A shift is taking place inside software teams. Coding assistants that once helped with small tasks are starting to take on larger roles. At Google, that shift is showing up in an internal system known as “Agent Smith,” an AI agent designed to handle parts of the development workflow with less human input.
Details about the system emerged from recent internal discussions reported by The Times of India, which cited remarks from Sergey Brin during a company town hall. According to the report, Google is testing an AI agent that can automate coding tasks and handle parts of development workflows. The report also says the system connects with internal tools used by developers.
This reflects a broader change in how AI is being used in software work. Earlier tools focused on code suggestions or autocomplete. Systems like Agent Smith point to something else: software built to handle multi-step tasks with less step-by-step input.
Moving past autocomplete
AI coding tools are not new. Over the past few years, developers have used assistants to write functions and fix bugs. Some tools also suggest test cases. These tools continue to rely on direct prompts and human guidance.
Agent-based systems aim to go further. Instead of waiting for instructions at each step, an agent can organise a task, break it into smaller parts, and then carry out some of those steps on its own. In practice, that could mean generating and testing code. It may also update related files or prepare changes for review.
The report on Google’s internal work suggests that its system is designed to handle more than just code generation. It can also interact with internal platforms, including documentation systems and issue trackers, according to The Times of India. Separate reporting by Business Insider notes that the system can access internal documents and employee data to complete assigned tasks.
That level of access changes the scope of what AI can do. It moves from assisting a developer to acting as a participant in the workflow.
From tools to agents
This shift aligns with a wider trend across the industry. Companies are exploring “agentic” AI systems that can act with some degree of autonomy.
In this model, the developer’s role changes. Instead of writing every line of code, the developer may define goals and review outputs. The developer may also step in to guide the system when needed, while the AI handles more of the execution.
Recent reports suggest these systems are designed to operate with less direct supervision, taking on tasks that previously required step-by-step input. While still early, this helps explain why companies are testing more advanced systems that go beyond code suggestions.
Changing how work is structured
One of the more immediate effects of agent-based systems is how they change the flow of work. Traditional development often depends on real-time collaboration. Developers write code, review each other’s work, and coordinate tasks during shared working hours. An AI agent introduces a more asynchronous model.
Tasks can be assigned to the system, which works through them over time. Reports indicate that developers can assign tasks to Agent Smith and return later to review the results, rather than guiding each step in real time. This does not remove the need for human input. Instead, it shifts where that input happens. Review and decision-making become more central.
It also raises new questions about oversight. If an AI system can make changes across multiple parts of a codebase, teams need ways to track what it does and ensure those changes are correct.
What Google’s AI agent can do beyond writing code
Another notable point from the report is that Google’s system is designed to interact with internal tools, not just code editors. This suggests a broader role for AI in engineering environments.
In practice, this could include updating documentation, managing tickets, or coordinating parts of the release process. These are tasks that often sit outside core coding work but still take up time. By handling these steps, an AI agent could reduce some of the overhead that developers deal with each day. At the same time, it introduces new dependencies on the system itself.
If the agent becomes part of the workflow, teams need to understand how it operates, where it may fail, and how to step in when needed.
What Google’s AI agent signals for developers
Google has not released full technical details about Agent Smith, and it remains an internal system. That makes it hard to assess how well it performs in practice.
Still, the direction is clear, as large tech firms move beyond simple assistants and test systems that can take on more responsibility.
The response from developers has been mixed. Some see these tools as a way to reduce repetitive work. Others are cautious about accuracy and control. Some are also concerned about the long-term effect on skills.
A gradual transition
The move from copilots to agents is unlikely to happen all at once. Most teams will continue to use a mix of tools, with humans still making key decisions.
What is changing is the baseline. Writing code line by line is no longer the only way to build software. Systems that can plan tasks, execute steps, and connect with existing tools are starting to take on a larger role.
Google’s internal work offers a glimpse of that shift. It shows how AI is moving deeper into the development process, not just helping at the edges, but taking part in the core workflow.
The focus may move away from writing every detail and toward reviewing the work that AI produces, with developers spending more time guiding that process and shaping the final result.
(Photo by Pawel Czerwinski)
See also: AI coding tools move into performance tracking at enterprise level
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