Coding agents are the real leap in AI

Every few weeks, a new artificial intelligence model appears. First it was ChatGPT, then Gemini, then Claude, and in a few months we will probably have a new reference again.

With every launch, the same conversation repeats: which model codes better, which one reasons better, which one understands more context, or which one gives better answers.

But I am increasingly convinced that this debate is becoming less important.

The real leap is not switching models. It is changing the way we work with them. For the last few years we have interacted with AI mostly through chat. That has been extraordinarily useful for answering questions, drafting text, and solving isolated problems. But that paradigm is starting to feel limited.

The next evolution is not a smarter chat. It is the use of coding agents.

And despite the name, I think these tools will become increasingly relevant for many professional profiles, not only for developers.

From answering questions to executing work

The difference between a conversational assistant and an agent looks small on paper, but it changes the entire experience.

When we use a chat, we usually ask questions or request very specific tasks: summarizing a document, drafting an email, translating text, or explaining a concept. The model replies, and the conversation continues.

With an agent, the approach changes. Instead of asking for one isolated action, we give it an objective and let it decide how to reach it.

That means it can explore a project, modify files, run programs, read documentation, call APIs, use external tools, check whether the result is correct, and repeat the process until it reaches a satisfactory solution.

In other words, it stops being limited to conversation and starts doing work.

This shift has already started

The industry has been clearly moving in this direction for months.

Claude Code was one of the first products to show that a model could work autonomously for long periods on the same objective: navigating a project, running commands, fixing errors, and iterating until a task was complete.

Then came proposals such as Claude Cowork, OpenAI Codex, and the agentic modes available from Google AI Studio. Each one takes a different approach, but they all share the same philosophy: combining the model's reasoning ability with tools that let it act in the real world.

It is not just about answering better.

It is about doing more.

The model is no longer the whole story

For a long time, we compared language models as if they were the central element of any AI solution.

But when you start working with agents, you realize the model is only one part of the system.

What matters is everything it can interact with.

An agent can connect directly to APIs from specialized providers such as Google AI Studio, Replicate, Fal.ai, or Runway. It can use command-line tools installed on your computer, run internal scripts, query databases, interact with desktop apps, automate browsers, access cloud infrastructure, or use a company's internal services.

The model stops being the thing that performs every task by itself and becomes the coordinator of a set of specialized tools.

This is similar to what happened years ago in software development. Applications rarely solve everything by themselves; they orchestrate databases, external services, APIs, and specialized libraries. Agents work in the same way.

The difference is the ability to act

A language model by itself only generates text.

An agent adds one fundamental capability: deciding when it needs a tool to keep moving.

It can interpret a request, decide it needs to call an API, run a command, open a browser, generate an image, launch a render process, or analyze a file. Then it brings that result back into context and keeps working.

The difference may sound technical, but it changes the use cases that become possible.

We are no longer talking only about content generation.

We are talking about task execution.

MCP and a more open ecosystem

Another reason this shift is accelerating is the appearance of standards such as MCP, the Model Context Protocol.

Until recently, each integration between an assistant and a tool required custom development. Now we are starting to have a common protocol that exposes capabilities so different agents can use them without building separate integrations for every platform.

It is comparable to what REST APIs did for web applications.

As more tools adopt these standards, it becomes easier to build agents that combine very different services without depending on a single provider.

Skills democratize this potential

There is another concept that may become as important as agents themselves: skills.

A skill encapsulates the knowledge needed to solve a specific task. It can include specialized instructions, optimized prompts, scripts, API calls, templates, tools, or best practices.

The advantage is clear.

The person using the agent does not need to know all those details. They simply describe what they want to achieve, and the agent reuses prepared knowledge.

This opens the door to sharing expertise across people and teams much more easily. Just as we reuse software libraries or components today, we will reuse skills capable of solving complete tasks.

Iteration stops being manual

Working with a chat often means repeating the same loop: write a prompt, get a response, correct the prompt, and try again.

With an agent, that iteration can happen autonomously.

It can create a first version, check whether it works, detect errors, fix them, run the process again, and repeat as many times as needed before showing the result.

That greatly reduces the time required to complete complex tasks and makes processes viable that previously needed constant human intervention.

Use cases that recently felt impossible

One area where this shift is especially visible is multimedia content creation.

Today an agent can write a script, generate narration, produce images, create video clips, generate music, record a web app automatically with Playwright, capture screens, assemble a project in HyperFrames or Remotion, and render the final result.

What is interesting is not that it can do this once.

It is that if we request changes, it can reuse existing resources, generate only what needs to change, and rebuild the whole project automatically.

The same approach applies to many business processes.

It can interact with web apps to download information, fill forms, generate reports, update data, or coordinate several SaaS tools without depending on a specific integration inside a chat platform.

And because every company has different processes, we can also build agents that use our own APIs, scripts, or internal tools.

At that point, they stop being generic assistants and become tools adapted to the business.

What if you do not know how to code?

This is probably the most common objection when people talk about coding agents.

But I also think it will be one of the first to disappear.

Most people never needed to understand how a word processor works to write a document. In the same way, they will not need to understand how a skill is built or how an API is integrated to benefit from an agent.

Some profiles will specialize in building those capabilities, while many others will simply use them through natural language.

Over time, the term "coding agent" will probably stop making sense. Code will be just one tool inside a much larger set of capabilities.

The future does not belong to the best chat

We will keep talking to AI through natural language.

That will not change.

What will change is what we expect to happen after we write an instruction.

Instead of receiving only an answer, it will become increasingly normal to expect the agent to act, coordinate tools, reuse existing knowledge, and complete a task from end to end.

That is why I think the future of AI will not be determined only by who has the best model.

It will be determined by who builds the most useful agents.

And when that happens, we will probably stop asking which chat is best and start asking which agent we want to work with.