Understanding AI Agents next generation of intelligent systems.

What Are AI Agents?

An AI agent is a program or system capable of autonomously performing tasks on behalf of a user, other systems, or organizations. Unlike traditional software, which typically follows preset rules, AI agents can reason, adapt, and take independent actions toward achieving specified goals.

Key characteristics of AI agents:

• Autonomy: Ability to make certain decisions without continuous human intervention.
• Goal-driven: Operate to fulfill objectives set by developers or users.
• Tool integration: Can access external data, APIs, and even other agents to expand their capabilities.
• Learning and adaptation: Can update their knowledge base and improve performance based on outcomes and feedback.

How Do AI Agents Work?

Traditional large language models (LLMs) are confined to knowledge within their training data. In contrast, agentic AI systems take a step further by integrating tool calling on the backend. This allows them to:

• Obtain up-to-date or real-time information,
• Optimize workflows,
• Create and execute subtasks autonomously,
• Adapt to complex, multi-step real-world objectives.

The operation of AI agents broadly unfolds across three major stages:

1. Goal Initialization and Planning

Despite their autonomy, AI agents need human-defined objectives and rules. The pathway for building and executing these goals involves:

• Developers: Setting up the foundations, behavior patterns, and allowed tool landscape.
• Deployment teams: Ensuring the right environment and access is provided for users.
• End-users: Specifying the goals and constraints for a given situation.

An agent will decompose a complex user request into plans consisting of many tasks and subtasks. For simple instructions, agents may simply react and iterate, bypassing extensive upfront planning.

2. Reasoning with Available Tools

Most agents cannot know everything required for every subtask. To close these gaps, they access tools such as:

• External APIs,
• Web search,
• Data repositories,
• Specialized external agents (e.g., for domain expertise).

By gathering real-time information, agents continuously refine their action plans and can dynamically self-correct, leading to better, more personalized responses.

3. Execution, Memory, and Adaptation

Once information is acquired and plans are set, an agent independently:

• Executes actions,
• Stores results and interactions in memory,
• Adapts future decisions based on what it learns—enabling long-term personalization and robust performance.

Practical Example:

Planning a Surfing Trip with AI AgentsImagine you want to schedule a surfing trip to Greece at a time with the most favorable weather. You instruct your AI agent to find the best week next year for surfing. Here’s how an agent can tackle this:

1. Goal Decomposition: The agent understands your goal and breaks it down: (a) check weather history, (b) identify typical surfing conditions, (c) match optimal weeks.
2. Tool Integration: The agent pulls multi-year weather data for Greece and recognizes it still needs specialized surfing criteria.
3. Collaboration with Specialized Agents: It queries a “surf expert” AI agent to define ideal conditions (like high tides and sunny weather with low rain).
4. Inference: Combining weather patterns and surfing parameters, your agent suggests the most promising week for your trip.

Through this process, the agent demonstrates deep reasoning and the ability to leverage a network of tools and agents, a leap beyond what fixed-rule or static AI models can do.

Why Do AI Agents Matter?

AI agents are reshaping industries by automating complex workflows—from customer support bots that resolve queries autonomously, to business analytics tools that gather and assess data in real time. Their ability to plan, reason, adapt, and harness new tools on demand is what makes them distinct and extremely valuable for modern organizations.