From Computer Vision to Autonomous Agency

For years, the application of AI in defense has largely centered around computer vision – automating tasks like identifying military hardware in photographs. While this technology has undeniably streamlined processes and reduced analyst workload, it fails to address the core challenge of information overload. The sheer volume of data generated by modern sensors demands a more sophisticated solution.

Agentic AI represents a generational leap forward. These systems aren’t simply processing data; they’re understanding intent, formulating plans, and executing actions across multiple platforms without human intervention. Agentic AI transforms raw data into actionable intelligence, dramatically amplifying the effectiveness of defense personnel. Instead of sifting through mountains of irrelevant information, analysts are presented with distilled insights, enabling faster and more informed decision-making.

What Does Agentic AI Actually *Do*?

At its core, Agentic AI is defined by its ability to:

1. Analyze a commander’s overarching objectives.
2. Decompose those objectives into a series of manageable tasks.
3. Independently execute those tasks across various systems and platforms.

This capability isn’t merely about speed; it’s about unlocking new levels of strategic and tactical flexibility. But this progress raises critical questions. What role will human analysts play in a world where AI handles much of the initial processing and decision-making? That’s a question for another day. Today, we’ll focus on the implications for procurement, investment, and policy.

For the Procurement Officer: Transparency and Adaptability

Procurement officers must exercise extreme caution when evaluating “black box” AI systems. If a vendor claims their agent utilizes “proprietary reasoning” that is difficult to audit, it’s a red flag. In the event of an incident, simply stating “the algorithm made a choice” will not suffice as a legal defense. The Pentagon must prioritize “Chain of Preference Transparency,” demanding that software logs its decision-making process, allowing for continuous refinement and accountability.

Furthermore, the traditional Firm-Fixed-Price contract model is ill-suited for Agentic AI. These systems require Continuous Authority to Operate to remain effective, especially in a dynamic threat environment. A static, one-time purchase will quickly become obsolete. Procurement officers should focus on acquiring a continuous pipeline of updates and improvements, not just a single software package.

The Investment Thesis: Beyond the Hype

The challenge for investors lies in differentiating between superficial AI applications and truly foundational defense operating systems. Many investment firms rely on retired military officers to assess defense technologies. However, these officers may lack firsthand experience with the latest advancements, leading to misdirected investments. It’s unfair to expect someone who left active duty years ago to accurately evaluate cutting-edge technology that even current practitioners are still mastering.

Proximity to end-users is key. Investors should actively engage with those who will actually utilize these systems – through small-scale exercises, trade shows, and demonstrations. Don’t focus solely on the Large Language Model; the true value lies in the “Action Layer” – the ability to integrate with real-world data sources. Look for startups building secure, high-side integrations that can connect to actual defense networks.

The “Defense Unicorn” of the future won’t be a company building new hardware; it will be the one providing the intelligent brain to revitalize existing systems. Collaborative development will be crucial to fostering innovation and preventing stagnation.

The Policy Wonk’s Warning: Avoiding the “Speed of Relevance” Trap

Early concerns about AI in defense centered on its potential impact on strategic stability. Now, with the advent of Agentic AI, a new danger emerges: the “Speed of Relevance” trap. If both the U.S. and its adversaries deploy Agentic systems to manage strategic command and control, or even frontline skirmishes, the window for diplomatic de-escalation could shrink to milliseconds, effectively eliminating human intervention and escalating conflicts beyond control.

To mitigate this risk, a fundamental shift in foreign policy is needed – a move from Arms Control to Algorithm Control. The next major treaty should focus not on the number of warheads, but on the verification of “Human-on-the-Loop” safeguards and the establishment of universal standards for AI behavior.

What safeguards can be implemented to ensure human oversight remains a critical component of autonomous defense systems? And how can we foster international cooperation to prevent a dangerous arms race in algorithmic warfare?

Frequently Asked Questions About Agentic AI

• What is Agentic AI and how does it differ from traditional AI?

  Agentic AI goes beyond simply analyzing data; it’s capable of understanding intent, formulating plans, and executing actions independently, unlike traditional AI which typically requires human prompting for each step.

• What are the key considerations for procurement officers when evaluating Agentic AI systems?

  Procurement officers should prioritize transparency, demanding clear explanations of the AI’s decision-making process and avoiding “black box” systems with proprietary reasoning that cannot be audited.

• How can investors identify promising Agentic AI startups?

  Investors should focus on companies building secure integrations with real-world data sources (“high-side” integrations) and avoid those solely focused on the Large Language Model itself.

• What are the potential risks associated with the widespread deployment of Agentic AI in defense?

  A key risk is the “Speed of Relevance” trap, where AI-on-AI interactions escalate conflicts beyond human control, highlighting the need for “Human-on-the-Loop” safeguards.

• What policy changes are needed to address the challenges posed by Agentic AI?

  A shift from Arms Control to Algorithm Control is necessary, focusing on verifying human oversight and establishing universal standards for AI behavior in defense applications.