What are AI Agents?

Artificial Intelligence (AI) agents are autonomous software entities that can perceive their environment, make decisions, and take actions to achieve specific goals. They represent one of the most exciting developments in AI, combining multiple technologies to create systems that can operate independently and intelligently. These agents extend beyond simple language interactions and are capable of decision-making, problem-solving, interacting with environments, and executing actions independently.

AI agents are used across industries for tasks like software engineering, IT automation, code generation, and conversational support. Powered by large language models (LLMs), these agents interpret user instructions, reason through tasks step by step, and invoke external tools as needed.

Definition

An AI agent is a computer system that can:

• Perceive its environment through sensors or data inputs
• Reason about the information it receives
• Act upon its environment to achieve goals
• Learn from experience to improve performance

Key Characteristics

1. Autonomy

AI agents operate without constant human intervention. They can make decisions and take actions based on their programming and learning.

2. Reactivity

Agents respond to changes in their environment in real-time, adapting their behavior accordingly.

3. Proactivity

Beyond just reacting, agents can take initiative to achieve their goals, planning and executing strategies.

4. Social Ability

Many agents can interact with other agents or humans, collaborating to achieve complex objectives.

Types of AI Agents

How AI Agents Work

At the heart of AI agents are LLMs—models like IBM® Granite®. Unlike standalone LLMs that are limited to their training data, AI agents enhance capabilities through tool usage, memory, and autonomous planning. This architecture allows them to:

Access external data and tools in real time.

Break down complex goals into manageable subtasks.

Store memory for personalized and adaptive interactions.

These functions are executed through three core processes:

1. Goal Initialization and Planning While agents act independently, they rely on goals and parameters defined by three entities:

Developers, who build and train the system.

Deployers, who integrate the agent into workflows.

End users, who provide specific objectives and tools.

Given this input, the agent decomposes tasks and creates a plan. For simple queries, the agent may operate without upfront planning by iteratively refining its responses.

1. Reasoning With Tools AI agents rarely have all the knowledge they need. They bridge this gap by using external resources such as APIs, databases, search engines, or even other AI agents. Through agentic reasoning, they:

Gather missing information.

Update their internal state.

Reassess their plan iteratively for improved decision-making.

Example: To find the best week for a surfing trip in Greece, an agent may:

Pull historical weather data.

Consult a surf-specific agent.

Combine insights to identify ideal dates based on tides, sunlight, and rainfall.

1. Learning and Reflection AI agents learn from outcomes—both from user feedback and from collaborating agents. This iterative refinement allows them to:

Adjust to user preferences.

Avoid repeating past mistakes.

Improve reasoning accuracy.

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Agentic vs. Non-Agentic Chatbots

Non-agentic chatbots respond based solely on input, with no memory, planning, or tool access. They're reactive and require constant user guidance.

Agentic AI chatbots, on the other hand:

• Learn over time.

• Plan and execute subtasks.

• Use tools and memory to generate personalized, multi-step responses.

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Reasoning Paradigms

AI agents can be designed using different architectural frameworks for multistep problem-solving:

ReAct (Reasoning + Acting)

• Think-act-observe loops.

• Tools are used iteratively based on intermediate observations.

• Encourages transparency through “thoughts” and stepwise reasoning.

ReWOO (Reasoning Without Observation)

• Plans tool usage before execution.

• Reduces redundancy and inefficiency.

• Allows users to preview and validate plans.

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Types of AI Agents

Model-Based Agents

These agents maintain an internal model of their environment to make more informed decisions.

Goal-Based Agents

Agents that work toward specific objectives, using planning and search algorithms.

Utility-Based Agents

Agents that maximize a utility function, making decisions that optimize for the best outcome.

Learning Agents

Agents that improve their performance over time through experience and feedback.

Use Cases

• Customer Support: Virtual assistants, interview simulations, mental health check-ins.

• Healthcare: Emergency room triage, treatment planning, medication management.

• Disaster Response: Social media analysis to locate people needing help.

• Finance & Supply Chain: Market trend prediction, logistics optimization.

Benefits of AI Agents

• Task Automation: Execute complex workflows with minimal human input.

• Enhanced Performance: Collaboration between multiple agents boosts outcomes.

• Personalized Responses: Adapt to users through memory and reasoning.

Risks and Limitations

• Multiagent Failures: Shared foundational weaknesses can propagate across agents.

• Infinite Loops: Faulty reasoning may lead to repeated tool calls.

• Computational Cost: Training and running agents is resource-intensive.

• Data Privacy: Improper integration can expose sensitive business or user data.

Best Practices for Deployment

• Activity Logs: Provide transparency by tracking tool usage and decisions.

• Interruptibility: Allow human users to halt agent actions as needed.

• Unique Identifiers: Trace agent developers and users for accountability.

• Human Oversight: Require approval for high-risk actions and monitor behavior during learning.

Challenges and Limitations

Technical Challenges

• Scalability: Managing complex environments and large amounts of data
• Real-time processing: Making decisions quickly enough for practical applications
• Integration: Combining multiple AI technologies effectively

Ethical Considerations

• Bias: Ensuring agents make fair and unbiased decisions
• Transparency: Understanding how agents make decisions
• Safety: Preventing harmful actions or unintended consequences

Future Directions

The field of AI agents is rapidly evolving, with several exciting developments:

• Multi-agent systems: Coordinated groups of agents working together
• Human-agent collaboration: Seamless interaction between humans and AI agents
• General AI agents: Agents that can handle a wide variety of tasks
• Embodied agents: Physical robots with AI capabilities

Conclusion

AI agents represent a significant step toward more intelligent and autonomous computing systems. As the technology continues to advance, we can expect to see agents playing increasingly important roles in our daily lives, from personal assistants to complex industrial systems.

The key to successful AI agent development lies in understanding the balance between autonomy and control, ensuring that agents can operate effectively while remaining safe and beneficial to society.