AI agents are changing the game — but what are they, and why do they matter? This not-too-deep dive covers the basics of agents, when to use them, and the value they create.
Written by
Kyle Mistele
Introduction
AI agents have rapidly become the hottest topic in artificial intelligence - it seems like everyone’s talking about them. Yet as much as the word “agent” fills up our LinkedIn feeds and social media, definitions for it can be hard to find. And it’s not necessarily obvious what an agent is, much less how it differs from products like ChatGPT.
This lack of clarity makes it difficult for business leaders, decision-makers, and even technical professionals outside of the AI space to engage in meaningful conversations, to evaluate options and solutions, and to make informed purchasing decisions.
In this post, we’ll break down what AI agents are, how they compare both to traditional “bots” and to other AI-powered systems, and where they deliver the most value. Whether you’re a business leader exploring AI & agentic solutions for your business or a technical professional looking to understand this emerging field, this guide will provide a clear framework for thinking about AI agents and their role in business.
So, what is an agent?
At its core, an AI agent is an autonomous system that can perceive its environment, make decisions, and take actions to achieve specific goals without continuous human supervision.
AI agents are built on top of Large Language Models (LLMs) which are a specific type of deep learning model that excels at language processing. Examples of popular LLMs are OpenAI's GPT-4 (which powers ChatGPT), Google’s Gemini, and Meta’s Llama models.
Unlike traditional software that follows rigid, pre-programmed instructions, AI agents leverage LLMs’ language understanding and generation capabilities to plan sequences of actions, use tools to interact with their environment, and adapt to feedback while working toward objectives.
Key Characteristics of AI Agents
AI agents stand out from other AI-powered systems because they:
Act with autonomy – They don’t just respond to single prompts but make ongoing decisions based on context.
Use external tools – An agent can be programmed to interact with webpages, databases, APIs, or even physical devices.
Adapt and iterate – Unlike static workflows, agents adjust their approach based on real-time feedback.
Handle complex tasks – Instead of performing one isolated function, they can chain multiple steps together dynamically.
How AI agents differ from traditional LLMs and chatbots
A common misconception is that AI agents are just advanced chatbots or LLM-powered applications like ChatGPT. While LLMs are a core component of AI agents, they are not agents themselves. There are significant differences between LLMs, chatbots, and AI agents:
Feature | Chatbots / LLMs | AI Agents |
|---|---|---|
Decision-making | Passive, responds to input | Actively decides next steps |
Task execution | Provides responses, no direct action | Can trigger actions using tools & APIs |
Adaptability | Follows predefined conversational logic | Adjusts based on feedback & environment |
Autonomy | Requires continuous human input | Works toward objectives independently or semi-independently |
Traditional chatbots, for example, are designed to respond to prompts but don’t independently decide what actions to take next. ChatGPT, while impressive, is not an AI agent in its standard form—it answers questions and assists with tasks, but it doesn’t autonomously execute multi-step processes or use external tools unless integrated into an agentic system.
The Limitations of AI Agents
While AI agents offer greater flexibility and automation, they aren’t a silver bullet or a magic solution to every problem. Agents…
Require clear objectives—without defined goals, they can struggle to make effective decisions.
Must be provided with predefined tools to complete actions — if you want an agent to be able to query a database or search the web, you have to provide the agent with the capabilities to do so. Agents are still programs, so they can’t do everything that a human can right out-of-the-box.
Still lack true human-like reasoning—while agents can handle structured tasks well, they don’t have deep intuition or creativity beyond their training data.
Do best at working towards one goal at a time — while agents can handle reasonably complicated tasks and workflows, they are not good at working on multiple different high-level goals at the same time.
AI agents represent an important evolution in how AI systems operate, moving from passive tools to active problem-solvers. But they aren’t a one-size-fits-all solution—understanding their strengths and limitations is key to using them effectively.
Different Types of AI Agents
AI agents come in different forms, ranging from simple assistants to fully autonomous systems. They generally fall into three categories:
Augmented LLMs - These are LLM-powered assistants with additional capabilities like retrieving information or executing simple tasks. They enhance interactions, but still rely on user input. Examples include automated Q&A systems
Workflow-based agents - These agents leverage AI but follow structured processes and pre-defined workflows. They're great for repetitive tasks and business automation. Research agents are a great example of workflow-based agents.
Fully autonomous agents - The most advanced agents operate independently. They can be given a high-level goal, and will break it down into sub-tasks and make decisions and adjust their approach dynamically to accomplish the goal.
Most businesses can benefit from workflow-based agents, while fully autonomous systems suit complex, dynamic tasks. The right choice depends on balancing control, flexibility, complexity, and cost.
When (and when not) to use agents
AI agents are most effective in scenarios that require adaptability, automation, and decision-making. Here’s when they make sense:
When flexibility is needed – Ideal for tasks that require real-time decision-making rather than rigid, predefined workflows.
For complex, multi-step processes – Useful when an AI needs to plan, use tools, and adapt dynamically.
To automate repetitive tasks – Great for handling high-volume, low-complexity work efficiently.
When external data or tools are required – Necessary if an AI needs to pull live data, interact with APIs, or execute actions beyond simple text generation.
For improving efficiency and reducing costs – Helps eliminate bottlenecks and frees up human time for higher-value work.
When NOT to use AI agents: If a simple rule-based system or chatbot can solve the problem, an agent is likely unnecessary. Tasks that can be handled by traditional automation approaches such as scripting or robotic process automation (RPA) often don’t need to use agents, unless there’s a compelling reason to do so.
The Business Value of AI Agents
AI agents aren’t just a technological novelty—they drive real business impact by improving efficiency, reducing costs, and enabling new capabilities. Here’s how they deliver value:
Increased Productivity – Automate repetitive or time-consuming tasks, allowing employees to focus on higher-value work.
Cost Savings – Reduce expenses tied to manual processes, inefficiencies, and human error.
Enhanced Decision-Making – Process and analyze large volumes of data in real time to support smarter, faster business decisions.
Better Customer Experience – Enable personalized interactions, faster response times, and seamless automation in customer support and sales.
Scalability – Handle increasing workloads without requiring proportional increases in human labor.
Competitive Advantage – Businesses that leverage AI agents effectively can operate more efficiently and innovate faster than their competitors.
When implemented thoughtfully, AI agents don’t just streamline operations—they unlock new opportunities for growth and transformation.
Example use-cases
Let’s look at a few real-world applications of AI agents that business are using right now:
Customer support — in many cases, customer support queries can be automatically resolved by agents that have access to a company’s knowledge base and can answer questions, escalating to human support agents only for more complicated queries. AI can also be used to automatically triage and prioritize support tickets.
Enhancing software development — while AI can’t replace software engineers yet, it can make them more efficient by generating code snippets for common problems, prototyping user interfaces, helping resolve bugs, reviewing code, and providing feedback. Tools like Cursor and Windsurf are becoming incredibly popular with developers.
Handling phone calls — with the advent of conversational AI technology, agents can handle a wide variety of tasks over the phone ranging from inbound lead qualification, customer support, appointment scheduling, and more. Conversational AI agents are a new but incredibly promising technology!
Challenges and Considerations
While AI agents offer significant advantages, they also come with challenges that businesses must navigate:
Data Privacy & Cybersecurity – Agents often integrate with internal & external software systems, and may require access to sensitive information, raising concerns about data protection and compliance.
Accuracy & Reliability – AI models can generate incorrect outputs, requiring oversight and validation mechanisms.
Technical Complexity – Implementing and maintaining AI agents requires specialized expertise in AI integration, tool orchestration, and system monitoring.
Ethical Concerns – Agents making decisions autonomously can introduce fairness, transparency, and accountability challenges.
Businesses should weigh these considerations carefully to ensure agentic solutions are deployed effectively and responsibly.
Conclusion
AI agents are transforming how businesses operate and creating immense value. By understanding what agents are, the value they create and how they can fit into the business landscape, organizations can leverage AI agents to drive efficiency, improve customer experiences, and gain a competitive edge.
However, deploying AI agents isn’t without challenges—privacy, accuracy, and technical complexity must be carefully managed. The key is to align AI capabilities with business needs, ensuring that automation enhances productivity without introducing unnecessary risks.
As AI continues to evolve, agents will play an even bigger role in business and technology. Companies that strategically adopt and integrate AI agents today will be better positioned for the future.
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