The Next Challenge for Government Agentic AI Is Trust

Government agencies are rapidly exploring artificial intelligence to improve productivity, automate workflows, and streamline operations. Across the public sector, AI is helping employees analyze information more quickly, reduce administrative work, and support better decision-making.

As AI begins taking on more autonomous responsibilities, agencies must also have confidence in how those systems operate. That means understanding what an AI system was authorized to do, what information it relied on, and whether its actions remained within established policies and delegated authority.

Jim Shaughnessy, Chief Legal Officer at Docusign, recently explored these issues in a TechUK guest opens in a new tab article titled "Agreements in the Era of Agentic AI: The UK's Chance to Lead." While the article focuses on the UK's opportunity to establish a trusted framework for agentic AI, the underlying questions around trust, accountability, and evidence are equally relevant for government organizations everywhere.

AI changes the accountability conversation

Most AI tools are utilized as assistants. They summarize information, generate content, or answer questions. Agentic AI expands that role by coordinating a series of steps on its own: analyzing agreements, applying business rules, routing workflows, recommending actions, and completing certain tasks with increasing autonomy.

For the government, this introduces an important governance challenge that’s easy to overlook. When AI helps run a procurement activity, a grant award, or a benefits determination, responsibility for those actions still belongs to the agency. Human accountability doesn’t disappear simply because AI participated.

This is why trust is becoming such an important part of the conversation. Agencies must trust that AI systems operate within clearly defined boundaries and that their actions can be explained and verified when a manager, an auditor, or a member of the public asks. 

This lands differently depending on where you sit. Federal agencies are already cataloging their AI use cases and working under government-wide direction on how AI should be managed and disclosed. State agencies are writing their own rules, often driven by new legislation and their own IT security programs. Local governments—counties, cities, school districts—face the same expectations with smaller teams, tighter budgets, and services that residents interact with directly. 

But beyond those differences, every agency runs on the same three things: agreements, approvals, and a record of who authorized what. That’s where trusted AI has to start—and, as it turns out, the government already has a well-worn model for handling this kind of scenario.

Delegated authority is something the government already understands

The government has always run on delegated authority. A warranted contracting officer can commit funds up to a set limit. An approving official signs off within a defined lane. A program manager can obligate a grant only under specific conditions. Everyone acts within the predetermined, contractual boundaries. 

An AI agent is no different. If it’s going to take action in a workflow, it needs to operate inside a clear delegation, and the agency needs to be able to prove that it operated within those boundaries. What was this system allowed to do? Did it stay within that limit? When did a person step in to review or approve? Framing AI this way is not a new burden. It is the same discipline agencies already apply to their own people, extended to a new kind of actor.

Policies alone are no longer enough

Most agencies are already developing AI policies and governance frameworks to establish where AI can and cannot be used. But a policy only describes what’s supposed to happen. It does not, by itself, show what actually happened.

Organizations will also need reliable evidence of what authority was delegated, which information an AI system relied on, which actions occurred throughout the workflow, and when human review or approval occurred. That’s the difference between saying you follow the rules and being able to prove it.

In government, that difference is not abstract. Agencies get audited by Inspectors General and the GAO. They answer public records and Freedom of Information Act requests. They keep records in accordance with retention schedules set by the National or State archives. And they’ll be asked to provide a clear, verifiable audit trail. 

Trusted agreement data becomes the foundation

As organizations prepare for more autonomous AI, the quality and accessibility of agreement data become increasingly important.

Agreements stored across email inboxes, shared drives, paper files, and disconnected systems make transparency, oversight, and accountability far more difficult. Before organizations can establish trusted evidence for AI-driven actions, they first need trusted agreement data.

A centralized, AI-powered agreement repository turns static documents into structured, searchable information that’s easier to manage, audit, and analyze. This creates a stronger foundation for future AI-enabled agreement workflows while preserving the visibility and governance agencies require.

This is where Docusign Intelligent Agreement Management (IAM) can help. Instead of leaving agreements spread across inboxes, shared drives, and filing cabinets, IAM brings them into one place and uses AI to turn them into structured, searchable data that an agency can govern. Then an agency can see what an agreement says, who approved it, and when—and provide it to an auditor, an Inspector General, or in response to a public records request. Getting your agreement data in order is the practical first step before layering AI on top.

Human judgment remains essential

For high-value, high-risk commitments, AI should augment human decision-making rather than replace it. AI can prepare documents, analyze agreements, validate information, identify risks, and recommend next steps. Final approvals, signatures, authorizations, and other legally binding commitments should remain the responsibility of authorized government personnel.

This approach allows agencies to benefit from greater efficiency while preserving the oversight and accountability that public service demands.

Building trust for what comes next

Agentic AI represents an important evolution in how organizations manage agreements and workflows. As these technologies quickly mature, agencies that establish trusted agreement processes, centralized agreement data, and transparent governance will be better positioned to adopt AI with confidence.

The future of agentic AI will depend not only on advances in the technology itself, but also on the trust organizations build around it. Secure agreement workflows, verifiable records, and clear accountability will help ensure AI supports government operations effectively and responsibly.

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