What Is Agentic AI and How Does It Apply to Investing?

The term "agentic AI" has been picking up steam, and like most AI terminology, it risks becoming meaningless through overuse. But the concept behind it is real and worth understanding, especially if you work in private equity or venture capital. It describes a genuinely different kind of system, and the difference matters for how you source and monitor deals.

What makes AI "agentic"?

A chatbot answers questions. You ask it something, it responds. The interaction is one turn deep. An agentic system is different. It takes a goal, reasons about how to accomplish it, selects from available tools, executes a plan, and adapts when things don't work on the first try.

The simplest way to think about it: a chatbot is like asking an intern a question and getting an answer. An agentic system is like giving an analyst a project and getting back a deliverable. The analyst decides what research to do, which databases to check, how to structure the output, and whether the first approach was good enough or needs a second pass.

In technical terms, agentic AI systems have a few defining characteristics. They reason about tasks before acting. They choose which tools to use based on the situation. They handle multi-step workflows where later steps depend on earlier results. And they adjust their approach when initial results are insufficient. None of this requires general intelligence or anything close to it. It's a practical architecture for getting useful work done with LLMs.

How is this different from just searching a database?

When you type a query into a traditional deal sourcing platform, you're executing a search. The system takes your filters, runs them against a database, and returns whatever matches. If nothing matches, you get an empty screen. If too much matches, you get noise. The system doesn't have an opinion about your query or your intent. It just executes.

An agentic system interprets what you're asking for. If you say "find me companies similar to ServiceTitan but earlier stage and focused on HVAC specifically," the system has to break that down. What makes ServiceTitan relevant? Is it the vertical software model, the field service management angle, the blue-collar workforce focus? The system reasons about that, then decides on a search strategy, then evaluates results, then potentially adjusts.

That reasoning step is what separates agentic from traditional. The system doesn't just execute your query. It figures out the best way to answer it.

What does agentic AI look like in deal sourcing?

In practice, an agentic deal sourcing tool works through a chain of decisions rather than a single lookup. Radar is a good example. When you describe what you're looking for, the system doesn't just run a keyword match. It interprets the query using an LLM, then selects from multiple tools depending on what's most appropriate. Sometimes it's a semantic search across company descriptions. Sometimes it's finding companies similar to a reference company. Sometimes it's filtering a broader set by specific criteria like geography, size, or funding stage.

The key part is that the system makes those choices itself. If a semantic search returns too few results, it broadens the query. If results are too broad, it applies additional filters. If you mention a specific company as a reference point, it shifts to a similarity-based approach rather than trying to match keywords from your description. These aren't features that were hardcoded for each scenario. They're decisions the system makes based on reasoning about your intent.

This is the same kind of multi-step reasoning that a good analyst does intuitively, except it happens in seconds across millions of companies rather than hours across a few hundred.

How does agentic AI apply to market monitoring?

Sourcing is one application, but monitoring is where the agentic pattern becomes even more valuable. Monitoring a market is inherently a multi-step process. You need to collect information, decide what's relevant, assess significance, and surface what matters. Each step depends on the one before it.

An agentic monitoring system handles this full chain. It scrapes new data from company websites, LinkedIn, job boards, and news sources. It filters out noise. It scores changes based on what actually matters for a given investment thesis. It synthesizes findings into a report that tells you what changed and why it might matter. The system isn't just aggregating alerts. It's reasoning about whether a particular hiring pattern or product launch or geographic expansion is significant given what you care about.

The practical result is the difference between getting 200 alerts a week that you ignore and getting a weekly brief that tells you three companies in your target market just hit inflection points.

Why should PE and VC firms care about this now?

The honest answer is that most firms don't need to understand the technical architecture. What matters is the output. Agentic systems find companies that filter-based tools miss. They monitor markets with a level of nuance that alert systems can't match. They improve over time as they learn what matters to your specific thesis.

The firms that are adopting these tools aren't doing it because they're excited about AI architecture. They're doing it because the results are materially better. They're seeing companies earlier, evaluating more relevant opportunities, and spending less time on manual research that doesn't convert to deals.

The underlying technology will keep improving. The reasoning will get sharper, the tool selection will get better, and the systems will handle more complex workflows. But the core pattern is already working: describe what you're looking for, and let the system figure out how to find it. That's what agentic means in practice, and it's a meaningful step beyond what came before.

Radar uses agentic AI to source and monitor private market opportunities. Describe your thesis in plain English, and Radar reasons about the best way to find companies that fit. Try it free or book a demo.