What are AI Agents?
AI Agents are systems that can autonomously handle complex tasks, continuously self-improve, and act on behalf of a human or other systems.
AI agents make decisions, solve problems, interact with external environments, and execute actions. All without human intervention.
Harness the power of AI agents
AI agents do far more than just power chatbots. They are transforming industries by automating complex tasks, optimizing workflows, and making data-driven decisions.
Agentic bots help businesses operate smoothly and achieve their goals faster and cheaper, whether that’s in customer service or supply chain management.
They are reshaping industries by independently managing customer inquiries more efficiently, automating processes, and even optimizing supply chain logistics to cut costs and improve delivery times.
Explore the full spectrum of AI agent capabilities to drive innovation and maintain a competitive edge in your industry!
Use cases of AI agents
AI agents can solve complex problems in a wide range of enterprise applications. Among many other capabilities, agents help automate software design, manage IT processes, generate code, and support conversational tasks.
AI agents in customer experience
AI agents are changing the retail landscape by improving both in-store and online customer experiences.
They can act as virtual shopping assistants, offering personalized product recommendations, managing inventory in real time, and making checkout processes smoother.
By understanding customer behavior and preferences, AI agents make the shopping journey easier, leading to higher customer satisfaction and better sales results.
With straightforward no-code solutions, retailers can integrate AI agents quickly, making operations more efficient and enhancing the overall experience.
Consumer Packaged Goods (CPG)
In the CPG industry, AI agents are making operations more efficient across the entire production chain.
Agents help companies streamline their processes by predicting demand, or managing inventory to running targeted marketing campaigns.
They analyze consumer data in real time to forecast trends, automate restocking, and enhance product recommendations, leading to a responsive, data-driven approach that improves both customer satisfaction and profitability.
Healthcare innovations driven by agents
AI agents are delivering meaningful change across healthcare by streamlining workflows and improving problem-solving.
Multi-agent systems can assist with everything from planning patient treatments in emergency departments to managing drug administration.
By taking on these crucial—yet time-consuming—tasks, AI agents free up medical professionals to focus on delivering urgent and high-quality care, ultimately improving both patient outcomes and operational efficiency.
Industrial use cases of AI agents
In the industrial sector, AI agents are revolutionizing operations by automating complex processes—whether it’s predictive maintenance for machinery, optimizing supply chains, or managing quality control, AI agents enable businesses to increase efficiency and reduce downtime.
By leveraging real-time data, these agents help organizations make smarter, faster decisions that keep operations running smoothly.
Benefits of AI agents
The difference between agentic and non-agentic AI chatbots is that agentic AI can take independent actions and make decisions on its own, while non-agentic AI simply responds to user inputs without initiating anything on its own.
Below are some of the benefits of AI agents.
1. Task automation to drive efficiency and reduce costs
AI agents take on repetitive or complex tasks such as scheduling, data entry, and monitoring. They can handle team scheduling by analyzing calendars, checking availability, and setting up meetings—all without manual effort.
They also manage data entry at scale, pulling information from various sources, verifying it, and updating systems (like Jira or HubSpot) in real time. By automating processes that usually need human input, businesses can redirect resources to more valuable activities, boosting productivity and cost-effectiveness.
AI agents also reduce human error, speed up decision-making, and streamline operations while maintaining high performance and quality. This enables teams to focus on strategic initiatives, such as innovation driven by human insights or customer engagement.
2. Enabling rapid responses and real-time decision making
AI agents use real-time data to make instant decisions, making them highly valuable for fast-paced industries like retail, supply chain management, and finance.
They process live web analytics, social media trends, and order data to adjust inventory or update pricing instantly, ensuring the right products are promoted based on current demand.
By responding to data in real time, businesses remain agile and competitive, quickly capitalizing on new opportunities or addressing risks like stock shortages or sudden market changes.
3. Enhancing performance, insight and collaboration
AI agents excel in collaborative settings, working together to solve problems and achieve shared goals. For example, one agent might monitor inventory levels while another tracks shipping schedules, enabling real-time coordination and optimization of logistics.
By pooling resources and learning from each other, these agents enhance overall performance, making multi-agent systems more efficient and effective compared to isolated, task-specific solutions.
4. Context-specific, high quality responses
AI agents go beyond simply executing pre-set instructions; they adapt in real time, using a wide range of data and external tools to improve their responses.
For instance, an AI-powered agent in customer service can assess a user’s past purchases, current browsing behavior, and product reviews to recommend the most suitable options immediately.
By leveraging extensive data points, AI agents provide precise, context-aware responses that directly meet user needs.
As a result, users not only get relevant information but also experience quicker and more efficient interactions.
How do AI agents work?
AI agents are built to operate autonomously, combining decision-making with real-time access to external resources, such as order databases, social media trends, or past interactions. Unlike traditional models that depend solely on pre-existing data, AI agents can actively search for new information, adapt to evolving inputs, and adjust their actions based on live feedback.
Powered by large language models (LLMs), these AI agents—often called LLM agents—extend beyond the limitations of traditional LLMs. Instead of working with only static data, they can access current information and automate complex processes. This enables them to independently break down tasks and achieve goals with minimal human intervention.
1. Defining goals and breaking down tasks
Once a goal is set, the AI agent takes charge by analyzing the objective and breaking it into smaller, manageable components. Rather than sticking to pre-programmed instructions, it strategizes the optimal approach to achieve the goal, adapting its plan as needed.
For simpler tasks, the agent can jump right in and execute.
For more complex challenges, it begins by formulating a detailed action plan, dividing the main objective into subtasks that can be handled step by step.
AI agents need a goal, but once it’s set, they work independently. Users define what needs to be done and provide access to the tools an agent might need.
2. Adapting and sourcing information in real time
When an agent lacks the full context or data to complete a task, it actively searches for the missing pieces—whether that’s through accessing databases, making API calls, or even consulting other AI agents specialized in different domains.
One of the key strengths of AI agents is their ability to fill in gaps by tapping into external tools and resources.
3. Executing tasks and delivering results
Once the agent has all the required information and a clear strategy, it moves on to execution. For AI agents, execution isn’t just about providing recommendations—they take direct actions to meet the goal.
This could mean automating workflows, starting processes, or working with other systems. The agent uses its connections to existing tools and resources, like ad systems, calendars, or various APIs, to put the plan into action.
The agent can automatically launch targeted campaigns, schedule posts across multiple platforms, or adjust ad spend based on live performance metrics.
Imagine a scenario where an AI agent is tasked
with helping a user plan a marketing strategy.
The agent goes beyond relying on pre-existing knowledge; it actively gathers up-to-date data, such as consumer behavior trends, competitor analysis, and industry insights. It may consult external analytics tools, review recent campaign results, or collaborate with other specialized agents for insights into social media performance or audience segmentation.
This includes automating workflows, initiating processes, or integrating with other systems. The agent leverages its connections to existing tools and resources—like ad platforms, calendars, or various APIs—to execute the plan effectively.
Each new piece of information refines its approach, allowing the agent to design a marketing strategy that is not only up-to-date, but also highly targeted and effective.
4. Continuous learning and self-improvement
Each interaction, whether from the user or other agents, helps AI agents refine their understanding and improve future performance. They don’t just act; they reflect, storing information and adapting their methods to better match user preferences.
After presenting the initial marketing strategy, the agent logs feedback from the user or other relevant data sources. If the proposed strategy doesn’t fully meet the user’s goals, the agent learns from this input, refining its approach to offer more precise, tailored recommendations moving forward.
This continuous feedback loop ensures that each iteration becomes increasingly aligned with the user’s specific objectives and evolving market conditions.
AI agents are not static—they continuously learn and adjust based on the feedback they receive.
Types of AI agents
Model-based reflex agents
Model-based reflex agents enhance basic models by incorporating memory, which allows them to build an internal representation of their environment based on past interactions.
This capability helps them make decisions even in unpredictable situations. Although they still use preset rules, they update their models in real time to adapt to changing conditions.
For example, a model-based reflex agent might monitor network activity, recording past cybersecurity incidents to build a memory of potential threats. When a new threat arises, it compares the current situation with past data and implements an updated defense, such as reconfiguring firewalls or isolating compromised network areas automatically.
Goal-based agents
Goal-based agents do more than react—they plan.
These agents break down complex objectives into actionable steps, constantly reassessing progress and adjusting their approach as conditions change. They execute decisions by carrying out specific tasks in sequence, while remaining flexible enough to adapt as needed to achieve the overarching goal.
For example, a goal-based agent might oversee the entire fulfillment process. It determines the most efficient picking routes for robots, adjusts these routes dynamically as new orders arrive, and re-prioritizes tasks based on inventory levels and delivery deadlines. The agent executes these decisions by sending commands directly to robotic systems, ensuring that products are packed and shipped as quickly and efficiently as possible.
Utility-based agents
Utility-based agents elevate decision-making by evaluating multiple possible actions and selecting the one with the highest value or ‘utility’.
They continuously weigh trade-offs such as time, cost, and resource availability to determine the most effective course of action.
For example, a utility-based agent might analyze energy demand, pricing trends, and renewable energy availability. It calculates the best way to distribute power and implements this by automatically adjusting energy flow between sources like wind farms, solar arrays, and power plants. The agent constantly rebalances supply and demand to ensure efficient energy distribution with minimal waste.
Learning agents
Instead of simply adapting, learning agents evolve. They continuously learn from their actions and outcomes, refining their approach over time.
These agents apply what they learn by modifying how they perform tasks based on previous interactions, becoming more efficient and accurate with each iteration.
For example, a learning agent might track customer interactions—like purchase history and browsing behavior—to recommend products. After each interaction, it refines its recommendation algorithm, dynamically updating product suggestions, pricing strategies, and promotional offers in real time to create increasingly personalized shopping experiences that drive conversions.
AI Agents in your AI Strategy
AI agents are powerful tools, but their success relies on effective integration into your broader AI strategy. Rather than serving as quick fixes for isolated tasks, AI agents thrive when they are part of a coordinated ecosystem that strengthens existing processes and drives innovation.
Aligning with business objectives
To get the most out of AI agents, they need to be introduced with a clear understanding of how they align with your business’s long-term goals. Whether automating complex workflows, improving customer experiences, or optimizing decision-making, AI agents should be used to extend and enhance your existing capabilities.
For example, in customer service, an AI agent that automates inquiries can deliver the best results when integrated with CRM systems to ensure seamless support.
Collaborating with other tools, data points, and frameworks
AI agents work best when they operate alongside other AI solutions. This integration creates a robust, interconnected AI infrastructure where agents not only complete tasks but also contribute valuable data to the larger decision-making process.
AI agents can connect with machine learning models, data analytics platforms, and predictive systems, providing real-time, actionable insights that complement your existing tools.
Data as the foundation
For AI agents to operate effectively, the quality and structure of your data are two critical components. Ensuring that your data architecture is well-structured and accessible will allow agents to adapt to dynamic business needs and provide accurate insights.
Agents rely on real-time data, APIs, and other external tools to work efficiently.
Ongoing adaptation and optimization
AI agents are dynamic systems that learn and evolve. To maximize their value, their performance should be continually monitored and optimized. This ongoing adaptation ensures that AI agents keep improving and continue to add value well beyond their initial deployment.
Optimization includes updating data sources, retraining models, and refining workflows as business needs evolve.
Integrate AI Agents into your strategy
Integrating AI agents into your business goes beyond technology—it’s about transforming how your organization works and evolves. To unlock their full potential, you need a strategic approach that seamlessly integrates them into your broader AI initiatives.
