By Ramakrishnan (Ramki) Krishnamurthy, Data Analytics Lead at REI Systems
A federal benefits agency processes thousands of applications daily. An agentic AI system routes each one, requests missing documents, flags anomalies, and updates applicants, all without a single human touchpoint until a case hits a defined risk threshold.
This is not a future scenario. Agencies are beginning to deploy systems like this today, and most governance frameworks are not ready for them.
For the past decade, AI in government has primarily meant prediction, fraud detection, anomaly flagging, and demand forecasting. These tools improved efficiency but left humans in control of every consequential action. Agentic AI changes that equation. These systems don’t just analyze; they act.
This transition from predictive to operational AI has important implications for oversight.
While agentic systems help agencies manage massive workloads with limited staff, they also introduce a new category of risk: the “action hallucination,” where a system takes a wrong or unauthorized action rather than just providing a wrong answer.
Federal leaders must address these challenges now, ensuring that as these systems move from experimentation to production, they do so under a framework of rigorous, real-time governance.
Start With the Right Mental Model: AI as Operational Infrastructure
Agentic AI behaves differently from traditional models and our governance must reflect that.
Current policy, centered on M-25-21, urges agencies to “remove unnecessary bureaucratic restrictions.” However, because agentic systems function more like operational infrastructure than static models, they require a different kind of lean governance. They interact continuously with live data, users, and other software while pursuing assigned goals.
Because their behavior can evolve as conditions change, agencies should treat agentic AI less like a software tool and more like an active participant in mission delivery. This requires moving from static “compliance checklists” to continuous performance monitoring and automated technical guardrails.
Govern AI Across Its Lifecycle
Under the current framework, agencies must implement minimum risk management practices for “High-Impact AI,” defined as systems where the output serves as a principal basis for decisions affecting civil rights, safety, or material benefits.
Effective governance for agentic AI must scale with the system’s maturity:
- Technical Validation: Early prototypes should operate in “sandbox” environments. At this stage, the focus is on ensuring the agent remains strictly isolated from live operational systems.
- Production Safeguards: For High-Impact deployments, agencies need real-time visibility into system activity. To prevent “action hallucinations,” agencies should implement action-token validation. For example, an agent may be authorized to draft a benefit approval but require a cryptographic trigger or a human click to execute the actual financial transaction.
Design Oversight That Works at Machine Speed
Many government AI policies emphasize keeping a human in the loop. That principle remains important, but it must be implemented in a practical way.
Agentic systems operate at a pace that human reviewers cannot match. Automated systems may process thousands of requests in the time it takes a government employee to review a single case. When systems operate at this scale, traditional human review cannot monitor every action.
This creates what governance experts describe as an oversight illusion. Humans remain formally responsible for decisions, yet the volume of automated activity makes meaningful supervision difficult.
A more effective model positions humans as risk gatekeepers.
In a permitting workflow, for example, an AI agent could route applications, request documentation, schedule inspections, and update applicants automatically. Human officials would review high risk cases or make final approval decisions. Routine coordination would occur automatically.
This structure preserves accountability while allowing agencies to benefit from automation.
Prepare for Multi-Agent Systems
Another challenge agencies must anticipate is the complexity created by multi-agent systems.
Even modest deployments may involve several autonomous agents working together. A public works system could include one agent that monitors infrastructure conditions, another that evaluates repair costs and available funding, and a third that schedules maintenance crews.
Each additional agent increases the number of automated decisions occurring across systems. If one agent behaves incorrectly, the error can propagate through the workflow.
Production deployments therefore require centralized monitoring, clear rules for resolving conflicts between agents, and the ability to intervene quickly when problems arise.
Build on Existing Governance
Federal agencies do not need to build governance frameworks from scratch.
Most organizations already maintain governance structures for data management, cybersecurity, and AI development. The task now is extending those frameworks so they address autonomous behavior.
In practice this often involves combining existing governance platforms with tools that monitor AI behavior. These tools help detect model drift, audit decision patterns, and identify policy violations in real time. Integrated governance systems allow agencies to oversee both the data and the agents acting on it.
Governance as an Enabler of Responsible Innovation
Strong governance is not a bottleneck; it is an accelerator. As the administration’s AI Action Plan makes clear, the goal is to drive mission effectiveness by removing barriers.
Agencies that establish clear guardrails can experiment more confidently, scale successful solutions more quickly, and maintain public trust. By integrating governance platforms with tools that detect model drift and audit decision patterns in real time, agencies can avoid the “technical debt” of building advanced automated systems on top of fragmented oversight.
Agentic systems are already reshaping the “machinery of government.” Federal leaders have a window to strengthen their frameworks now, ensuring that these systems remain a tool for the mission and a pillar of American AI leadership.
