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SMART SUMMARY
- Agentic AI opens up new paths for efficiency gains, but it also introduces unique and heightened privacy and data protection concerns.
- Interested private equity firms should recognize the regulatory and cyber implications up front, pilot the right use cases, and embed agentic AI into operations in a controlled, auditable way.
As artificial intelligence ("AI") accelerates, a new class of tools is entering the fray: AI agents – systems that don't just respond to prompts the way large language models ("LLMs") such as OpenAI's ChatGPT and Anthropic's Claude do, but can independently interact with third-party systems (often through Application Programming Interface ("APIs")) and execute multistep tasks to achieve user-defined goals.1 AI agents are designed to solve problems without the need for human oversight or prompting and are akin to personal assistants.
How Can Agents be Utilized to Save Time and Money?
There's significant opportunity emerging through agentic AI, such as increased efficiency and productivity, scalability of task automation, improved decision-making, and longterm cost savings. Private equity firms, in particular, are often resource-constrained but workflow-heavy, with repetitive tasks that can benefit from automation.
Here's where AI agents can help:
- Deal Sourcing & Screening: Agents can monitor databases, scan news feeds, and flag emerging acquisition targets that match specific investment criteria.
- Diligence Support: Agents can extract key terms from NDAs, LOIs, and financial statements and compare data across documents (e.g., EBITDA discrepancies, etc.)
- Investor Reporting & Communications: Agents can manage investor FAQ repositories, draft LP update templates and quarterly updates, or track fund compliance deliverables.
- Internal Operations & Workflow Automation: Agents can manage HR onboarding tasks, invoice processing, and manage scheduling. They can also auto-generate notes, emails, financial data, and can schedule follow-ups or assign action items.
What Makes AI Agents Riskier Than Traditional LLMs?2
Because AI agents have the capacity to act with much greater independence and to access significantly more data from a more diverse set of data stores and sources, they present distinct risks from those posed by traditional LLMs.
Unlike LLMs that respond to discrete queries, AI agents often connect directly to various internal and external systems, such as email servers, file repositories, customer databases, and enterprise tools. These integrations and interactions increase the potential cyberattack surface, as vulnerabilities in any integrated system may be exploited to compromise the agent or its host environment.
Because AI agents may autonomously take actions (e.g., send emails, modify databases, initiate transactions) in pursuit of a user's directive, they pose a greater risk of accessing or disclosing personal information and other confidential or proprietary data in ways that the user did not anticipate or desire. This could run afoul of privacy laws such as the General Data Protection Regulation (GDPR) and U.S. state consumer privacy laws, which are premised on concepts such as transparency, consent, and data minimization and purpose limitation.
These risks can be compounded by the fact that many AI agents' decision-making processes are somewhat of a "black box" – meaning, they're difficult for users to understand or explain - as many agents lack built-in tools for detailed logging, versioning, or audit trails.3 Even when AI models are built to show their "chain of thought" (i.e., how they came to a conclusion), many models do not accurately reflect on their own reasoning.
Conclusion
AI agents aren't just a tech trend – they're a strategic inflection point. Despite the risks, there is significant upside for private equity firms that utilize agentic AI. Private equity firms that treat them with caution, but also creativity, stand to gain measurable advantages in speed, scalability, and insight. In a market where agility is everything, smart deployment of AI agents could be the edge that propels a firm past its peers.
Footnotes
1 Daniel Berrick, Minding Mindful Machines: AI Agents and Data Protection Considerations, Center for Artificial Intelligence at the Future of Privacy Forum (Apr. 2025), https://fpf.org/wp-content/uploads/2025/04/MindingMindful-Machines_-AI-Agents-and-DataProtection-Considerations.pdf.
2 National Institute of Standards & Technology, AI Risk Management Framework (Jan. 2023), https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
3 Nirupam Samanta, The Growing Challenge of Auditing Agentic AI, ISACA (Sept. 2, 2025), https://www.isaca.org/resources/news-and-trends/industrynews/2025/the-growing-challenge-ofauditing-agentic-ai
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