Recently, I wrote about Text-to-Action—the idea that we’ll eventually speak directly to computers in natural language instead of writing code. Of course, the immediate question was: “Sounds exciting, but how would that actually work?” The answer takes us straight to AI agents.

What are AI agents?

At their core, AI agents are software that does what you want without you needing to intervene. They take your intentions and execute them directly, acting like intermediaries between you and the real world.

We’re already familiar with early versions of this—virtual assistants like Siri or Alexa. But these systems are still pretty primitive. They can handle basic commands, but they fall apart when it comes to more complex tasks. What’s coming next is on a whole different level: agents that don’t just manage simple tasks but can execute entire workflows. And the best part? They get better over time. The more they do, the smarter they become.

A Practical Example: Planning a Trip to Berlin

The real advantage AI agents have over traditional software is their ability to understand and learn.

Take planning a trip to Berlin as an example. A normal travel app will give you a list of sights, but the actual planning is left to you. An AI agent goes much further: it factors in your personal preferences, checks the weather, suggests suitable restaurants, and creates a customized itinerary. All of this happens automatically—you don’t have to lift a finger.

But the true strength of AI agents lies in their ability to collaborate. Imagine a team of specialized digital assistants:

1. A travel expert who knows how to create unforgettable itineraries.
2. A Berlin insider who knows the city inside and out.
3. A logistics planner who ensures everything runs smoothly and efficiently.

These agents don’t work in isolation. They form a cohesive team, organizing themselves to handle the entire process—without you having to intervene. And they’re constantly adapting to your preferences, learning from previous tasks, and getting better at understanding your needs. Over time, you end up with a system that feels tailor-made for you, anticipating your desires intuitively.

A Glimpse into the Code

Here’s an example of an agent system in pseudo-code, showing how developers typically approach this. Each agent has a clear task, but the process isn’t pre-defined. The agents communicate and work together to find the best solution. That’s the key: instead of programming rigid workflows, we let the agents figure out the optimal way to complete the task. This is the kind of flexibility we need in more complex systems.

# Define the agents

travel_expert = Agent(
    role='Travel Expert',
    goal='Create perfect travel plans based on customer preferences',
    backstory="""You are an experienced travel consultant with 20 years of experience.
    You know the best travel destinations and how to plan unforgettable trips.""",
    tools=[search_tool],
    verbose=True
)

local_expert = Agent(
    role='Local Berlin Expert',
    goal='Share detailed knowledge about Berlin',
    backstory="""You have lived in Berlin for 30 years and know the city inside and out.
    From hidden gems to the best restaurants – you know everything about Berlin.""",
    tools=[search_tool, translation_tool],
    verbose=True
)

logistics_expert = Agent(
    role='Travel Logistician',
    goal='Optimize travel routes and plans',
    backstory="""You are an expert in travel logistics and can create perfect schedules.
    You know all transportation options and how to make the most of time.""",
    tools=[search_tool, calendar_tool, weather_tool],
    verbose=True
)

# Define the tasks

task1 = Task(
    description="""Create a list of 5 must-see attractions in Berlin.
    Consider both well-known landmarks and hidden gems.
    Provide a brief explanation for each attraction.""",
    agent=local_expert
)

task2 = Task(
    description="""Based on the attractions, create an optimal 2-day travel itinerary.
    Consider opening hours, distances, and typical visiting times.
    Add recommendations for nearby restaurants.""",
    agent=logistics_expert
)

task3 = Task(
    description="""Review and refine the travel plan. Add personal recommendations and
    insider tips to enhance the travel experience. Also, consider
    weather and seasonal features.""",
    agent=travel_expert
)

# Create and start the crew
crew = Crew(
    agents=[travel_expert, local_expert, logistics_expert],
    tasks=[task1, task2, task3]
)

result = crew.kickoff()

For these agents to do their jobs properly, they need the right tools. Without them, they’d be blind—they could understand what you want, but not execute it. In this example, the agents use a search tool. The Berlin expert uses it to look up landmarks, and the logistics planner uses it to find the best routes. But that’s just the start. A translation tool helps with multiple languages, a calendar tool ensures the schedule works, and a weather tool keeps the plan flexible. These tools are what allow the agents to adapt seamlessly to your needs.

How You Can Use AI Agents Today

If you’re ready to bring AI agents into your daily life, you have three options:

1. Build your own: If you have specific requirements, you can create your own agents. There are tools that can help you with this. This is the route to take if you want full control and maximum flexibility [1].
2. Use ready-made solutions: There are pre-built platforms like agent.ai that you can plug into your processes right away. This is the fastest and easiest way to get started.
3. Hybrid approach: You can start with an off-the-shelf solution and customize it as needed. This gives you the speed of ready-made tools while maintaining the flexibility to adapt the system to your needs.

Looking Ahead

AI agents are still in their early stages, but their potential is already clear. In the coming years, they’ll drastically change the way we work and live. They’ll become smarter, faster, and more accessible to everyone. Whether you’re building your own agents or using ready-made solutions, "Text-to-Action" is no longer a theory—it’s becoming a reality. It opens up new possibilities for automation and intelligent processes.

Imagine never having to book a trip, manage your finances, or handle tedious administrative tasks again. AI agents could take care of all of that for you—and this isn’t some far-off future. It’s already becoming possible. The real question is: what tasks would you hand over to focus on what really matters to you?