This week brought significant progress in making agentic AI more accessible and inclusive. At the Fortune Brainstorm AI conference, Google’s Sapna Chadha stressed the importance of human oversight in AI systems, explaining that agents must request user approval at key decision points to avoid unintended consequences. This approach ensures users retain control, a critical aspect of inclusive design. Google’s Project Astra exemplifies this vision, enabling agents to handle diverse tasks like diagnosing bike repairs via camera or initiating support calls, making complex processes more user-friendly.

AWS highlighted the need for reliability in agentic AI, noting that agents must achieve 90%+ accuracy to gain widespread trust. The company introduced tools like Amazon Nova, which automates tasks like email management or document processing, and Amazon SageMaker, which helps developers build compliant AI systems. These enterprise-ready solutions aim to reduce errors and ensure consistency, addressing concerns about unpredictable AI behavior. AWS also emphasized self-examination in agents, allowing them to adapt strategies mid-task and learn from past interactions, which improves their ability to handle diverse user needs.

New tools like Google Opal and Claude Code sub-agents are democratizing access to AI. Opal lets non-technical users design complex workflows using Google tools (e.g., YouTube, Docs), automating tasks like creating lesson plans or transcribing videos. For example, an Opal agent can generate educational materials by analyzing a video, conducting research, and producing worksheets and quizzes—all without coding. Meanwhile, Claude Code’s sub-agents handle repetitive coding tasks like debugging or quality assurance, freeing developers to focus on creative work. These tools lower the barrier to entry, enabling more people to benefit from AI automation.

Industry leaders also addressed ethical considerations. Google released a white paper outlining standards for secure AI agents, including strict guidelines to prevent data misuse or rogue behavior. Accenture’s research revealed that only 8% of companies have scaled AI adoption, but early adopters are seeing benefits in HR, finance, and IT. For instance, life sciences firms use agents to accelerate regulatory approvals, while banks employ them for fraud detection. These applications demonstrate how agentic AI can streamline workflows while maintaining compliance.

The focus on transparency and user control emerged as a recurring theme. Google and AWS both stressed the need for clear communication about AI actions and decision-making processes. By prioritizing these principles, developers aim to build systems that are not only powerful but also inclusive, ensuring they serve diverse user needs responsibly.