Google is increasingly using artificial intelligence agents to automate some of the work traditionally performed by forward deployed engineers, according to a report by The Information. The development highlights how AI is beginning to affect not only routine business operations but also highly specialised technical roles that were created to help companies deploy AI successfully.
Forward deployed engineers, often called FDEs, work directly with customers to solve practical technology problems. In the enterprise AI market, their responsibilities can include understanding a company's data infrastructure, organising proprietary information, developing applications and adapting AI systems to specific business requirements.
According to The Information, Google Cloud is now using AI agents to automate parts of this process, particularly work involving the organisation of enterprise data. Andi Gutmans, Google's vice president and general manager overseeing database products, said AI agents can perform some of the tasks that FDEs previously handled manually.
The development is notable because companies across the technology sector have been investing heavily in forward deployed engineering teams. Firms including OpenAI, Anthropic, Microsoft and Amazon have been hiring engineers who work closely with customers to turn experimental AI systems into practical enterprise applications.
Google's approach suggests that the role of these engineers could evolve as AI agents become more capable. Rather than requiring an engineer to perform every data preparation or technical configuration task manually, an AI agent could potentially execute repetitive steps while the engineer supervises the process and handles more complicated problems.
Google Cloud has simultaneously announced plans to hire hundreds of forward deployed engineers to help customers determine which applications they should build using Gemini AI tools. That means the company does not appear to be abandoning the human engineering model. Instead, it is combining human expertise with automated AI systems.
This distinction is important for understanding the wider effect of enterprise AI. Automation does not necessarily mean that an entire occupation disappears immediately. In many cases, individual tasks are automated first, allowing employees to spend more time on planning, customer relationships, problem solving and oversight.
Google's own research and product strategy reflects this shift toward agent based business systems. The company's 2026 AI Agent Trends Report says AI agents can understand a goal, create a multi step plan and take actions under human guidance. Google expects agents to become increasingly integrated into business workflows rather than being used only as simple chatbots.
Google Cloud has also been promoting agentic AI for enterprise data. Its Agentic Data Cloud is designed to help organisations connect data sources, manage enterprise information and make data available to AI systems. The company has said that businesses are moving from basic AI assistants toward systems capable of carrying out more complex workflows.
The underlying challenge is that enterprise AI depends heavily on data quality. A powerful AI model can still provide poor results if a company's information is fragmented, badly organised or difficult for the system to access. Forward deployed engineers have traditionally helped solve these problems by understanding the customer's technology environment and designing practical solutions.
AI agents could now take over some of the more repetitive portions of this process. An agent could inspect data structures, identify inconsistencies, organise information and recommend changes, potentially reducing the amount of manual engineering required.
However, complex enterprise environments are unlikely to be fully automated by a single AI system in the near term. Large companies often have complicated databases, legacy applications, security restrictions and industry specific compliance requirements. Human engineers may still need to make important architectural decisions and validate whether automated recommendations are appropriate.
Security is another major consideration. Enterprise data can contain confidential financial information, customer records, intellectual property and other sensitive material. AI agents operating on such information require strong access controls, monitoring and governance. Google itself has emphasised security and governance as important parts of deploying agentic AI at scale.
The technology sector is therefore moving toward a model in which engineers and AI agents work together. Instead of spending large amounts of time on repetitive data preparation, engineers could focus on designing workflows, supervising agents, checking outputs and solving unusual customer problems.
Other technology companies are pursuing similar strategies. OpenAI has reported that its own employees increasingly use Codex, its AI coding agent, for tasks including automation, data transformation, debugging and structured analysis. The company said AI agents are increasingly handling tasks that would previously have required significant amounts of human time.
The trend could have broader implications for employment in technology consulting and enterprise software. Companies may eventually require fewer engineers for repetitive implementation work while increasing demand for people who can manage AI systems, understand business processes and handle complex technical decisions.
For customers, greater automation could reduce the time required to deploy AI applications. If an AI agent can prepare data and perform routine configuration work more quickly, companies may be able to move from experimentation to production faster.
Google Cloud has already been working with enterprises to deploy AI agents across sectors. The company says businesses are using agents to automate manual workflows, support customer operations and turn large amounts of data into actionable information.
The development also illustrates an important contradiction in the AI industry. Technology companies are hiring specialists to help customers adopt AI while simultaneously developing AI systems that can perform portions of those specialists' work. This does not necessarily make the human roles obsolete, but it changes what those jobs involve.
The future enterprise engineer may therefore spend less time manually organising information and more time supervising automated systems, designing AI workflows and ensuring that technology produces reliable business outcomes.
For Google, the strategy could provide a way to scale enterprise AI adoption without increasing engineering resources at the same rate as customer demand. For businesses, it could mean faster implementation and lower costs, provided the automated systems can meet security, accuracy and reliability requirements.
The development remains part of a broader transition toward agentic AI, in which software systems do more than generate answers. They can plan tasks, use tools, interact with business systems and complete multiple steps with varying degrees of human supervision.
Google's reported use of AI agents for forward deployed engineering tasks therefore offers an early example of how AI may reshape highly skilled technical work. The most likely near term impact is not the disappearance of engineers, but a change in how their time is spent and which skills businesses value most.