What is Agentic OSINT?

First there was manual OSINT, then there was automated OSINT, then there was Narrative Intelligence, now comes Agentic OSINT.

To understand what Agentic OSINT is, let’s quickly cover what each generation of OSINT (Open-Source Intelligence) activity does. The above categories are not four distinct groups as much as they are a linear progression of intelligence software.

The four modes of OSINT systems

Manual OSINT is the traditional approach. A human analyst searches, cross-references, and connects the dots across public sources. It's precise and context-rich, but slow and hard to scale.

Automatic OSINT uses scripts and tools to crawl, scrape, and correlate data across thousands of sources in minutes. It's fast and repeatable but rigid. You define the steps upfront, and the tool executes them blindly.

Narrative Intelligence technology moved away from looking at meta-data elements (keywords, followers, etc.) to determine relevance. Instead, it looks at the full content of a post or article, understands its context, and through that determines relevance. It’s an approach that really took off with the advent of Generative AI and LLMs, tough it's a less agentic approach.

Agentic OSINT is the emerging mode. An AI agent doesn't execute a fixed script. It pursues a goal, reasoning about what to do next based on what it's already learned. The agent adapts. It is autonomous. It follows leads, drops dead ends, and decides when it knows enough, like a human analyst would, but at machine speed.

What’s the difference between generative AI and agentic AI?

Understanding the fundamental difference between the two (let’s admit it, buzzwords) makes their application to the OSINT use case clearer.

Most broadly, generative AI is about generating insight, while agentic AI is about taking action.

Traditional AI offered users powerful ways to recognize patterns and analyze data. Generative AI goes a step further: it can create new patterns and content including text, images, video, audio, or even software code. It does this by identifying and encoding patterns and relationships across vast amounts of data, allowing models to understand natural language requests and generate high-quality content in real time based on the information they were trained on.

Agentic AI, by contrast, focuses on making decisions and taking actions rather than creating new content. Instead of relying solely on human prompts or constant oversight, agentic systems can independently pursue goals, decide what steps to take next, and adapt based on what they learn along the way.

To ensure an AI agent can reliably plan, access tools, and execute tasks in the real world, it needs a structured environment that manages how it operates. This control layer (called an Agent Harness) connects the agent to data sources, tools, and rules so it can safely and effectively pursue its goals.

What does Agentic OSINT do?

Now that we’ve established the proactive nature of agentic AI, let’s look at how it applies to OSINT.

Agentic AI can function as a kind of OSINT analyst you can interact with in natural language, asking it to decide tactics based on a new use case. For example, you might ask something like: “The opening ceremony of the Olympics will take place in our city next month. Which assets should I track? What narratives should I monitor? Please create an initial monitoring report.”The system can interpret the goal, determine what sources and signals are relevant, and begin building a structured intelligence picture.

In fact, the agent does not even need to be prompted by a specific event. A defining characteristic of agentic AI is autonomy. An agentic OSINT system should be given a goal, then independently form a plan, gather intel, adapt its approach based on new inputs, produce reports, and trigger actions.

For example, it can be tasked with protecting the interests of a country, organization, or mission, and continuously monitor the information environment on its own. When it detects a developing event or emerging narrative, it automatically identifies relevant assets to track, maps the narratives forming around the issue, and generates the reports needed to brief decision-makers.

The idea is not to replace the OSINT analyst, but to give them a team to work with, dramatically increasing the amount of insight they can produce and the speed at which they can produce it.

Another example can be found in mitigation strategies. An agentic OSINT system can review successful mitigation techniques and adapt them to a new scenario. It might generate dozens of possible approaches, evaluate them, narrow them down to the most promising ones, and ultimately present a small set of actionable plans, some to be executed automatically, some for the analyst to execute. In this way, the human analyst remains in control, while the system accelerates exploration, analysis, and decision-making.

Brinker Agentic OSINT

Brinker is an AI-first company. AI didn’t happen to us. We were built around it. Because of that, our processes are naturally integrated with the way agentic AI systems work. Our AI agent, located in the top-right corner of the platform, is called Hans (modeled after Hans Brinker, whose story inspired the company’s name. Google it :).

Hans is built on top of Brinker’s Narrative Intelligence technology and our vast network of data sources. The result is one of the most innovative OSINT tools available today, combining deep narrative analysis with agentic capabilities that help analysts explore, monitor, and respond to complex information environments faster and more effectively.

Welcome to the age of agentic OSINT