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. But the moment when anything unexpected—such as an unfamiliar data field or a missing file—appeared, the automation stopped until a human could intervene.

Now, agentic systems are breaking that bottleneck. AI agents, the intelligent layer built on top of large language models, don’t require step-by-step instructions. All they need is a clear objective. Then they can reason, adapt, and act to achieve that objective with a level of autonomy that mirrors human decision-making.

Federal leaders are standing at the edge of a pivotal shift. Traditional robotic process automation cannot fully address the complexity of today’s missions. For the federal government, the question is not whether to adopt agentic transformation but how to do so responsibly. The goal is not to replace people but to elevate them. Agencies that move first will shape the standards and frameworks that others follow. But industry needs to show them the way.

The New Model of Work

AI agents determine the steps they need to take to achieve a goal and perform multi-step, dynamic tasks. Instead of following “scripts,” step-by-step instructions provided by humans, they independently assemble the workflow required to get to the outcome.

Consider the onboarding process. Traditionally, human resources staff or software scripts follow a series of manual steps: creating user accounts, scheduling training, setting up payroll. An agentic system could handle the entire process from a single instruction, such as “onboard this employee,” and carry out the necessary actions across systems, adapting as new policies or forms are introduced.

For the federal workforce, agentic transformation means fewer repetitive steps and more focus on higher-value judgment and problem-solving. Employees become supervisors of intelligent systems rather than operators of rigid ones. The result is faster service, fewer errors, and more time for employees to spend on mission-critical work.

In federal health, AI agents could verify data, prepare case files, and triage routine claims, freeing human caseworkers to focus on exceptions and oversight. Clinicians who spend hours navigating multiple health record systems could direct the AI agent to automatically schedule follow-up appointments, notify patients, and update care plans.

Agentic systems will also allow government to deliver more human-centered services. Citizens interacting with federal portals will no longer have to navigate forms and menus. They will simply state their intent, like “I need to renew my benefits,” and the agent will do the rest, handling the data collection, validation, and notifications behind the scenes.

Trust, Transparency, and Governance

As AI systems take on more responsibility, agencies will need clear rules for how decisions are made, monitored, and reviewed. The goal is not to slow innovation but to manage it responsibly.

Trust begins with visibility. Users must be able to see what the agent is doing and why. Every recommendation or action 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 already exists in the National Institute of Standards and Technology (NIST) AI Risk Management Framework and executive guidance on responsible AI. These principles—transparency, security, fairness, and human oversight—must anchor every agentic system.

Governance should mirror the way we manage employees: agents act within their roles, follow policies, and report on their work. Agencies may even establish “AI controllers,” people responsible for reviewing system performance and ensuring compliance.

Agentic transformation can unfold in phases. First, agencies should pilot limited use cases in low-risk environments like internal helpdesk support or data triage.

Next comes controlled expansion, where agents are deployed into larger workflows with real-time monitoring and human oversight. As confidence grows, agencies can scale across departments, developing shared standards for agent development, audit logging, and governance.

Finally, agencies can move to optimization and innovation: fine-tuning models, introducing multi-agent collaboration, and continuously improving based on performance data and user feedback. Each phase should include training, communication, and clear metrics for success.

The Moment to Act

Agentic transformation is as much a leadership challenge as it is a technical one. Industry leaders can support federal agencies in defining the vision, setting guardrails, and modeling trust. The transition will require collaboration across IT, policy, and mission teams, plus training to help employees effectively manage AI agents.

Industry leaders can start by asking:

• Where could agentic systems improve mission delivery?
• What governance, security, and data standards must be in place before we scale?

Industry can support agencies in laying the foundation for broader adoption, following principles such as:

• Start small but start now.
• Identify processes where intelligent autonomy can deliver meaningful gains.
• Pilot responsibly, measure outcomes, and share results.

Done right, agentic transformation will make government smarter, faster, and more human, and it will strengthen trust between citizens and the institutions that serve them.

Murali Mallina serves as chief technology officer at Tria Federal, where he leads innovation through Tria Labs. An accomplished technology leader, he has over 27 years of experience in developing transformative software solutions and cultivating high-performing teams.