AI agents have come out of the woodwork fast and furious. Most of the AI hype this year is focusing on them. But what are they? Why should you care?
Will they transform the way we build and use software? How can they fit into how you do business? Let’s get some answers.
What are AI Agents?
AI agents are autonomous systems that observe their environment, make decisions, and take actions to achieve specific goals. And they do so without explicit human instructions.
The agent’s behavior is driven by a cognitive architecture composed of three key components:
- The Model (the agent’s brain): A large language model (or multiple models) acts as the central decision-maker. They follow instruction-based reasoning frameworks (like ReAct or Chain-of-Thought) and can be general-purpose, multimodal, or fine-tuned for specific tasks.
- The Orchestration Layer: This component governs the agent’s operational cycle. It receives information, processes it through internal reasoning, and then determines the next action.
- The Tools: While language models generate text and images, they lack the ability to interact with the external world. Tools fill this gap by enabling agents to perform actions—such as fetching data or updating databases—via standard web API methods.
Some AI agents operate reactively, responding to inputs as they come. Others take a more proactive approach, anticipating future needs and adapting to new information.
Depending on their design, AI agents can work independently or as part of a multi-agent system, collaborating to solve complex problems.
It’s early days for AI agents but we’re only going to see the application of them grow. One of the first examples in action is OpenAI’s Operator. This agent browses the internet for you and can complete tasks like making reservations, book tickets, shop, or file an expense report.
Key Differences: AI Agents vs Traditional Software
Traditional software follows strict, pre-programmed rules—if X happens, do Y. That makes it reliable for repetitive tasks but struggles with anything unexpected.
AI agents, on the other hand, make decisions dynamically. They analyze their environment, adapt to new inputs, and improve over time.
For example, a rule-based chatbot can only respond to specific keywords, while an AI-powered chatbot understands intent and refines its answers with experience.
Similarly, a traditional navigation app follows preset routes, but an AI agent reroutes in real time based on live traffic. This adaptability makes AI agents far more effective in complex, unpredictable scenarios.
| Feature | Traditional Software | AI Agents |
|---|---|---|
|
Decision-Making
|
Follows fixed, rule-based logic
|
Adapts dynamically to new inputs
|
|
Flexibility
|
Limited to predefined scenarios
|
Learns and evolves over time
|
|
Handling Uncertainty
|
Struggles with unexpected data
|
Adjusts to new information in real time
|
|
Example: Chatbots
|
Answers only predefined questions
|
Understands intent and improves with interactions
|
|
Example: Navigation
|
Uses static routes
|
Reroutes based on live traffic conditions
|
How do AI Agents Work?
AI agents operate in a continuous cycle: they gather information, analyze it, make decisions, and take action.
These systems range from simple task-specific bots to advanced, adaptive agents that can reason, plan, and refine their responses based on experience.
Let’s look at the three tasks they run through:
1. Perception: Gathering Input
AI agents first collect data from their environment. This could come from sensors, user interactions, or real-time data streams. A customer service AI, for example, processes text or voice inputs, while an autonomous drone uses cameras and GPS.
Recent developments, such as Anthropic’s Model Context Protocol, aim to standardize the connection between agents and the systems where data is stored—ranging from content repositories and business tools to development environments. This protocol is designed to help ensure that AI agents receive the relevant, up-to-date data needed for accurate perception and analysis.
2. Decision-Making: Processing and Reasoning
After gathering data, modern AI agents employ advanced reasoning techniques that mirror aspects of human thought. Emerging approaches include:
- Mixture of Experts (MoE): A model architecture that uses multiple specialized sub-models (experts) and a gating mechanism to dynamically select the most relevant expert for each input, enhancing performance and efficiency.
- Chain-of-Thought: A method where the model generates intermediate reasoning steps before arriving at a final answer, making its thought process explicit and improving accuracy on complex tasks.
- Tree of Thoughts: An approach that explores multiple reasoning paths simultaneously, evaluating different sequences of thought like branches in a tree to choose the most promising solution.
- ReAct: A framework that combines reasoning and action by interleaving thought processes with executable actions, allowing the agent to both explain its reasoning and act on it.
As noted in a recent Google whitepaper on orchestration, these innovations are reshaping how agents process information, allowing for more dynamic and adaptable decision-making. This evolution in reasoning unlocks use cases that were once exclusively reserved for human cognition.
3. Action: Executing Decisions
After processing the input, the agent takes action based on its objectives. This could mean responding to a user query, adjusting a supply chain route, or alerting a security system to a potential threat.
Effective execution also depends on having access to external tools and resources. By connecting with these tools—most commonly via APIs—and integrating with SaaS products such as Slack, email, calendar, automation platforms, productivity and task management tools, CRMs, and data analytics services, AI agents can perform actions with enhanced precision and adaptability, ensuring they anticipate and optimize outcomes in dynamic environments.
Can AI Agents become co-workers in our day-to-day business operations?
Check out the Talking AI podcast to find out.
Types of AI Agents
AI agents come in different forms, depending on how they process information and make decisions. Some react instantly to inputs, while others plan, learn, or collaborate with other agents to achieve complex goals.
Simple Reflex Agents
These agents make decisions based solely on the current input, ignoring past experiences or broader context. They follow a set of predefined if-then rules.
How They Work:
- Sense the environment.
- Apply a fixed rule to determine an action.
Use Cases:
- Rule-based chatbots that provide scripted responses.
- AI opponents in video games that react to player movements.
Model Based Reflex Agents
Unlike simple reflex agents, these model based agents use an internal model of the environment to track changes and make better decisions.
How They Work:
- Sense the current state of the environment—using sensors or input devices—to understand the current situation.
- Use stored knowledge to interpret changes.
- Select an action based on predefined models.
Use Cases:
- Smart thermostats that adjust temperature based on historical patterns.
- Robotic navigation systems that update their paths based on obstacles.
Goal-Based Agents
These agents don’t just react—they make decisions based on achieving a specific goal. Instead of following rigid rules, they evaluate how an agent’s actions affect the outcome to determine which will bring them closer to success.
How They Work:
- Assess the current state and define a goal.
- Plan a sequence of actions to reach that goal.
- Adjust the plan as conditions change.
Use Cases:
- Pathfinding algorithms that help delivery drones find optimal routes.
- AI planning tools for logistics and scheduling.
Utility-Based Agents
These agents take goal-based reasoning a step further by assigning values (utilities) to different outcomes, allowing them to choose the most beneficial action.
How They Work:
- Compare different possible actions.
- Choose the action that maximizes benefit.
Use Cases:
- Self-driving cars optimize routes based on speed, fuel efficiency, and traffic.
- Dynamic pricing systems that adjust prices based on demand and competitor behavior.
Learning Agents
Unlike the previous types, learning agents improve over time. They analyze past experiences, update their decision-making processes, and refine their models to perform better in the future.
How They Work:
- Observe the results of their actions.
- Use feedback loops to adjust future decisions.
- Continuously update their knowledge base.
Use Cases:
- AI assistants that personalize responses based on user behavior.
- Fraud detection systems that adapt to new fraudulent patterns.
Hierarchical Agents
These agents operate in layers, with high-level agents overseeing the behavior of lower-level agents. This structure helps break down complex problems into manageable tasks.
How They Work:
- High-level agents define broad strategies.
- Lower-level agents handle specific tasks.
Use Cases:
- Robotics systems where a central AI coordinates multiple sub-processes.
- Large-scale automation systems managing different levels of operations.
Multi-Agent Systems (MAS)
Instead of a single AI, multi-agent systems involve multiple AI agents working together, each with its own role and capabilities. These agents may collaborate, compete, or negotiate to achieve a shared objective.
How They Work:
- Agents communicate and coordinate their actions.
- They distribute tasks among themselves for efficiency.
Use Cases:
- Stock market trading bots that adjust strategies based on others’ actions.
- Swarm robotics, where multiple small robots collaborate on a task.
Master-Subordinate Models in Multi-Agent Systems
Building on the concept of hierarchical agents, many multi-agent systems adopt a master-subordinate model to further streamline operations. In this structure, a master agent plays the role of coordinator or manager, delegating tasks and making high-level decisions, while subordinate agents carry out specific functions under its direction.
Key Roles of Master Agents:
- Task Allocation: They assess the capabilities of subordinate agents and assign tasks accordingly.
- Decision Making: By aggregating data from various sources, master agents form overarching strategies.
- Coordination & Synchronization: They ensure that all agents move in unison toward a shared objective.
- Monitoring & Feedback: Ongoing oversight allows them to optimize performance and adjust directives in real time.
This model is particularly beneficial in environments such as AI-driven workflow automation, distributed computing, and robotics swarms where coordinated action is crucial.
Subordinate Agents in Action:
- Task Execution: They perform the specific operations or computations assigned by the master.
- Reporting Progress: By providing regular updates, they help the master agent refine decisions.
- Specialization: Often, these agents are optimized for particular functions—whether it’s data analysis, physical operations, or customer interaction.
- Adaptability: They are designed to respond to evolving instructions, ensuring flexibility in dynamic settings.
Together, the master and subordinate agents create a robust framework that enhances coordination and efficiency, making this model a cornerstone for complex applications such as autonomous systems, cybersecurity, and intelligent automation.
Want to learn more about Multi-agent, real-time systems?
Check out our Talking AI podcast episode where we talk to Praveen Gunasekaran, Senior Director and Chief Architect at Visa to discuss their impact on software development.
Where to Find AI Agents
If you want to explore agent types and experiment with their usefulness, you have a range of options:
agent.ai: agent.ai is a marketplace that aggregates various AI agent listings from multiple providers. The platform lets you review agent descriptions, compare functionalities, and read user feedback, making it easier to identify the solution that best fits your needs.
Crew AI: There’s also Crew AI, a platform focused on providing tools and frameworks for building and managing AI agents. This resource is especially useful if you’re looking to integrate or develop customized agent solutions.
Open Source Initiatives: Building on that, Reuven Cohen has developed an open source AI Agent framework with Crew AI, offering a customizable foundation for those interested in building and testing their own agents. More details can be found in his LinkedIn post.
LangChain: For developers seeking a comprehensive toolkit, LangChain provides extensive documentation and tutorials on building AI agents. Their resources guide users through the process of creating and customizing agents, making it a valuable reference for those interested in hands-on development.
Browser Use: This GitHub repository offers an open source framework for developing browser-based AI agents. It provides tools and examples that help developers experiment with integrating AI functionalities directly into web applications.
Benefits of AI Agents
AI agents transform the way businesses operate by improving efficiency, customer experiences, and decision-making.
Here’s a deeper look at how these intelligent agents deliver tangible value across different areas.
Handling More, Faster
Traditional customer service teams can only manage a limited number of inquiries at a time. AI agents, on the other hand, can handle thousands of interactions simultaneously, reducing response times and improving resolution rates.
AI agents can also improve logistics, optimize workflows, and assist in decision-making across industries.
For example, an AI agent in a supply chain system can monitor real-time inventory, predict stock shortages, and reorder products automatically, eliminating the need for manual intervention. In cybersecurity, AI agents detect and respond to threats faster than human analysts, minimizing risks before they escalate.
Improved Customer Satisfaction
Customer frustration often stems from slow responses, repetitive inquiries, and inconsistent support. AI agents tackle these challenges by providing instant, context-aware responses with high accuracy.
Imagine a customer contacting an airline about a delayed flight.
Instead of waiting on hold, an AI agent can instantly check flight status, offer rebooking options, and even suggest compensation based on company policies.
24/7 Availability
AI agents don’t need breaks, sleep, or time off. They provide round-the-clock support, ensuring businesses remain responsive even outside regular working hours. This is especially valuable for global businesses serving customers in different time zones.
For example, an AI-powered IT helpdesk can troubleshoot technical issues at any hour, helping employees stay productive without waiting for human support.
In e-commerce, AI agents can process orders, answer product queries, and handle returns automatically, ensuring seamless service 24/7.
In fact, in some AI circles, the conversations around AI Agents is less about how AI works as an employee’s tool and more about how they operate as an employee themself. It’s called an agentic organization.
We have a whole podcast episode on it. Check it out → Agentic Organizations: The New Era
Scaling Without Bottlenecks
One of the biggest challenges businesses face is maintaining service quality as demand increases. Hiring and training human employees is time-consuming and costly, but AI agents scale effortlessly to meet rising demand.
During peak shopping seasons, for example, customer service teams are often overwhelmed with inquiries.
AI agents can absorb the surge by answering FAQs, processing orders, and even assisting with troubleshooting—ensuring businesses can handle high volumes without slowing down or sacrificing service quality.
In healthcare, AI agents help scale telemedicine services by handling patient intake, scheduling appointments, and providing preliminary diagnoses.
Data-Driven Insights: Turning Information Into Action
AI agents help organizations identify patterns, predict needs, and optimize strategies.
For example, an AI agent monitoring customer support tickets might detect a recurring product defect, prompting proactive improvements. In finance, AI-powered advisors can analyze market trends and provide real-time investment recommendations. In HR, AI agents can assess employee sentiment through workplace chat analysis, helping companies address concerns before they lead to dissatisfaction.
Implementing AI Agents
If you want to gain the benefits of using AI agents, you’ll need to implement them the right way. Here are the steps we recommend as an AI company:
1. Define Clear Goals and Objectives
Before building an AI agent, it’s crucial to outline exactly what it should do and what success looks like. Will it handle customer inquiries, automate logistics, or analyze data? Defining key tasks, expected outcomes, and performance metrics ensures that the AI agent is aligned with business needs.
For example, a customer service AI agent might have goals like reducing response times, handling common questions, and escalating complex issues to human agents. A fraud detection AI agent could focus on identifying suspicious transactions with high accuracy while minimizing false positives.
2. Choose the Right Technology
AI agents rely on various technologies, from machine learning models to natural language processing (NLP) and computer vision. The right tools depend on the agent’s purpose.
- Rule-based AI agents work well for structured tasks with predefined decision paths (e.g., simple chatbots).
- Machine learning-powered agents improve over time by learning from data, making them ideal for tasks like fraud detection or predictive analytics.
- NLP-driven agents enable AI to understand and respond to human language, crucial for virtual assistants and customer support bots.
Beyond the AI models themselves, choosing the right platform for deployment is equally important. Cloud-based solutions (like AWS, Azure, or Databricks) offer scalability and easy integration with existing business systems.
3. Train and Test the AI Agent
Once the AI agent is designed, it needs to be trained using relevant data. This step ensures that the agent can accurately process inputs and make correct decisions.
Supervised learning: The agent is trained on labeled examples to recognize patterns and behaviors.
Reinforcement learning: The agent learns by trial and error, improving performance over time.
Testing is equally important. Running simulations, stress tests, and real-world trials helps identify weaknesses and refine responses before full deployment. A customer service AI agent, for instance, should be tested against different phrasing styles, typos, and unexpected inputs to ensure it responds appropriately.
4. Monitor and Evaluate Performance
AI agents need ongoing oversight to remain effective. Performance metrics should be tracked to ensure the agent is delivering on its objectives.
Key areas to monitor include:
- Accuracy: Is the AI agent making the right decisions?
- Response time: Is it handling tasks faster than human alternatives?
- User experience: Are customers or employees satisfied with its performance?
Regular updates, retraining with new data, and feedback loops help refine the AI agent’s capabilities over time. If an e-commerce AI chatbot starts misinterpreting customer questions, developers can fine-tune its NLP model based on real interactions.
Applications of Agents in AI
Throughout this piece we’ve hinted at where AI agents can be useful and how. In this section, we’re really going to hammer that point home and explore the possibilities so that you can find how AI agents will fit into your business and industry.
AI Agents in Customer Service
Customer interactions are one of the most widespread applications of AI agents. These systems handle inquiries, provide instant responses, and personalize support experiences.
Chatbots and Virtual Assistants: AI-driven chatbots manage high volumes of customer requests, offering instant answers without human intervention. More advanced AI agents can escalate complex issues to human agents, ensuring seamless support.
Salesforce uses AI-powered virtual assistants to automate responses, recommend solutions, and even detect customer sentiment, improving engagement and resolution times.
By implementing AI agents in customer service, businesses can reduce wait times, improve response accuracy, and scale their support operations effortlessly.
AI Agents in Autonomous Systems
AI agents aren’t just powering conversations—they’re driving vehicles, flying drones, and operating robots.
Self-Driving Cars: AI agents process sensor data in real time to make driving decisions, navigate roads, and adapt to unexpected obstacles. Tesla’s Autopilot and Waymo’s autonomous taxis rely on AI agents for safe, real-time navigation.
Drones and Robotics: AI-driven drones can conduct search-and-rescue missions, monitor agricultural fields, or manage warehouse inventory without human pilots. Robots equipped with AI agents assist in everything from assembly lines to space exploration.
These AI-powered systems represent the next frontier of automation, reducing human workload while increasing precision and efficiency.
AI Agents in Business Optimization
AI agents aren’t just reacting to customer inquiries or driving vehicles—they’re making businesses smarter by automating workflows and assisting decision-making.
Workflow Automation: AI agents improve internal operations by managing routine tasks, from scheduling meetings to processing invoices.
Decision-Making Assistants: AI agents help executives by analyzing vast datasets and providing actionable insights, reducing decision fatigue and improving business outcomes.
AI agents in HR software can screen applications, predict candidate success, and reduce hiring bias by focusing on skills and qualifications.
For businesses, AI agents provide a strategic advantage by eliminating inefficiencies, boosting productivity, and ensuring decisions are backed by real-time insights.
AI Agents in Healthcare
AI agents are revolutionizing healthcare by enhancing diagnostics, optimizing patient care, and assisting medical professionals.
AI for Diagnosis: AI agents analyze medical images, detect anomalies, and provide early warnings for diseases like cancer. IBM’s Watson Health once leveraged AI to assist in diagnosing complex conditions.
Patient Management: AI-driven virtual nurses provide round-the-clock monitoring, reminding patients to take medications, tracking symptoms, and alerting doctors to any concerning changes.
Predictive Healthcare: AI agents analyze patient data to forecast potential health issues, allowing doctors to intervene before conditions worsen.
By leveraging AI agents, healthcare providers can deliver faster, more accurate, and more personalized care, ultimately improving patient outcomes and operational efficiency.
The Future of AI Agents
What’s next for AI agents? As technology evolves, these systems will become more intelligent, more interconnected, and more autonomous. Here’s what we see on the horizon:
Adoption is Only Going to Increase
AI agents are already transforming industries, but we’re still in the early stages of adoption. Businesses are rapidly integrating AI-driven automation into workflows, from customer service to logistics and cybersecurity.
It’s an exciting time to be building with AI.
As AI models become more accessible and affordable, expect to see AI agents handling more critical tasks and decision-making across sectors.
The increasing availability of AI agents will only make this come to fruition faster. OpenAI has released Operator, an AI agent that can autonomously complete tasks across multiple applications, handling complex workflows with minimal human input.
Instead of just responding to commands like a chatbot, Operator can interact with different tools, retrieve and process information, and take actions on behalf of users.
As more AI agents like Operator become commercially available, businesses will have access to ready-to-deploy solutions that reduce manual workload and accelerate decision-making.
In fact, there are already others you can turn to (and which are more affordable and are open-source), like Browser Use. There’s also Vertex AI, who has an agent builder, and Glean, who has launched agents directly in their platform.
There Will Be Greater Integrations
AI agents won’t work alone. They’ll become part of larger, interconnected systems, working alongside technologies like IoT, blockchain, and edge computing to create faster, smarter, and more responsive environments.
- Manufacturing and Smart Factories: AI agents will track production lines, predict maintenance needs before breakdowns occur, and adjust workflows in real time to improve efficiency.
- Supply Chain Optimization: AI-powered logistics will manage shipments dynamically, responding to real-time demand shifts, weather conditions, and supplier availability to reduce delays and costs.
- AI in Finance and Security: Decentralized finance (DeFi) systems will use AI agents to automate transactions, detect fraud, and enforce smart contract agreements without human intervention.
Multi-Agent Systems Will Self-Govern
AI agents are already working together in multi-agent systems (MAS), but the future will see them become more autonomous and self-governing.
Instead of relying on human intervention, groups of AI agents will collaborate, negotiate, and make decisions as independent entities.
Imagine self-driving car fleets coordinating traffic flows without human oversight or AI-powered financial markets where agents trade, hedge risks, and rebalance portfolios on their own.
These systems will redefine automation, making AI-driven networks more adaptable and resilient than ever before.
Figure Out How AI Agents Can Work For Your Business
Whether you’re looking to improve customer interactions, optimize operations, or build autonomous systems, the key is finding the right AI solution for your business.
At HatchWorks AI, we help you identify and build that solution so that you can turn AI into ROI.
Visit HatchWorks AI to explore AI solutions designed to move your business forward.
