How Agentic AI Will Transform VC Finance

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TL;DR:

• VC finance is ripe for agentic AI, with early wins in KPI reporting, fund modeling, and data orchestration across fragmented systems.
• Trust, explainability, and governance are non-negotiables for deploying AI agents in high-stakes finance workflows.
• Strategic modeling, LP updates, and scenario planning could soon be agent-first, cutting hours of manual work down to minutes.
• VCs must go beyond investing in AI to operationalizing it across portcos, using playbooks to drive margin expansion and efficiency.
• Integration is a competitive moat. “Agents that live in isolation don’t drive value,” says Pete. The best startups are built to embed deeply into enterprise systems.

Before we dive into the key takeaways from this episode, be sure to catch the full episode here:

Meet Pete - VP of Finance at 645 Ventures

Pete Keenan, VP of Finance at 645 Ventures, bridges deep VC expertise with a sharp eye for operational innovation. From portfolio KPI tracking to fund modeling and LP updates, Pete’s experience spans the full finance stack.

He supports diligence on later-stage investments, leads transaction support, and drives the finance function across all 645 entities. He’s also a thought leader in how agentic AI can power the next generation of decision-making and reporting.

With a strong POV on orchestration, integration, and trust, Pete champions a future where AI agents amplify, not replace, finance teams. Whether streamlining investor updates or enabling scenario modeling in real time, Pete is helping reshape what enterprise-grade AI looks like in venture capital.

Why Agentic AI Is a Game Changer for VC Finance

Agentic AI is a complete shift in how finance teams operate. “Traditional SaaS tools have been reactive. You still have to know the right questions to ask,” says Pete Keenan.

By contrast, agents bring autonomy and orchestration to the table. They don’t wait for human inputs, but proactively drive workflows forward. Pete explains, “Agents can coordinate tasks across multiple systems, APIs, and datasets.”

This cross-functional orchestration is exactly where most operational friction exists today. The move from assistive chatbots to autonomous AI workers allows VC finance teams to offload entire workflows, not just get answers.

It’s not about replacing humans, but about turning AI into “AI employees” that extend the team and increase decision velocity across the finance stack.

The Real Roadblocks: Trust, Context, and Compliance

Deploying agents in venture finance isn’t plug-and-play. Trust, reliability, and compliance aren’t optional, they’re essential.

“The agent has to perform consistently across messy, disparate real-world inputs,” — Pete Keenan

And beyond performance, explainability is a must. “We need to understand how the agent reached a particular conclusion and be able to audit that workflow.”

Without this, adoption stalls. Pete stresses that agents must operate within a framework of compliance and traceability, especially when informing LP updates or strategic modeling.

Even more critical is context. “Agents are only as good as the context they’re operating within,” Pete explains. That means access to systems like the general ledger and portfolio data sources. Governance is the foundation. Without it, no amount of model performance will earn the trust of high-stakes finance teams.

Portfolio Use Cases: From KPI Reporting to Strategic Modeling

Agentic AI shines brightest when applied to portfolio-level tasks. Pete points to KPI reporting as one of the clearest wins: “It’s messy, time-consuming, and unstructured.

A database agent could structure and update that data in real time, while a reporting agent translates it into a dashboard for LPs.” Fund modeling is another standout use case.

“You’re modeling scenarios for capital calls, reserve strategies, or distributions. A decision agent could suggest actions or alert us to risks,” Pete explains.

These aren’t simple automations, but high-context workflows powered by a stack of agents working together. He emphasizes, “It’s the composition of agents, not isolated capabilities, that unlocks value.”

“Humans plus AI get to better outcomes, in less time. That’s the power.” — Ankur Patel

The endgame is freeing teams from manual work and enabling faster, more strategic decision-making across venture capital operations.

Operationalizing AI Across the Portfolio, Not Just Investing in It

VCs should not just fund AI. They should help portcos operationalize it. “I firmly believe VC should be helping the portfolio adopt AI agents, not just invest in the category,” says Pete.

At 645 Ventures, that means building out playbooks and tooling that founders can actually use. “We build playbooks on go-to-market, hiring, finance. And I’m especially hands-on with the finance ones,” Pete shares. The goal is to drive operational margin expansion.

“You’ll start seeing 75 percent gross margin companies becoming 85 percent plus. And others hitting breakeven faster.” — Pete Keenan

AI becomes the lever that improves unit economics across the board. For Pete, this hands-on approach is what separates capital providers from real partners.

“We view value creation as more than just capital and connections,” he says.

What VCs Should Look for in AI-Native Startups

Agent-native startups need more than model performance. Pete outlines what he looks for: “First, the team must have the right technical depth, not just in AI, but in systems engineering and orchestration.”

These workflows break if the logic breaks. Second, having an integration strategy is critical.

“Agents that live in isolation don’t drive value. The best startups integrate into CRMs, ERPs, and data warehouses.” — Pete Keenan

Without that, they’re just another tool outside the customer’s workflow. Security protocols, APIs, and awareness of enterprise complexity are key signs of maturity.

Pete puts it bluntly: “The best companies are building around real enterprise systems.” Finally, he stresses that agents need to be composable and repeatable.

It's not about flashy demos. It’s about building durable infrastructure that can scale across high-stakes, regulated environments.

Would you like to learn more about strategic investing in AI? Check out this episode on strategic investing in AI from a venture perspective with Collin Bhojwani and Taylor Sieverling.