Can AI Do Intelligence Analysis? Apparently Not.

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Can AI Do Intelligence Analysis? Apparently Not.<br>I built my own intel analyst team using AI agents as a weekend project

Robin Dimyanoglu<br>Jun 03, 2026

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Structured Analytical Techniques (SAT) are a set of methods used in intelligence analysis to minimize cognitive shortcuts and biases. When working on cases like attribution analysis, geopolitical CTI or forecasting, applying these is a must for accurate analysis.<br>But as is also well known, applying these techniques requires people who are trained in SAT and who work through these methods as a group. Meaning analysis done this way takes a very long time and is costly. In the private sector, there is rarely enough time to devote to this kind of structured analysis.<br>Thanks for reading Predictive Defense Blog! Subscribe for free to receive new posts and support my work.

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To solve this problem, I wondered: could I build an analyst team made up of AI agents? If it worked, I would have an automation that could apply these time consuming, costly structured techniques on its own and produce analysis rather quickly. This post is about the path I took, the things I tried and my thoughts on why it didn’t work.<br>The architecture

The idea was a pipeline of specialized agents, each owning a distinct role in the intelligence cycle. From intake all the way to finished reporting. Here's what I designed:<br>01 - Stakeholder interviewer<br>The entry point. This agent chats with the customer to capture the intelligence requirement in an unstructured, conversational format. It asks follow-up questions to understand not just what the customer wants to know, but why they need it and what decisions the intelligence will inform. From that conversation, it distills a primary intelligence question and breaks it down into a set of focused sub-questions that can actually drive collection.

02 - Collection planner<br>Has access to a curated database of trusted information sources, each carrying an assigned trust score that reflects its reliability and accuracy. This agent takes the sub-questions from the interviewer, figures out what kind of data is needed to answer each one and maps them to the best-fit sources available.<br>It deliberately avoids over-relying on any single source. Diversity of sourcing is a hard requirement. The trust scores it assigns here follow the information all the way through to the final analysis.<br>Additionally, the collection planner does not pass the intelligence question itself to the collector. It only specifies what to look for and where. The collector must not know what conclusion the analysis is working toward to avoid confirmation bias.

03 - Intelligence collector<br>Executes the collection plan by looking up the specified sources and retrieving anything potentially relevant. Critically, this agent does not interpret or summarize what it finds. It surfaces information verbatim, exactly as it appears in the source. Its job is to bring raw material to the table, not to answer the question. When the plan's sources come up empty, it can fall back to open-source internet search, but must explicitly flag those results as coming from an unverified source.

04 - Analysis coordinator<br>The orchestrator of the analytical layer. This agent looks at the intelligence question and the collected material, then decides which analytical techniques are most appropriate for the problem at hand. Each technique is its own independent agent. Think of them as individual analysts with different methodological specializations. The coordinator selects which ones to engage and manages the flow of information between them.

05 - Analyst agents<br>A multi-agent layer that runs the actual structured analysis. Rather than a single "analyze this" prompt, these agents work through a deliberate reasoning loop. Scenario generation, refutation, evidence weighting, assumption checking. This process mirrors how a real SAT session would operate. They also communicate with each other and can loop back to collection when they identify gaps. More on this below.

06 - Reporter<br>Takes the analytical output and translates it into something a real stakeholder can act on. This agent looks at the use-case established in the intake stage, builds a mental model of the audience, and structures the report accordingly. The governing principle is BLUF (Bottom Line Up Front). The key assessment and its implications should be readable in under 30 seconds, without having to wade through caveats and methodology. Alternative scenarios are included, but the report takes a clear position and explains why.

The analysis layer

The analyst layer deserves a closer look. I wanted the agents to run through a structured reasoning loop, one that encodes the actual analytical steps that make SAT valuable in the first place:<br>Scenario generation<br>Start by generating a broad list of possible scenarios using a creative SAT....