What Are AI Agents? Definition & Examples

The rise of Large Language Models (LLMs) has transformed what’s possible with automation, yet orchestrating complex, multi-step AI tasks remains a significant challenge. Traditional scripts fall short when systems need to dynamically adapt, learn, and interact with various tools to achieve a goal. This is where AI agents step in – autonomous entities designed to perceive environments, make decisions, and take actions without constant human oversight. This article will demystify AI agents, exploring their core components, different types, and how they are reshaping software development, customer service, and infrastructure automation.

What are AI agents?

An AI agent is a software program that can perceive its environment, process that information, and take autonomous actions to achieve specific goals. Unlike a simple script that follows a rigid set of instructions, an agent operates within a perception-action cycle: it senses changes, makes decisions based on its programming and past experiences, and then acts upon its environment.

This ability to operate autonomously is what sets AI agents apart. They are designed to be proactive and persistent, capable of running complex, long-duration tasks without direct human intervention. This could involve anything from monitoring a system for anomalies to executing a multi-step financial transaction or provisioning cloud infrastructure. The core idea is to delegate not just the “how” but also the “what” and “when” of a task to a system that can reason and react. For a deeper dive into how Kestra implements this, explore our documentation on AI Agents in Kestra.

Key characteristics of AI agents

AI agents are more than just automated scripts. They possess a unique combination of characteristics that enable their autonomous and intelligent behavior.

Reasoning and planning capabilities

At their core, agents have the ability to reason. They analyze their current state, consider their goals, and formulate a plan to bridge the gap. This involves breaking down a high-level objective into a sequence of smaller, actionable steps. For example, an agent tasked with “deploying a new application” would need to reason about dependencies, check for available resources, execute deployment scripts, and verify the outcome. This strategic thinking allows agents to handle novel situations and complex, multi-stage problems that would stump a pre-programmed script.

Memory, learning, and adaptability

An agent’s ability to learn and adapt is crucial. This is facilitated by memory, which can be short-term (context for the current task) or long-term (knowledge gained from past experiences). By remembering what worked and what didn’t, an agent can improve its performance over time. This continuous learning loop allows it to adapt to new data, changing environments, or evolving goals. For instance, a customer service agent can learn from thousands of interactions to provide more accurate and helpful responses. Kestra provides mechanisms for managing agent memory, a key component for building stateful, intelligent systems.

Interaction with environments and tools

Agents are not isolated. Their primary function is to interact with an environment to achieve a goal. This environment can be digital (a file system, a database, a set of APIs) or even physical (via robotics). To perform meaningful actions, agents rely on tools. A tool could be anything from a web search API to a code execution environment or a database query interface. The ability to select and use the right tool for a given sub-task is a hallmark of a sophisticated agent. This often includes knowing when to involve a human for approval or clarification, creating a human-in-the-loop system.

Types of AI agents

AI agents are not a monolith. They exist on a spectrum of complexity and capability, typically categorized into five main types.

Simple reflex agents

These are the most basic agents. They operate solely on a condition-action rule basis, reacting to the current state of the environment without any consideration for past history. Think of a thermostat: if the temperature is above X, turn on the AC. They have no memory and cannot learn.

Model-based reflex agents

A step up from simple reflex agents, these agents maintain an internal model or state of the world. This internal representation allows them to handle partially observable environments by reasoning about the aspects they cannot directly see. Their decisions are based on both the current perception and their internal model of how the world works.

Goal-based agents

These agents are designed with a specific goal in mind. They use their model of the world to plan sequences of actions that will lead them to their desired state. This involves search and planning algorithms to find the optimal path to the goal. For example, a navigation app finding the fastest route is a form of goal-based agent.

Utility-based agents

When multiple paths can lead to a goal, a utility-based agent chooses the one that maximizes its “utility” or happiness. This is useful in complex scenarios with trade-offs, like balancing speed, cost, and safety. The agent evaluates the desirability of different outcomes and selects the action that leads to the best possible state, not just any goal state.

Learning agents

Learning agents are the most advanced. They can improve their performance over time through experience. They consist of a learning element, a performance element, a critic, and a problem generator. The critic evaluates the agent’s actions, the learning element uses this feedback to modify the performance element, and the problem generator suggests new actions to explore. This allows the agent to operate in unknown environments and become more effective over time.

Applications and examples of AI agents

The theoretical capabilities of AI agents translate into powerful real-world applications across various domains, revolutionizing how we approach complex problems.

In software development and DevOps

Agents are becoming indispensable in modern software engineering. They can automate code generation, suggest bug fixes, and even write unit tests. In DevOps, they act as intelligent operators, monitoring system health, predicting failures, and executing automated remediation plans. An agent might detect a performance degradation, analyze logs to find the root cause, and then automatically roll back a recent deployment or provision additional resources. This moves beyond simple CI/CD pipelines to create self-healing, adaptive infrastructure. Blueprints like the one to add tasks to Todoist via AI show how agents can integrate with developer tools to automate routine tasks.

In customer service and business automation

In customer-facing roles, AI agents power sophisticated chatbots and virtual assistants that can handle complex queries, process transactions, and provide personalized support 24/7. Beyond customer service, they automate internal business processes. For example, an agent could manage invoicing by extracting data from emails, validating it against a database, generating an invoice, and sending it for approval. This level of automation streamlines operations and reduces manual error. A practical example is using an agent to draft GDPR breach notifications, a complex task requiring precision and adherence to legal standards.

Real-world use cases of AI agents

Leading technology and finance companies are already deploying agentic systems at scale.

• JPMorgan Chase uses AI agents for cybersecurity analytics, processing billions of data points to detect threats and trigger automated remediation workflows.
• Apple’s ML team orchestrates large-scale data and AI pipelines with agent-like systems to manage workflows across the App Store, Apple Music, and device diagnostics.

These examples highlight how agents are not just theoretical concepts but are being used to solve mission-critical problems in complex, high-stakes environments.

AI agents vs. traditional AI programs

The term “AI agent” is often used interchangeably with other AI concepts, but there are important distinctions to be made.

Distinguishing autonomy, proactivity, and persistence

While a traditional AI program might perform a specific task like image classification, an AI agent is defined by a higher level of capability:

• Autonomy: Agents can operate without direct, constant human supervision. They are given goals, not just instructions.
• Proactivity: Agents don’t just react to their environment; they can take initiative to achieve their goals.
• Persistence: Agents are often designed to be long-running, continuously monitoring and acting over extended periods.

A script that runs on a schedule to process data is automation. An agent that continuously monitors data streams, decides when to run a process based on evolving conditions, and adapts its process based on the results is an agentic system.

Understanding LLMs and agents: Is ChatGPT an AI agent?

A Large Language Model (LLM) like ChatGPT is a powerful component, but it is not an AI agent by itself. An LLM is a model that can process and generate text. An AI agent is a system that uses a model like an LLM as its “brain” to reason and make decisions. To become a true agent, an LLM needs to be integrated into a system that provides it with:

1. Memory: To learn from past interactions.
2. Tools: To interact with the outside world (e.g., APIs, file systems).
3. A Goal: A high-level objective to pursue.
4. An Execution Loop: A mechanism to plan and execute actions.

So, while ChatGPT can perform tasks when prompted, it doesn’t autonomously pursue goals or interact with external tools without being explicitly directed in each turn.

The 30% rule in AI: Fact or Fable?

You may have heard of a “30% rule” in AI, but it’s not a formal, widely accepted principle. The term can pop up in different contexts with different meanings. Sometimes it’s used as a heuristic suggesting that AI can automate roughly 30% of tasks in most professions. In other cases, it might refer to a specific performance metric in a research paper. Unlike foundational concepts like the Turing Test, the “30% rule” is not a standard term. It’s more of an informal observation or a context-specific rule of thumb rather than a universal law of AI.

Building and deploying AI agents with Kestra

Creating a single AI agent is one thing; deploying and managing a fleet of them in a production environment is another. This is where orchestration becomes critical.

Essential components for agent development

A robust AI agent requires several key components to function effectively:

• LLM Integration: A connection to a powerful language model (like GPT, Claude, or Gemini) for reasoning.
• Tool Access: A secure and reliable way for the agent to use external tools and APIs.
• Memory Management: A system for storing and retrieving information from past interactions.
• Orchestration Layer: A control plane to define, execute, and monitor the agent’s lifecycle and its interactions with other systems.

Kestra provides a comprehensive suite of AI tools to build and manage these components declaratively.

Kestra’s role in orchestrating AI agents

Kestra acts as the orchestration control plane for agentic AI systems. By defining agents and their workflows in declarative YAML, you can apply GitOps principles to your AI applications, ensuring they are version-controlled, auditable, and scalable.

id: ai-powered-incident-responder
namespace: company.team.sre

tasks:
  - id: incident-agent
    type: io.kestra.plugin.ai.agent.AIAgent
    model:
      provider: openai
      model: gpt-4
    prompt: |
      An alert was triggered for {{ trigger.alert_name }}.
      Analyze the logs from the past 15 minutes, identify the root cause,
      and suggest a remediation action. If confidence is high,
      execute the rollback procedure.
    tools:
      - type: io.kestra.plugin.ai.tool.KestraTask
        flowId: get-service-logs
        namespace: company.team.observability
      - type: io.kestra.plugin.ai.tool.KestraTask
        flowId: execute-rollback
        namespace: company.team.deployment

With Kestra, you can build complex, multi-agent systems where different agents collaborate to solve problems. The platform handles the state management, error handling, and parallel execution, allowing you to focus on the agent’s logic. This approach transforms agent development from an experimental process into a disciplined engineering practice. The journey from idea to production is accelerated by features like the AI Copilot for generating workflow code and a rich ecosystem of blueprints and plugins.

Challenges and best practices in implementation

Deploying AI agents comes with challenges. Agent “hallucination,” where the LLM produces incorrect or nonsensical output, is a significant risk. Other concerns include security (giving agents access to tools), cost management (uncontrolled API calls), and observability (understanding why an agent made a particular decision).

A robust orchestration platform helps mitigate these risks. By defining guardrails, timeouts, and approval steps within a Kestra workflow, you can ensure agents operate safely. The detailed logging and auditable history provide full observability into every action an agent takes, making it possible to debug and govern their behavior in production.

The future of agentic AI

The field of agentic AI is evolving rapidly, with trends pointing towards more capable and integrated systems.

Emerging trends and multi-agent systems

The next frontier is multi-agent systems, where teams of specialized agents collaborate to solve problems that are too complex for a single agent. Imagine an “analyst” agent that gathers data, a “coder” agent that writes a script to process it, and a “communicator” agent that summarizes the results and sends a report. Orchestrating these interactions is a key challenge that platforms like Kestra are designed to solve.

Impact on industries and daily life

The impact of AI agents will be transformative. They promise to create self-healing infrastructure, fully automated supply chains, and highly personalized digital experiences. As they become more capable, they will handle an increasing amount of the complex coordination and decision-making currently performed by humans. This shift will require new ways of thinking about automation, governance, and the partnership between humans and AI.

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