By Tony Ibáñez, Senior Solutions Architect, Ocient National Security Solutions
At AFCEA TechNet Cyber 2026, I explored a challenge that many organizations have yet to fully appreciate: while agentic AI promises to accelerate analysis and decision-making, it also creates a hidden strain on the infrastructure that supports it. You can watch my entire talk below but I also wanted to share some insights here on the blog about a topic at the center of how the Ocient National Security Solutions team is building OcientAIQ™ to solve our customers most pressing challenges.
For years, multi-source data fusion has operated at human speed. Analysts formulate questions, issue queries, evaluate results, and decide when they have enough information to move forward. This process naturally limits demand on underlying systems. Agentic AI changes that equation.
Unlike human analysts, autonomous agents do not operate sequentially. They decompose objectives into multiple sub-questions, explore numerous hypotheses simultaneously, and continuously iterate based on new findings. The result is a dramatic increase in activity occurring beneath the surface.
The Hidden Cost of Autonomous Fusion
One of the key observations discussed during the presentation is that a single agent-driven analytical task can generate significantly more backend activity than a traditional analyst workflow.
When an agent receives a mission, it does not simply issue one query and wait for a response. Instead, it breaks the problem into smaller components, launches parallel lines of inquiry, validates assumptions, and refines its approach repeatedly until it reaches a satisfactory conclusion.
This behavior is not a flaw. In fact, it is often what makes agentic systems effective.
The challenge is that every one of those actions creates workload on the data layer.
Organizations focused on deploying AI capabilities frequently concentrate on models, orchestration frameworks, governance, and user interfaces. While these elements are important, they do not eliminate the underlying infrastructure demands created by autonomous systems. In many cases, they can unintentionally increase them.
Why Traditional Approaches Aren’t Enough
Many organizations are investing in semantic layers, governance frameworks, and specialized analytics engines to support AI initiatives.
These investments improve trust, consistency, and control. Semantic layers help agents reason about data more effectively. Governance ensures policies and access controls remain intact. Specialized engines can optimize individual workloads.
However, none of these approaches fundamentally reduces the number of questions an autonomous agent asks.
As agent adoption grows, organizations may discover that the primary challenge is no longer query correctness but query volume. The question becomes whether the underlying analytical infrastructure was designed to accommodate that reality.
Fragmentation Creates Friction—and Cost
The challenge becomes even more pronounced when analytical environments are fragmented across multiple systems.
Many organizations maintain separate platforms for historical analysis, streaming data, geospatial workloads, graph analytics, and machine learning. Each system introduces extra orchestration, movement, and coordination requirements.
For human analysts, this complexity may be manageable. For autonomous agents executing hundreds or thousands of operations, each system crossing introduces overhead that compounds over time.
What appears to be a single analytical question at the user level may trigger a chain of interactions across multiple platforms. An agent may need to retrieve data from one system, summarize it, pass that context to another system, combine results, and then repeat the process as it explores new hypotheses.
This creates more than latency; it creates cost.
Every time an agent carries information from one system to another, it consumes context window space and generates greater token usage. As the number of systems increases, so does the amount of data that must be repeatedly moved, summarized, and reintroduced into the workflow. What begins as a simple analytical task can quickly become more expensive—not because the question is difficult, but because the environment is fragmented.
In this way, fragmentation becomes a recurring tax on both performance and efficiency. The challenge is not simply that separate systems are slower. Every system boundary increases the amount of orchestration, context sharing, and processing required for an agent to arrive at an answer.
A more consolidated analytical environment reduces those crossings, allowing agents to spend more time generating insights and less time stitching together information from disconnected platforms.
Building Infrastructure for Agentic Operations
The goal is not to slow agents down.
The goal is to build analytical environments that anticipate how agents work.
Organizations preparing for the next generation of AI-enabled operations should consider several principles:
- Make system costs and resource contention visible.
- Consolidate analytical capabilities where practical.
- Apply governance at machine speed, not just human speed.
- Treat agent workloads as first-class production workloads.
These considerations become increasingly important as autonomous systems move from experimentation into operational environments.
Preparing for What’s Next
National security organizations face growing pressure to transform massive volumes of data into actionable intelligence faster than ever before. Agentic AI offers tremendous promise in helping analysts and operators meet that challenge.
At the same time, organizations must recognize that autonomous systems fundamentally change the demands placed on their data infrastructure.
The conversation is no longer only about building smarter agents. It is also about reducing the friction, complexity, and cost that agents encounter as they move across analytical environments. Organizations that can provide agents with a unified, governable analytical foundation will be better positioned to operate efficiently at mission scale.
As agentic AI adoption accelerates, the organizations best positioned for success will be those that prepare both the intelligence layer and the data layer to operate together.
To learn more about Ocient National Security Solutions and the OcientAIQ™ platform, visit ocient.com/solutions/national-security or contact our team to discuss your mission data challenges.