By Chris Radich

| December 17, 2025 04:59 PM ET

COMMENTARY | Agentic government is not a future concept. It is a practical operational model agencies can adopt today.

• Artificial Intelligence
• Workforce

Federal agencies have spent years modernizing, but modernization has not always made work easier. Many programs added tools without improving how work actually flows. Automation grew, but often in pockets with little coordination, shared data or mission alignment.

We are now at the point where automation, machine learning and agentic orchestration can genuinely work together. This is not theory. It is already happening in defense and civilian agencies that have moved past pilots and into production, using agents that bring context, consistency and speed to complex workflows while preserving accountability.

These seven principles for an agentic government give leaders a practical framework for adopting automation and AI responsibly. They are designed to spark the right conversations and guide real operational decisions.

A few questions worth asking:

• Are our automations coordinated or scattered across silos?
• Do we know exactly where AI fits into each workflow?
• Can we explain every digital decision if asked?

If the answers are unclear, start here.

1. Orchestration Over Silos

Principle: Coordination is modernization.

Agencies do not need more tools. They need the tools they have working together. Today, hundreds of bots and AI models run independently across government, creating duplication instead of progress. Agentic Government starts with orchestration: a single operational fabric that connects human work, automation, machine learning and oversight. It provides visibility, routing, exception handling and accountability.

The Navy and Treasury have already shown the impact. By orchestrating RPA, ML models and human review, they reduced months-long document and data reconciliation cycles to days. The success came from orchestration, not any single technology. Standalone language models will not scale this impact. An independent orchestration tier will.

2. Human in the Loop, Not Human Out of the Loop

Principle: Humans remain in control.

Agentic systems can manage complexity, but they must operate within a clear chain of command. The model that works in government is accountable autonomy. Agents move quickly, but humans define the mission, approve exceptions and own the outcome. Every agent should log its actions, raise exceptions and ask for guidance in edge cases. This keeps autonomy effective and explainable. Approval queues are mandatory.

3. Mission Outcomes Over Model Scores

Principle: Impact beats benchmarks.

Technical performance matters, but it is not the mission. The real measure of AI and automation is whether they improve readiness, auditability, speed, accuracy or service delivery.

If an automation or model does not serve a mission or process owner, it is not production ready. It is a demo.

4. Transparency Over Black Boxes

Principle: Trust is built on traceability.

Government must be able to explain every digital action. Agentic systems should provide clear, auditable logs, not summaries or approximations. Opaque systems erode trust. Transparent ones make compliance easier. If you cannot explain it, you cannot deploy it.

5. Workforce Enablement Over Workforce Reduction

Principle: Automate the work, not the worker.

Agentic automation expands capacity. It frees employees from repetitive tasks so they can focus on analysis, judgment and mission execution. Agencies that pair automation with upskilling get stronger adoption and better outcomes. Empowered teams move faster. Fear slows everything down.

6. Processes Over Systems

Principle: Fix the work before you automate it.

Too many modernization efforts start with system upgrades instead of process redesign. Agentic automation succeeds when it targets the workflow, not the software. If you automate a broken process, it remains broken. Start with the mission flow, such as benefits eligibility or procure-to-pay. Then apply automation. Process is the true unit of transformation.

7. Deterministic First, Non-Deterministic Second

Principle: Predictability builds confidence.

Rules-based automations are the foundation. They are consistent, repeatable and explainable. AI adds intelligence on top, enabling agents to interpret documents, classify data and make recommendations. Government needs both. A mature architecture uses deterministic steps where control matters and AI where flexibility is required, all under transparent governance.

Putting the Principles to Work

These principles matter only if they change how work gets done. Leaders can start now:

**1. Get visibility. **Inventory where automation and AI already sit across the agency. You cannot orchestrate what you cannot see.

**2. Assign a commander. **Name a clear owner for automation governance and agent behavior.

**3. Redesign one workflow. **Pick a core process, such as procure-to-pay. Rebuild it using orchestration, transparency and human-in-the-loop oversight.

**4. Upskill the workforce. **Offer recurring sessions that help staff understand how digital agents work and how to direct them.

**5. Require auditability. **Every automation should have an audit trail, approval workflow and clear escalation path.

Real progress will not come from experimental models sitting in labs. It will come from connecting existing systems, expanding RPA, using ML where documents exist and integrating LLMs securely inside orchestrated workflows.

Agentic Government is not a future concept. It is a practical operational model agencies can adopt today, turning automation, AI and human expertise into measurable mission results.

Chris Radich is the public sector CTO and vice president for customer success at UiPath, where he advises government executives on adopting agentic AI, automation,and other emerging technologies to accelerate mission impact. He helps public sector organizations manage large scale technology transformations, including the shift to cloud, AI and intelligent agents.