# Speed Isn’t Enough: How to Generate Defensible Investigative Insight > How agentic AI, investigative tradecraft, and explainable intelligence help organizations move beyond data collection to defensible investigative insight. **Published:** Jul 15, 2026 **Topics:** Agentic Risk Intelligence --- For ages, the intelligence community spent untold hours collecting data it hoped would lead to investigative insight. Investigators read newspapers published around the world. They listened to foreign radio broadcasts. They pored over paper records. They took photos of surveillance subjects and analyzed aerial imagery. Sometimes they found the information they needed. Often, they didn’t. The digital age created a data gold mine. More data was being produced than ever before, with the potential to provide more and deeper insight than previously imagined. And all this data became more easily accessible. Documents formerly stored on paper in an array of government offices, newsrooms, university libraries, and other venues became searchable at any time, from anywhere. Quickly, though, the amount of online data grew too vast for then-current methods of collection and analysis. Open-source intelligence and early-generation AI technologies arose to search and analyze publicly available information (PAI) at machine speed. It hasn’t been enough. Data abundance has shifted investigative challenges. The problem is no longer availability_._ It’s investigative _ability_ to efficiently search enormous amounts of data, collect relevant information, connect disparate data points, and feel confident in the insight returned. Reactive AI — the type of AI used in many existing governmental and enterprise investigative systems — can’t effectively do this. Reliant on a series of Boolean searches, these systems require human intervention at every step. Analysts must pose question after question to obtain insight. Even agentic AI can fall short without the right training. Agentic AI is artificial intelligence capable of undertaking complex, multi-step tasks without the need for consistent human involvement. But without specific training in investigative tradecraft, these AI agents cannot determine which information counts as investigative evidence. They don’t know the importance of finding disconfirming evidence, or of evidence chains. They are ignorant of investigative workflows. They simply don’t think like human investigators. ## What is Agentic Risk Intelligence? Agentic Risk Intelligence combines AI agents, investigative tradecraft, and trusted data sources to automate complex investigations while delivering transparent, defensible intelligence. The solution? Tradecraft-infused [agentic AI risk intelligence platforms](https://www.babelstreet.com/insights-investigator-agentic-ai-risk-intelligence) built on world-class data. These platforms prioritize the type of accurate insight, transparent reasoning, and citable sources that lead to defensible conclusions. ## The current investigative environment Currently, public and corporate investigations are stymied by at least four impediments. These are: - Over reliance on target-first or hypothesis-driven search processes - Conclusions based on suboptimal data - Black-box AI processes - Metrics that value speed-to-insight over defensibility of conclusions reached Let’s take a closer look at each. Outdated OSINT and AI technologies can only accommodate (imperfectly) **target-first or hypothesis-driven searches**. Terrorist attacks, phishing attacks, drug smuggling, human trafficking, and similar crimes and threats are already on investigative radars. But if an investigator cannot envision a situation, she cannot glean insight into it. And this is a problem. Imagine the following scenario. You’re planning a summer getaway. You’ve visited a short-term rental site and found a beachfront cottage you like. To research the place, you follow target-driven or hypothesis-driven methodologies. Through these practices, you find the cottage was initially listed during the spring, so it only has a handful of reviews — all excellent. You access GPS systems to find that the cottage really does sit on the beach, not six streets back. You research the owner: no complaints, criminal or otherwise. You find no negative reports emanating from the addresses on either side of the cottage. The home’s certificate of occupancy is valid. Historical weather patterns point to bright skies during this particular week in July. You rent the cottage and have a miserable time. Why? The owners of a home two streets away are known for their nightly summer parties: lots of people, lots of drinking, techno music blaring from industrial speakers. The previous season, town residents lodged more than 200 noise complaints against this neighbor. Because of this household’s behavior, the town council is now considering a new ordinance that provides harsher penalties for noise scofflaws. You never thought to check for this. Most people would not. But, when instructed on the elite tradecraft of expert investigators, agentic AI platforms can help broaden investigative paths for a more expansive view of possible threats. In essence asking an analyst “Have you considered …?” agentic AI platforms help expand certain areas of investigation while quickly shutting down others as erroneous. A simple query to an agentic AI platform — _Tell me about any issues in this home and the surrounding neighborhood that might negatively impact a visit planned for July 9th-July 16th by two adults, a 10-year-old, and an infant_ — could have uncovered the party atmosphere threatening your vacation. And, because agentic AI platforms can perform multi-step tasks, it would have done so without the query-by-query human involvement required by AI systems now commonly used. You can see how existing AI systems too often provide subpar insight. The situation is exacerbated by their tendency to draw on **suboptimal data**. As noted earlier, investigators have easier access to more data than ever before. But that data is typically disorganized, chaotic, and decontextualized. It appears in innumerable sources and hundreds of languages, with no connection among data points. (Social media platforms don’t magically connect to shipping information; commercially available datasets don’t link to dark web message boards.) [Open-source intelligence](https://www.babelstreet.com/osint-and-threat-intelligence-solutions) and early-generation AI systems may also cull insight from incomplete, unverified, or uncredible sources. Insight based on this data is inevitably incomplete or incorrect. Worse, investigators may never know how bad the insight is. Why? **“black box” processes** leave investigators unable to see how AI systems have made their decisions, and on which data their conclusions are based. This is a significant drawback because AI systems can hallucinate: finding linkages when there aren’t any. Here’s an example. An investigator charged with helping to secure a large concert is looking for anything indicative of planned domestic terrorism. The investigator’s AI system finds three different social media posts from people planning to attend the show: “This show is gonna bomb,” “This show is gonna be the bomb,” and “I can’t wait to watch the group bomb around the stage.” Although these are all benign sentiments, the use of the word “bomb” may trigger investigative AI systems. The AI system may be further concerned when it learns that all three fans have criminal convictions. Human investigators understand nuance. Based on slang use of the word “bomb” and previous histories of DWI, shoplifting, and possession of marijuana, they would be extraordinarily unlikely to conclude that these three fans are conspirators planning to bomb the arena. A reactive AI system, meanwhile, might jump to that conclusion. This happens, in part, because AI systems often suffer from “sycophancy,” or the tendency to tell investigators what they want to hear. The investigator in this scenario wanted to find any evidence of planned domestic terrorism. The AI system may confidently but erroneously proclaim, “I’ve found it.” No one wants to dismiss the importance of **speed**: time to insight is often critical in emerging situations. But increasingly, investigators find they’re quickly obtaining bad insight. Speed — along with volume of data collected, number of sources searched, and other metrics used by outdated OSINT and AI systems — counts for little if investigative technologies make erroneous decisions. Target-first searches, suboptimal data, and black-box processes don’t provide fast investigative insight. They serve up noise at scale. To improve investigations, organizations need insight that is contextualized, validated, and defensible. Speed remains important, but it cannot be the sole criterion on which investigative success is judged. ## The solution Babel Street, a global leader in mission-grade risk intelligence, has spent more than a decade providing military, defense, intelligence, and enterprise organizations with AI-powered insight based on world-class data. That experience and expertise have shaped the [Babel Street Agentic Risk Intelligence Platform](https://www.babelstreet.com/platform). Setting a new standard for threat detection, the platform helps governments and businesses outpace dynamic threats by providing panoramic, up-to-the-second insight into geopolitical risk and threats, insider threats, supply chain vulnerabilities, and more. Working with Babel Street, corporations and governments obtain defensible insight based on verified data. Transparent agentic AI processes speed time to trustworthy insight. This is accomplished through: ### Elite tradecraft The platform’s Insights Investigator is an agentic capability that puts tradecraft-trained AI agents at the forefront of investigative work. These agents are modeled on proven investigative techniques and workflows developed by expert analysts and investigators. They enable a shift from target-first or hypothesis-driven searches to more holistic investigations. Human practitioners remain in control of investigative objectives, pathways, scopes, and outcomes. ### Agentic execution Rather than merely returning chatbot-like answers to queries, Insights Investigator’s AI agents execute complex, multi-step investigative tasks. In saving investigators from the mechanical labor of endless Boolean searches, Investigator can cut data-gathering time in half. Automated reporting capabilities can reduce the time needed to produce certain reports from 36 hours to a mere 15 minutes.[1] ### Data Dominance™ Babel Street’s rights-cleared, mission-curated, multilingual data pipeline underpins Investigator. This data is culled from credible information sources published in more than 200 languages. Our [entity extraction](https://www.babelstreet.com/blog/what-is-entity-extraction) capabilities structure data at ingestion, improving search precision and enabling more consistent, evidence-backed analyses. ### Trust, governance, and auditability Unlike black-box AI systems, Investigator is an explainable AI tool that can delineate its own research plans, query logic, reasoning, and sourcing. Investigator also provides audit trails and the other types of traceability needed to satisfy regulators, leadership, and stakeholders. At Babel Street, we fuse elite tradecraft with the world’s deepest collection of rights-cleared data to empower fast, insightful, and defensible decision-making. **Endnotes** 1. Babel Street, “Mercyhurst University: A Partnership for Nurturing the Next Generation of Intelligence Analysts,” June 2025, [https://www.babelstreet.com/resources/case-studies/a-partnership-for-nurturing-the-next-generation-of-intelligence-analysts?utm_source=Direct&utm_medium=Direct&utm_campaign=Not+Provided](https://www.babelstreet.com/resources/case-studies/a-partnership-for-nurturing-the-next-generation-of-intelligence-analysts?utm_source=Direct&utm_medium=Direct&utm_campaign=Not+Provided) ## Frequently asked questions **What is agentic AI in intelligence analysis? ** Agentic AI in intelligence analysis is artificial intelligence that autonomously executes multi-step investigative workflows — planning, searching, analyzing, and synthesizing data without constant human input. When combined with investigative tradecraft and trusted data, it produces transparent, citable, and defensible intelligence. **Why isn’t data collection enough for investigations?** Data collection alone is insufficient because the challenge is no longer access — it’s analysis and execution. Investigators must filter massive volumes of fragmented data, connect signals, and validate findings. Without context and verification, more data increases noise and risk of incorrect conclusions. **What makes investigative intelligence defensible?** Defensible intelligence is built on verified data, transparent reasoning, and traceable evidence. It includes explainable AI outputs, citable sources, and audit trails, ensuring conclusions can withstand scrutiny from regulators, analysts, and decision-makers. **How does agentic AI improve investigations?** Agentic AI improves investigations by automating complex workflows, expanding investigative scope, and delivering faster, evidence-backed insights. It reduces manual query work, applies investigative tradecraft, and provides explainable results with full source attribution. **What is the difference between traditional OSINT and agentic AI?** Traditional OSINT relies on manual, query-driven searches and often produces fragmented insights. Agentic AI executes end-to-end investigations, connects data across sources, and delivers transparent, defensible conclusions — shifting from data retrieval to actionable intelligence.