What Are AI Agents and How Do They Work?
As artificial intelligence transforms industries worldwide, understanding AI agents—their types, functions, and real-world applications—has become essential for leveraging their full potential in business operations, customer experiences, and technological innovation.
Updated May 19, 2025
What Are AI Agents?
AI agents are autonomous computer systems designed to perceive their environment, process information, make decisions, and take actions to achieve specific goals. Unlike traditional software programs that execute predefined commands, AI agents can operate independently, adapt to changing conditions, and improve performance over time without direct human supervision.
These intelligent systems range from simple rule-based applications to sophisticated platforms capable of complex problem-solving, learning from experience, and even collaborating with other AI systems or humans. Whether optimizing supply chains, personalizing customer interactions, or assisting with medical diagnoses, AI agents are fundamentally changing how we approach complex tasks across industries.
Modern AI agents often leverage generative AI capabilities to produce content, solutions, and responses that weren’t explicitly programmed. By utilizing advanced AI models trained on vast datasets, these agents can create original outputs that address specific needs, significantly expanding their utility and effectiveness in real-world applications.
How Do AI Agents Work?
AI agents function through a structured cycle of perception, processing, and action:
1. Perception
The agent collects data from its environment using various inputs, which may include:
- Digital interfaces (APIs, web services, databases)
- Sensors (cameras, microphones, IoT devices)
- User interactions (text, voice, or behavioral data)
- System logs and performance metrics
2. Processing
Once data is collected, the agent analyzes it using:
- Machine learning models (including neural networks)
- Knowledge bases and decision trees
- Rule-based logic systems
- Planning algorithms for determining optimal actions
3. Action
Based on its analysis, the agent executes actions through:
- Digital outputs (generating content, performing calculations)
- System commands (database updates, API calls)
- Control of physical systems (in robotics or automation)
- Communication with users or other systems
Advanced AI agents often use multiple types of reasoning and processing in combination. For example, self-driving vehicles simultaneously employ various agent types to handle navigation, obstacle detection, and passenger safety—creating a complex system greater than the sum of its parts.
What Does an AI Agent Do?
AI agents perform a wide range of functions across different domains:
Automation and Task Execution
- Schedule management and calendar optimization
- Document processing and information extraction
- Workflow automation and process orchestration
- Quality control and anomaly detection
Decision Support
- Data analysis and insight generation
- Risk assessment and fraud detection
- Resource allocation and optimization
- Predictive maintenance and failure prevention
Interaction and Communication
- Customer service and query resolution
- Personalized recommendations and guidance
- Language translation and content generation
- Virtual assistance and task management
Learning and Adaptation
- Performance improvement through experience
- Pattern recognition and trend identification
- Behavioral modeling and preference learning
- Continuous skill acquisition and refinement
The 5 Types of AI Agents
AI agents are categorized based on their capabilities, decision-making processes, and level of autonomy:
| Type | Key Characteristics | Real-World Example |
| Simple Reflex Agents | • React directly to current stimuli using predefined rules • No memory of past experiences or actions • Function through condition-action rules (if-then statements) | Basic thermostats that turn heating on or off based on current temperature readings without considering historical patterns or optimizing for efficiency |
| Model-Based Reflex Agents | • Maintain an internal model of the environment • Track how the world evolves independently of the agent • Consider how actions affect the environment | Robot vacuum cleaners that create maps of rooms, detect and navigate around furniture, and remember which areas they’ve already cleaned to avoid redundant work |
| Goal-Based Agents | • Evaluate potential actions based on goal achievement • Consider multiple possible solutions • Plan sequences of actions to reach objectives | Navigation systems in autonomous vehicles that calculate optimal routes to destinations while accounting for traffic, road conditions, and safety parameters |
| Utility-Based Agents | • Assess actions based on maximizing a utility function • Balance competing objectives (speed, cost, quality) • Optimize for best possible outcomes rather than binary goals | Tesla’s Autopilot system, which chooses the safest and most efficient driving maneuvers by considering multiple factors like road conditions, vehicle speed, and energy consumption |
| Learning Agents | • Improve performance through experience • Adapt behavior based on feedback • Modify internal models and decision criteria over time | Customer service chatbots trained through reinforcement learning that initially handle basic queries but progressively manage more complex questions as they learn from interactions and feedback |
How Many AI Agents Are There?
The number of AI agents in deployment continues to grow exponentially as organizations discover new applications and implementation strategies. According to a joint survey by IBM and Morning Consult involving 1,000 developers building AI applications for enterprise, 99% reported they are exploring or actively developing AI agents, confirming 2025 as “the year of the agent.”
Rather than counting specific implementations, it’s more useful to understand the breadth of AI agent adoption across sectors:
- Consumer applications: Billions of virtual assistants embedded in smartphones, smart speakers, and home automation systems
- Enterprise deployments: Hundreds of thousands of specialized agents handling functions from customer service to cybersecurity
- Industrial systems: Tens of thousands of agents monitoring equipment, optimizing production, and managing logistics
- Research applications: Thousands of experimental agents exploring new capabilities and domains
Is ChatGPT an AI Agent?
No, ChatGPT is not a true AI agent. While it shares some characteristics with AI agents, it lacks the critical components that define agency in artificial intelligence systems.
ChatGPT represents a powerful language model designed to engage in conversational interactions, but it fundamentally operates as a reactive system rather than a proactive agent. As the Institute of AI Studies explains, ChatGPT is “not a true AI agent” but instead functions as “a large language model that acts as a co-pilot or assistant” that “lacks the ability to work independently or take actions on its own” unlike true AI agents that can perform tasks without constant human input.
The critical distinctions that prevent ChatGPT from qualifying as a true AI agent include:
Lacks autonomy: ChatGPT requires explicit human prompts to function. It cannot independently initiate actions, set its own goals, or operate without continuous human direction. True AI agents can work proactively toward objectives with minimal human intervention.
Cannot interact with external systems: Despite recent advances, standard ChatGPT lacks the ability to directly manipulate other software or access the internet independently. While it can generate outputs based on its training data, it cannot “directly interact with other software or the internet” without specific integrations.
Limited persistence: ChatGPT doesn’t maintain long-term goals or independently manage its own operation. Each interaction is largely isolated, with only the immediate conversation history providing context.
Restricted tool use: While newer implementations like GPT-4 with plugins offer limited tool-calling capabilities, ChatGPT itself cannot orchestrate complex workflows involving multiple tools and services the way true agents can.
OpenAI recognizes these limitations and is actively working toward more agent-like capabilities. In January 2025, OpenAI introduced “Operator” to ChatGPT Pro users—described as “an AI agent that interprets orders and goes to a web browser to perform these tasks for the user” that might automate several daily chores. This evolution suggests that while ChatGPT itself is not an agent, it’s serving as a foundation for the development of true AI agents that can operate with greater autonomy.
The industry’s shift toward more capable AI agents represents a significant evolution beyond the current capabilities of language models like ChatGPT. As IBM notes, “What’s commonly referred to as ‘agents’ in the market is the addition of rudimentary planning and tool-calling (sometimes called function calling) capabilities to LLMs” which enable language models to break down complex tasks into smaller steps. True agency remains an aspiration rather than a current reality for systems like ChatGPT.
What Are Examples of AI Agents in Real Life?
AI agents are already transforming numerous industries through practical applications:
Financial Services
Dynamic pricing algorithms for services like Uber adjust costs in real-time based on demand, competition, and environmental factors—explaining why your ride costs more during rush hour or bad weather.
Insurance claim processing has been revolutionized by AI agents, with a large Dutch insurer automating approximately 90% of individual automobile claims through an integrated AI workflow that allows human adjusters to focus exclusively on complex cases requiring judgment.
Healthcare
AI agents assist with treatment planning in emergency departments and manage pharmaceutical processes, saving medical professionals valuable time for more critical patient interactions.
Google’s diagnostic AI agent achieved 85.4% sensitivity in identifying skin cancer, exceeding the accuracy rates of human dermatologists in controlled studies.
Manufacturing and Supply Chain
AI-powered production systems optimize manufacturing processes by coordinating different components, increasing production by up to 25% without sacrificing quality and reducing operational costs by as much as 40% through improved efficiency.
In factories, robotic AI agents handle tasks from quality control to assembly operations, optimizing production times while maintaining consistent quality standards across manufacturing lines.
Customer Service
Advanced customer service systems like Intercom’s AI bots provide real-time, personalized interactions that have demonstrated 50% faster response times and 40% reductions in support costs, while Ada’s automation platform has enabled companies to handle 80% of customer inquiries without human intervention.
Transportation
Mercedes-Benz has implemented conversational search and navigation in their new CLA series vehicles using Google Cloud’s Automotive AI Agent, while GM’s OnStar service has been enhanced with virtual assistants that better recognize driver intent and provide more natural interactions.
Public Sector
The State of Nevada has deployed AI agents to accelerate unemployment claim appeals processing, and mRelief developed an SMS-accessible AI chatbot that simplifies applications for the SNAP food assistance program, providing eligibility information and assistance within minutes rather than days.
How Do AI Agents Differ from Traditional Software Programs?
AI agents represent a significant evolution beyond conventional software in several key aspects:
| Aspect | Traditional Software | AI Agents |
| Autonomy | Executes predefined commands and requires explicit instructions for each task | Independently determines actions needed to achieve goals without step-by-step human guidance |
| Adaptability | Maintains fixed behavior unless manually updated or reconfigured | Adjusts strategies based on changing conditions and new information |
| Learning Capacity | Performance remains constant regardless of experience | Improves functionality over time through data analysis and feedback |
| Environmental Awareness | Operates within narrowly defined parameters with limited context | Perceives and responds to complex environmental conditions and variables |
| Goal Orientation | Focused on completing specific tasks as instructed | Prioritizes achieving objectives through flexible means and approaches |
How Can AI Agents Be Used to Improve Business Processes?
Organizations are leveraging AI agents to transform operations across various functions:
Customer Engagement
Companies implementing AI-driven customer service solutions report significant benefits, including 24/7 availability, immediate response times, and increased customer satisfaction. These systems intelligently route complex issues to human agents while autonomously handling routine inquiries, with 72% of customers reporting greater loyalty to businesses that provide rapid service.
Marketing and Sales
AI agents are revolutionizing sales outreach by autonomously building custom communications, tracking responses, and following up with leads based on engagement levels. When integrated with CRM systems, these agents maintain updated pipelines and ensure timely prospect engagement without manual intervention.
Financial Operations
In financial services, AI agents analyze transaction details exponentially faster than human operators, checking up to 5,000 data points in milliseconds compared to the 20-30 points typically reviewed manually, dramatically improving fraud detection while reducing false positives.
Supply Chain Management
AI agents optimize logistics, inventory management, stocking, and deliveries by coordinating multiple systems to improve efficiency. This integration allows businesses to reduce costs, minimize waste, and improve delivery times through continuous optimization.
How Do AI Agents Learn and Improve Over Time?
AI agents employ several mechanisms to enhance performance through experience:
Supervised Learning
Agents improve by comparing their outputs against labeled examples, gradually refining their models to produce more accurate results. This approach works well for classification tasks like sentiment analysis or image recognition.
Reinforcement Learning
Similar to how streaming platforms like Netflix and Amazon refine their recommendations, AI agents using reinforcement learning adjust their behavior based on positive or negative feedback. Even if your profile suggests you enjoy horror and thriller movies, the system will adapt if you suddenly switch to romantic comedies.
Transfer Learning
Agents apply knowledge gained in one domain to improve performance in related areas, allowing them to leverage existing expertise when facing new but similar challenges.
Federated Learning
Multiple agents share insights while preserving data privacy, enabling collective improvement without centralizing sensitive information—particularly valuable in healthcare and financial applications.
Why Learn to Build AI Agents?
Developing AI agent capabilities offers significant advantages for organizations and professionals:
Competitive Advantage
As AI agents become central to business operations, organizations with advanced agent implementations gain efficiency and responsiveness advantages over competitors.
Innovation Opportunities
2025 is emerging as “the year of the agent” with significant experimentation happening across industries. Every major technology company and hundreds of startups are exploring agent implementations, creating a wave of innovation that offers first-mover advantages.
Workforce Enhancement
Rather than replacing human workers, well-designed AI agents augment human capabilities by handling routine tasks, providing decision support, and enabling focus on higher-value activities.
Scalability Solutions
AI agents offer critical advantages in scalability, operating continuously without human schedule limitations. This enables 24/7 handling of tasks like customer support, system monitoring, and order processing in real-time, reducing downtime and ensuring consistent service delivery across global time zones.
What AI Tools Are People Using for Their Work?
Professionals across industries are adopting various AI agent tools to enhance productivity:
Development Frameworks
LangChain has emerged as a popular library for Python and JavaScript/TypeScript development, enabling the creation of sophisticated AI agents through streamlined workflows and integration capabilities.
Google’s ADK-Python represents an open-source, code-first toolkit for building, evaluating, and deploying sophisticated AI agents with enhanced flexibility and control over the development process.
Enterprise Platforms
Companies are implementing comprehensive AI agent platforms to support various business functions:
ChemCrow serves as a specialized LLM chemical research agent employed in applications ranging from organic synthesis to drug discovery and materials design, demonstrating how domain-specific agents can accelerate scientific research.
Google’s industry-specific automotive AI agents are being integrated into vehicles from manufacturers like Mercedes-Benz and General Motors, providing conversational interfaces for navigation, vehicle control, and information access.
AI Model Integration Frameworks
Organizations increasingly rely on tools that facilitate the integration of multiple AI models into cohesive agent-based systems. These frameworks manage the orchestration of different models—from large language models to specialized classification systems—ensuring they work together effectively within agent architectures.
Anaconda’s platform plays a crucial role here, providing the infrastructure needed to securely deploy a variety of AI models within agent implementations while maintaining governance and performance standards across enterprise environments.
Conclusion: The Future of AI Agents with Anaconda
As AI agent technology continues to evolve, we can expect increasing sophistication, integration, and autonomy across applications. While media coverage often highlights sensational promises about AI agents, the reality for 2025 involves significant experimentation as organizations explore practical implementations that balance autonomy with appropriate human oversight.
How Anaconda Powers AI Agent Development
Anaconda stands at the forefront of this transformation, providing a unified AI platform designed specifically for the complex requirements of AI agent development:
The Anaconda AI Platform delivers a comprehensive ecosystem that uniquely combines trusted distribution, simplified workflows, real-time insights, and governance controls—essential elements for any organization building or deploying AI agents. This unified approach allows data scientists and developers to focus on innovation rather than infrastructure management.
Expanded Distribution ensures access to the broadest range of secure, enterprise-grade packages and libraries required for AI agent construction. With over 47 million users worldwide, Anaconda’s package ecosystem provides the critical Python packages and AI artifacts developers need to build sophisticated agents quickly.
Secure AI Development addresses one of the primary concerns when implementing autonomous systems. Anaconda enables organizations to run models securely and privately on their own systems with AI Navigator, providing robust coding assistants available in the cloud, on the desktop, or in enterprise environments.
Actionable Insights through Anaconda Gateway delivers automated usage and user metrics for enterprise compliance, presenting critical information in integrated in-product dashboards that help teams understand how their AI agents are performing and being utilized.
Implementing AI Agents with Anaconda
Organizations partnering with Anaconda for AI agent implementation benefit from:
Comprehensive Support Structure that includes installation assistance, implementation guidance, onboarding services, and dedicated technical account management to ensure successful AI agent deployment.
Risk Reduction through Anaconda’s Package Security Manager, which provides CVE scanning and compliance tracking to protect AI agent development from vulnerabilities in the open-source ecosystem.
Development Acceleration with conda package manager for automated dependency resolution across Python and other common AI and open-source code, eliminating manual processes that slow agent development.
Unified Experience that simplifies and streamlines the AI user experience through centralized accessibility to products, services, and community resources.
The trajectory of AI agent development points toward systems that are increasingly personalized, collaborative, and context-aware—transforming not just how we interact with technology, but how technology interacts with the world on our behalf. With Anaconda’s comprehensive platform and tools, organizations can confidently navigate this transition while maintaining security, governance, and performance optimization across their AI initiatives.
Ready to accelerate your AI agent development journey? Request a demo or get started for free with Anaconda’s comprehensive tools for building secure, scalable AI applications.