How AI Agents Will Transform Everyday Computing in the Next Five Years

Although software has made great progress over the past decades, it is still somewhat clumsy: to complete a task you must tell the device which application to use, and even the best websites can only partially understand your work, personal life, interests, and relationships. In the next five years this will change dramatically; you will no longer need different apps for different tasks. By simply describing what you want in everyday language, an AI‑driven personal assistant—called an “Agent”—will understand you and respond based on the amount of information you share.

In computer science and AI, an Agent is defined as an autonomous computing entity that can perceive its environment, make decisions, and take actions to achieve its goals. Agents may be physical, such as robots, or virtual, such as software programs, and they exhibit autonomy, social ability, reactivity, and proactivity, enabling them to operate independently in complex environments and interact with users or other agents.

Below is a basic workflow for building an Agent application on a platform:

Platform selection and registration: Register and log in to the Qianfan Large Model Development and Service Platform, which provides a rich model library and toolset for model training, inference, and application development.

Model selection and training: Choose an appropriate base model according to application needs, adjust parameters and training strategies to improve accuracy and stability.

Agent design and development: Design the Agent’s architecture and workflow in the provided development environment, using APIs and toolsets to implement perception, reasoning, and execution, and create UI/interaction designs as required.

Integration and testing: Integrate the Agent into the target application and conduct functional, performance, and user‑experience testing to ensure stable operation and user satisfaction.

Deployment and release: Deploy the tested Agent to production, promote it on app stores or online platforms, and continuously collect user feedback for iterative optimization.

In large‑model application development, the Agent technology framework is the core that supports the entire development process. A complete Agent framework typically includes four key elements: perception, decision, action, and learning. Perception gathers environmental information; decision makes choices based on that information; action executes the decisions; and learning enables the Agent to improve over time by learning from experience. The framework also incorporates memory mechanisms, tool‑calling skills, and a reasoning engine, allowing Agents to operate efficiently and autonomously in complex environments.

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