Artificial Intelligence (AI) has evolved rapidly in recent years, moving beyond simple buzzwords to become a transformative force. AI technology has progressed from basic text generation to complex systems capable of planning, acting, and reasoning. This evolution is marked by distinct stages, each introducing new levels of intelligence and autonomy with significant operational relevance for national security.
For many professionals in the national security space, the pace of change can be difficult to follow. Questions like, “What is an ‘AI agent?’,” “How is that different from ChatGPT?,” and “What exactly is ‘agentic AI?’” are common. The following step-by-step examples, grounded in national security, clarify this evolution, showing not just what is possible, but what is already happening.
The mainstream breakthrough for modern AI came in late 2022 with the advent of Large Language Models (LLMs) like ChatGPT. This initial stage can be thought of as using AI to draft a CONOPS, threat summary, or executive briefing. These models demonstrated a remarkable fluency in writing, summarizing, and explaining complex topics based on a clear prompt.
However, LLMs are limited despite their power. They do not take action, access internal systems, or know anything beyond the data they were trained on.
The next stage involves making AI operational by giving it access to timely, relevant data. RAG systems function like a smart assistant that can read intelligence reports, travel logs, or collection briefs before formulating a response. This is achieved by enabling LLMs to access live documents, databases, and internal reports, ensuring answers are based on real-world data, not just generalized training information.

AI Agents represent the shift from just answering questions to actively performing tasks. These agents pair LLMs with toolkits such as APIs, dashboards, and scripting environments, allowing them to take action, complete steps, and prepare outputs, often without additional user input.
Agentic AI introduces a collaborative framework where systems understand intent, create plans, and coordinate tasks among other agents or tools to achieve a mission outcome. Instead of just following instructions, these systems can decompose a high-level goal into sub-tasks and adapt dynamically to changing conditions.
This stage involves multi-agent systems that persist across tasks, build a shared memory, and improve over time. These ecosystems can learn user preferences, mission priorities, and operational context across different domains, leading to scaled and optimized intelligence operations.

The current frontier is a phase where decentralized networks of AI agents collaborate, improve themselves, and manage mission workflows at operational tempo. These agents can collectively manage surveillance of key regions, adjust priorities in real-time, and support command-level decision-making. The agents function as a seamless, fully autonomous team.

As AI becomes more autonomous and capable of setting goals, orchestrating sensors, and delivering intelligence, the responsibility of its human operators grows with it. Agentic AI is not just about automation; it is about intention. These systems will adapt and collaborate to pursue mission outcomes with increasing autonomy, but humans will always remain in the loop to set objectives, guide actions, and make the final decisions. Governance, transparency, and ethical design are therefore essential to ensure these powerful systems serve national security objectives responsibly.
At Riverside Research, we are using internal R&D to develop agentic AI systems that are scalable, secure, and aligned with operational requirements. The objective is to provide capabilities that support the future warfighter by improving decision speed, situational awareness, and adaptability across all domains. The future of intelligence is not about more data or more tools; it’s about better orchestration, grounded in trust, accountability, and purpose.
Ready to learn more about Riverside Research’s agentic AI solutions? Contact us today.
Matthew May is the Director of Cognitive Intelligence Solutions at Riverside Research, leading advanced research and development in Artificial Intelligence, Multi-INT fusion, and autonomous systems for national security applications. He oversees initiatives spanning Agentic AI applied to the Intelligence Cycle, Modular Autonomous Payloads (MAP), and Dynamic Intelligence Orchestration (DIO), driving innovation across multi-domain data environments for DoD and Intelligence Community customers.
With a background that bridges defense innovation and applied technology, Matthew has led programs supporting multiple intelligence agencies—delivering prototypes that integrate AI agents, sensor data, and real-time situational awareness to accelerate decision-making at the tactical edge.
Prior to Riverside Research, Matthew founded and led multiple technology startups focused on open-source intelligence and analytical automation, including Applied Technology Solutions (ATS), whose software has been leveraged by government and non-profit organizations to combat human trafficking and transnational threats.
His work focuses on fusing human and machine intelligence to advance the future of autonomous decision support and Intelligence-as-a-Service.
LinkedIN
The above listed authors are current or former employees of Riverside Research. Authors affiliated with other institutions are listed on the full paper. It is the responsibility of the author to list material disclosures in each paper, where applicable – they are not listed here. This academic papers directory is published in accordance with federal guidance to make public and available academic research funded by the federal government.