Know, control, prove: Building trust in agentic AI
How visibility, calibrated control and verifiable evidence can govern autonomous AI in mission environments
Why it matters
- Autonomous AI can act faster than humans can intervene
- Mission environments demand visibility, control and verifiable accountability
- Calibrated oversight preserves human authority over consequential decisions
A few years ago, I built an agent to route expense approvals. The agent misapplied a rule and nearly auto approved a misfiled purchase.
Thankfully, we caught it in time. Nothing dramatic happened. Nobody got fired. The model had behaved exactly as designed.
But the experience exposed something important: Somebody has to watch the work – trust must be earned, not given.
Fast forward to today, and the same fundamental challenge is playing out in defense and intelligence environments—only the consequences are far greater than a misfiled expense report.
What agentic AI changes in mission environments
Agentic AI, systems that can act with increasing autonomy, is arriving in mission environments faster than many governance frameworks can keep pace. Its prevalence isn’t a mystery, since it offers agents that can hunt for threats across garrisons and forward-deployed networks at machine speed, taking on work that could otherwise consume hours of an analyst’s time.
But that speed also raises the stakes.
Why speed increases the need for accountability
The faster agents can act, the more important it becomes to know what they’re doing, control what they’re allowed to do, and prove why an action was taken.
Speed without accountability can quickly become a liability.
Know. Control. Prove.
Know, control, prove are three powerful words and conditions an agent must meet before it earns the right to act. But to get there, you need an underlying model built on calibrated trust where agents are governed with visibility, limits, and evidence built in from the start.
Here’s what that looks like in practice:
Know
The platform can see every agent operating in its environment: Who built it, what it’s allowed to touch, and the autonomy it earned before it ever takes an action. Identity systems alone do not cover this ground. They confirm who an agent claims to be, but they don’t say what it’s authorized to do. Closing that gap is the foundation everything else depends on.
Control
This is where most governance conversations stop short. Not every agent decision deserves the same scrutiny. A calibrated trust model scores each moment, weighing task familiarity, outcome safety, and reversibility against a threshold. Low-risk, reversible actions execute on their own. High-consequence, hard-to-reverse actions pause for a human.
That is the difference between human-on-the-loop and human-in-the-loop governance.
Human-in-the-loop asks a person to sign off on every decision, which sounds safe until the approval queue becomes a bottleneck. Human-on-the-loop monitors continuously and escalates only the actions that genuinely warrant a second look. For high-volume environments, that approach can scale oversight without turning human review into a bottleneck.
But neither model is sufficient on its own. Architectural limits must sit underneath both, constraining agent behavior before a human is ever asked to weigh in.
Prove
Close the loop. Every decision, human and machine, leaves a hash-chained, tamper-evident record mapped to NIST compliance families. That record is the difference between a receipt and a promise, and it gives an auditor or authorizing official something they can independently verify.
Why agentic AI governance cannot wait
Given its ubiquity, power, and potential, organizations should have a vested interest in ensuring agentic AI works. But the promise is not without peril. In a June 2025 forecast, Gartner projected that over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value or inadequate risk controls.
The technology tends to work well enough in the demo. But the absence of a governor layer is what can quickly break projects.
What trusted agentic AI means for mission leaders
For the analyst, this means speed without second-guessing every output. For the commander, it means authority stays intact even as machines act faster than any person can track in real time. For the authorizing official, it means evidence instead of assurances.
The organizations that get this right will not be the ones that adopted AI agents first. They will be the ones that can prove, months later, exactly why every action taken was justified.
Key takeaways
- Agent identities must establish both who they are and what they are authorized to do
- Oversight should reflect an action’s risk, reversibility and potential consequences
- Leidos applies visibility, calibrated controls and verifiable evidence to strengthen trust in agentic AI
