways mentioned below that show how Agentic AI is helping companies to grow and enhance their systems
E-commerce Support
AI can help in E-commerce support in various ways:
- Automates order tracking, returns, and refund processing.
- Proactively engages customers with delivery updates or cart recovery messages.>
- Example: An AI agent detects a delivery delay and offers an apology coupon without the customer asking.
Telecom & ISP
AI Call services are beneficial for the growing world in many ways:
- Troubleshoots connectivity issues using backend diagnostics.
- Schedules technician visits or escalates complex cases autonomously.
- Example: AI detects service disruption and contacts affected users with status updates and estimated fix times.
Banking and Financial Services
AI is not limited; now they are helping the banking sector to enhance their growth, look how they are doing it:
- Assists with KYC verification, transaction disputes, and loan application FAQs.
- Detects fraud patterns and warns users in real time.
- Example: A customer messages about an unfamiliar charge; the AI flags potential fraud and freezes the card until verified.
Healthcare
Agentic AI also contributes to healthcare by supporting healthcare professionals in handling their queries, some of which are listed below:
- Handles appointment booking, prescription refills, and symptom triaging.
- Sends reminders and follow-up care instructions.
- Example: AI agent triages incoming symptoms and directs non-urgent cases to telehealth consultations.
Travel and Hospitality
Tourism is also growing with an increasing economy, many people have issues connecting to solve their issues, but AI can automate the process by:
- Manages flight changes, hotel bookings, and customer complaints.
- Offers real-time recommendations for upselling during high-traffic seasons.
- Example: After a cancelled flight, the AI proactively rebooks the customer and sends updated boarding info.
Agentic AI vs Generative AI: What's the Difference?
While Generative AI and Agentic AI are groundbreaking branches of artificial intelligence, they serve distinct purposes and operate differently. Understanding their roles is essential for businesses strategically implementing AI in customer service and other domains.
What is Generative AI?
Generative AI refers to models that can create new content like text, images, code, music, and more based on training data. Tools like ChatGPT, DALL·E, and Google Gemini fall under this category.
- Key Function: Content generation
- Powered by: Large Language Models (LLMs) and transformer-based architectures
- Typical Use Cases:
- Writing emails or articles
- Answering questions
- Generating code snippets or summaries
- Creating visuals or design drafts
Generative AI is passive, it produces output when prompted, but doesn't act independently.
What is Agentic AI?
Agentic AI, on the other hand, is designed to take autonomous action toward a goal. It combines generative capabilities with decision-making, memory, planning, and execution.
- Key Function: Goal-oriented action and autonomous task management
- Powered by: LLMs + Planning algorithms + Memory + API integrations + Reinforcement Learning
- Typical Use Cases:
- Resolving customer issues without human intervention
- Proactively handling service outages or order delays.
- Coordinating multi-step workflows (e.g., booking, payments, follow-ups)
- Operating as digital employees or service agents
Here is a side-by-side comparison between Generative AI and Agentic AI
Also, take a look at when to use Generative AI and Agentic AI according to use:
There is a line in AI World which says, " Generative AI is the engine. Agentic AI is the driver. You need both to go far."
Final Thoughts: The Future Is Agentic AI
As businesses compete to give faster, more intelligent, and more human-like experiences, Agentic AI is emerging as a game-changer. Unlike traditional AI or basic chatbots, agentic systems don't just react they perceive, plan, and act independently. They resolve issues before customers notice, streamline backend operations, and unlock new revenue channels by driving proactive engagement.
Whether handling thousands of support queries simultaneously or autonomously closing the loop on complex, multi-step workflows, Agentic AI offers scalability and precision, exactly what modern CX demands in 2025 and beyond.
Why Choose SquadStack's AI Agent as a Solution For Presales and Customer Support?
At SquadStack, we're building the next generation of AI-powered customer engagement tools beyond scripted voicebot and Chatbot. Our proprietary Humanoid Agent combines the power of agentic AI with deep domain expertise to deliver:
- 24/7 autonomous support that feels human
- Context-aware conversations across voice and text with their conversational AI
- Dynamic decision-making to resolve issues end-to-end
- Seamless integration with CRMs, APIs, and internal tools
With SquadStack, businesses can automate intelligently without losing the personal touch.
