How agentic systems are reshaping government contracting

We've deployed enterprise artificial intelligence models for years, including in regulated, high-security environments. They help analysts search faster and even recommend the next step. These tools feel fundamentally safe because they stop and wait for a human to approve every action.

Agentic AI is coming, and it doesn’t wait. It plans and acts across multiple systems without pausing for permission. In government contracting, where process is policy, we cannot overlook that difference.

Recent research indicates that 53% of federal agencies are exploring or actively planning agentic pilots, and 15% are already using agentic systems. In several commercial sectors, it’s safe to say that we’re already past the early-adopter phase.

Unfortunately, the vast majority of companies aren’t ready with the strong foundation of data and governance that makes these systems safe and reliable.

For government agencies, preparing that foundation isn’t optional.

Why the public sector needs to prepare for agentic systems

Models we’re familiar with recommend and wait. But what do we do with the new models that will analyze and act on their own?

That single difference changes a lot. In a contracting office, approval chains and clearances were designed on the assumption that a person would execute the steps. Separation of duties and audit trails both depend on that person being accountable. But when an agent drafts acquisition packages or routes workflow tasks, the old assumption no longer holds.

If something goes wrong, blaming the model isn’t an acceptable response. Agencies and contractors need to know who is accountable before deployment. This requires a plan, and the leaders who wait for a mandate will already be behind.

The issues that come with deploying agentic systems at scale

People tend to focus on the potential engineering problems when they first see an agent at work. Ironically, hurdles such as orchestration frameworks and system integrations tend to be the easier issues to solve.

In government contracting environments, the harder problem is proving the system is trustworthy enough to operate. This is a space where the stakes and the scrutiny are both equally high.

The environment presents a number of stumbling blocks for an agentic model. Classified and air-gapped environments don’t play nicely with agents that need to reach across systems to do their job, so the architecture has to be rethought carefully. Autonomy can’t be allowed to build haphazard bridges across segmentation boundaries.

Every action the agent takes must be logged and attributable to satisfy FedRAMP-level scrutiny standards. If the agent queries a dataset or routes a document, there needs to be a record showing what happened and why. Just as importantly, agencies and contractors need governance that can prove chain of custody for sensitive information at every hop.

Implementing disciplined traceability helps log every prompt and handoff. But government environments will demand a high bar, which is why I say that proving the system is trustworthy enough for this environment is the harder problem.

How agency leaders and government contractors can build guardrails to allow both autonomy and security

The biggest mistake I see agencies make when deploying systems is treating autonomy like an on-or-off switch. It should be treated more like a dial.

While many companies start by granting broad autonomy upfront, the better approach is to start narrow and earn more autonomy over time. Give agents a tightly scoped set of allowable actions and set confidence thresholds that force escalation to a person when the system isn’t sure. Sandbox agent behavior before anything touches a live system.

As agents handle routine cases end-to-end, they kick anything below a confidence threshold to a person and leave a trail so everything can be explained. In government contracting, those thresholds should generally be tighter and the review gates more conservative, but the underlying model is the same.

The earlier you build those guardrails into the system, the better. They aren’t there to slow your system down. They’re there to build the evidence that lets you trust it more. When leaders treat guardrails as a way to accumulate evidence of repeatability and safety, they create a pathway to responsibly increase autonomy over time.

What government agencies and government contractors should do now to prepare

The compliance questions will change once agents start executing multi-step workflows. Traditional contracts and oversight structures were built on the assumption that a person does the work and is accountable for it.

When an agent executes a process across systems, agencies will need new ways to demonstrate compliance. They will need to verify both the final output and validate how it was produced.

Your preparation for agentic AI begins with the foundation. Get the data layer, identity systems, and access governance in order before you even think of touching agent orchestration. That foundation work is necessary in government contracting.

From there, run pilots in genuinely low-risk workflows. You can use those early deployments to build the traceability infrastructure you need while the stakes are still small. It’s far cheaper to build oversight into a system early than to retrofit it after something goes wrong.

My advice is to begin preparation now. Don’t wait until a policy mandate forces your hand. The agencies and contracting organizations that start laying this groundwork will be far ahead of the rest a few years from now, but not because they will be the first to deploy agents. It will be because they built the accountability and control systems that make autonomy safe enough to scale.

 Atul Arya is an entrepreneur and business leader with a strong focus on technology, product development, and building high-performing teams. As the founder and CEO of Blackstraw AI, he brings a passion for transforming innovative ideas into successful products, solutions and businesses. Atul’s leadership combines strategic business vision with hands-on technology and product expertise, enabling teams to move rapidly from AI pilot to production while delivering measurable business value.