AI Agents Are Ready for Government Work – If Agencies Are Ready for Them
For years, federal agencies have leaned on automation to speed up routine work. However, when faced with unexpected situations—such as an unfamiliar data field or a missing file—the automation process would halt until a human could intervene. This bottleneck is now being addressed by agentic systems.
AI agents, built on top of large language models, do not require step-by-step instructions; they only need a clear objective. These systems can reason, adapt, and act autonomously to achieve their goals, mirroring human decision-making capabilities.
The New Model of Work
AI agents can independently determine the steps necessary to achieve a goal and perform dynamic tasks. Unlike traditional robotic process automation, which follows scripted instructions, agentic systems can handle complex workflows autonomously. For instance, during the employee onboarding process, a traditional HR staff member would follow a series of manual steps. In contrast, an agentic system could manage the entire onboarding process from a single instruction, such as “onboard this employee,” adapting as new policies or forms are introduced.
For the federal workforce, this transformation implies fewer repetitive tasks and a greater focus on high-value judgment and problem-solving. Employees transition from being operators of rigid systems to supervisors of intelligent ones, resulting in faster service, fewer errors, and more time for mission-critical work.
In the federal health sector, AI agents could verify data, prepare case files, and triage routine claims, allowing human caseworkers to concentrate on exceptions and oversight. Clinicians could utilize AI agents to automatically schedule follow-up appointments and update care plans, significantly enhancing efficiency.
Furthermore, agentic systems promise to deliver more human-centered services. Citizens interacting with federal portals will no longer need to navigate complex forms and menus; instead, they can simply state their intent, such as “I need to renew my benefits,” and the AI agent will handle the rest, including data collection and validation.
Trust, Transparency, and Governance
As AI systems take on increased responsibilities, agencies must establish clear rules for decision-making, monitoring, and review processes. The objective is not to hinder innovation but to manage it responsibly.
Trust begins with visibility. Users need to see what the agent is doing and why. Every recommendation or action taken by AI agents should be traceable to data sources and rules. For high-stakes outcomes, such as benefits determinations or healthcare decisions, human approval remains essential.
The federal framework for trustworthy AI is grounded in the National Institute of Standards and Technology (NIST) AI Risk Management Framework, which emphasizes principles like transparency, security, fairness, and human oversight. Governance should parallel employee management, with agents acting within their defined roles, adhering to policies, and reporting on their activities.
Agencies can implement agentic transformation in phases. Initially, they should pilot limited use cases in low-risk environments, such as internal helpdesk support. Following this, controlled expansion into larger workflows can occur, incorporating real-time monitoring and human oversight.
As confidence in these systems grows, agencies can scale their use across departments, establishing shared standards for agent development and governance. The final phase involves optimizing and innovating the systems, fine-tuning models, and enhancing performance based on user feedback.
The Moment to Act
Agentic transformation presents a challenge not only in terms of technology but also in leadership. Industry leaders must assist federal agencies in defining their vision, establishing guardrails, and fostering trust. Successful transition requires collaboration among IT, policy, and mission teams, along with training to effectively manage AI agents.
Industry leaders can begin by asking the following questions:
- Where could agentic systems improve mission delivery?
- What governance, security, and data standards must be established before scaling?
Industry support is essential in laying the groundwork for broader adoption, following principles such as:
- Start small, but start now.
- Identify processes where intelligent autonomy can yield significant benefits.
- Pilot responsibly, measure outcomes, and share results.
When executed correctly, agentic transformation can enhance government efficiency, speed, and human-centered services, ultimately strengthening trust between citizens and the institutions that serve them.
