What makes this AI shift so radical is the ability to leverage multiple agents at the same time that work together to divide tasks and level up efficiencies and outputs. Within these multi-agent systems, each agent has a unique role and contributes specialized knowledge (taking on roles such as a software engineer, project manager, or analyst). Coordination between them is handled by a single orchestration agent.

The result is more sophisticated decision-making and streamlined workflows that remove routine tasks and error-prone manual steps from human workloads. Efficiencies gained through agentic AI can also help to address the challenges of an aging society and a shortage of skilled workers. From boosting workplace productivity to driving research and development, multi-agent systems have huge potential to free up people to focus on strategic and creative tasks across all industries. Businesses like Formula 1 have already reduced issue resolution time by 86 percent with the help of comprehensive AWS tools and capabilities to securely build multi-agent workflows.

Agentic AI is also starting to transform the way we think about product design and iterate on new ideas. Lovable is one example of how it is radically changing the way software is imagined and created. Non-technical users can create full-stack web applications using natural language, enabling anyone to bring ideas to life without coding expertise or undergoing complex development processes.

What’s more, hyper-personalization through agentic AI is creating unmatched user experiences that are closing the gap between current customizations. And, because it is standardized, companies can bring these experiences to life while maintaining low operating costs. For software-as-service (SaaS) companies with a broad product portfolio, agentic AI can empower them to create holistic workflows across their applications—stitching together their offering in a way that’s more appealing and effective for customers. Unlike AI assistants that respond to direct prompts, AI agents harness the context of the software development infrastructure to drastically streamline how code is deployed, tested, and iterated.