What’s Next After Generative AI: The Rise of Autonomous AI Agents

The last few years have seen a major shift in how public sector agencies and educational institutions use AI. Much of the focus has been on generative AI which are tools that create content like text, images, or dialogue on demand. But now, a new wave is emerging that will change the way we interact with systems. Autonomous or “agentic” AI systems that do more than generate, they act. In other words, the future of AI is moving from “tell me what to write” to “do this for me.” This is the AI for tomorrow.

From Generation to Action

Generative AI tools usually respond to human-made prompts such as “Create an infographic,” or “Write a report.” A great example of this is everyone’s favorite tool, ChatGPT. They are powerful, especially for education and public-service settings, however, they still rely on user input and supervision. By contrast, autonomous AI agents can be given a goal and then break it down with steps, plan, and execution to achieve results with minimal oversight. For example, in a school, an agent may be able to automatically compile data based on student grades and attendance to predict patterns, notify educators and staff, and suggest follow-up actions without someone there to trigger each step.

How can this affect the public sector?

Resources are tight and demands are growing in many institutions. Autonomous AI can free staff from mundane and routine workflows so they can focus on higher value work like strategy and community engagement. Imagine agents that operate across data fields that work 24/7 to alert of network risks, constantly track data trends, and even suggest intervention strategies all without human interaction.

What you need to consider.

Autonomous agents are not plug-and-play. They require quality data, clear governance, and thoughtful design. Agents only work well if the underlying data and processes are sound. If a government agency automates a broken workflow, then the agent will just automate the broken process. The decisions of these agents need to be explainable, especially when dealing with students, constituents, and/or public services making trust and transparency critical. When implementing, organizations need to start small with well-defined tasks like summarizing documents or meetings before scaling to bigger, more sensitive workflows.

What you should do now

Here are a few practical steps for organizations in the public sector to properly, and safely, implement autonomous AI:

• Identify workflows that are right for automation. These should be workflows that are often repetitive and require the same steps each and are data intensive. This could be something like network tracking or monitoring student progress.
• Ensure data readiness. Before deploying agents, review the quality of your systems and make sure that you are, right now, getting the results you need before automation.
• Build a level of oversight. Since agents are autonomous, clear accountability needs to be established where humans are kept in-the-loop of the workflows progress.

The future of AI in public service

As organizations get comfortable with generative AI, the next frontier is the “agentic” or autonomous AI that works on behalf of human teams. AI’s future is not just about content creation. But about real-world action and outcomes. For the public sector, this means looking ahead to how AI can not only assist, but execute tasks enabling everyone to work more smarter, more efficient, and focus on what more critical tasks ahead.