Governing Autonomous AI Agents: Critical Implications for Health Tech Entrepreneurs and Investors​​​​​​​​​​​​​​​​

Disclaimer: The thoughts and opinions expressed in this essay are my own and do not reflect those of my employer.

Abstract

This essay examines Joe Kwon's policy paper "AI Agents: Governing Autonomy in the Digital Age" from the Center for AI Policy and analyzes its critical implications for health tech entrepreneurs and investors. The paper proposes a comprehensive regulatory framework for autonomous AI agents that includes an "Autonomy Passport" system, continuous monitoring requirements, human oversight mandates, and workforce impact research. For health tech stakeholders, these proposed regulations present both significant compliance challenges and strategic opportunities. While the regulatory framework may increase development costs and time-to-market, it also creates competitive moats for well-capitalized companies and standardizes safety practices across the industry. Health tech entrepreneurs must prepare for substantial regulatory compliance investments, while investors need to adjust their due diligence processes and portfolio strategies to account for the emerging regulatory landscape. The paper's emphasis on human oversight in critical domains aligns closely with existing healthcare regulations but may limit the efficiency gains that make AI agents attractive to healthcare organizations.

Table of Contents

1. Introduction and Context Setting

2. Key Findings from Kwon's Policy Paper

3. Current State of AI Agents in Healthcare

4. Regulatory Impact Analysis for Health Tech Companies

5. Strategic Implications for Entrepreneurs

6. Investment Considerations and Portfolio Strategy

7. Compliance Framework Development

8. Competitive Landscape Transformation

9. Future Scenarios and Risk Assessment

10. Recommendations and Action Items

Governing Autonomous AI Agents: Critical Implications for Health Tech Entrepreneurs and Investors

In May 2025, Joe Kwon of the Center for AI Policy published a comprehensive policy paper titled "AI Agents: Governing Autonomy in the Digital Age" that proposes the most detailed regulatory framework yet conceived for autonomous AI systems. For health tech entrepreneurs and investors, this 23-page document represents a potential inflection point that could fundamentally alter the development, deployment, and commercialization of AI-powered healthcare solutions. The paper's recommendations, if adopted by Congress, would establish mandatory pre-deployment licensing, continuous monitoring requirements, human oversight mandates, and comprehensive workforce impact assessments for AI agents operating above basic assistant levels.

The timing of this policy proposal coincides with explosive growth in healthcare AI agent adoption. Industry data from 2025 shows that AI agents are already managing 50-60% of front-office administrative tasks in many healthcare practices, reducing revenue cycle management costs by up to 70%, and automating complex workflows from appointment scheduling to clinical documentation. Major healthcare technology companies like ServiceNow report USD 325 million in annualized value from autonomous agent deployments, while organizations like Mayo Clinic are pioneering "agentic automation architectures" that fundamentally reimagine clinical and operational workflows. This rapid adoption trajectory makes Kwon's regulatory proposals particularly consequential for stakeholders who must navigate the intersection of technological capability and regulatory compliance.

The paper's central thesis argues that AI agents pose three distinct categories of risk: catastrophic misuse through cyberattacks or dual-use applications, gradual human disempowerment as decision-making migrates to opaque algorithms, and unprecedented workforce displacement affecting up to 300 million jobs globally according to Goldman Sachs projections. Healthcare, with its complex regulatory environment, high-stakes decision making, and substantial economic footprint, represents a critical testbed for these regulatory approaches. The proposed framework would require health tech companies to fundamentally rethink their product development cycles, compliance strategies, and go-to-market approaches while potentially creating new competitive advantages for organizations capable of navigating increased regulatory complexity.

Understanding the paper's key findings requires examining the sophisticated five-level autonomy classification system that Kwon proposes as the foundation for risk-proportional regulation. Level 1 "shift-length assistants" can work autonomously for roughly eight hours with human oversight, while Level 5 "frontier super-capable systems" operate indefinitely across any domain without human guidance. Most current healthcare AI applications fall into Levels 1-2, but the rapid pace of capability advancement suggests that Level 3-4 systems capable of multi-day autonomous operation may emerge within the next few years. This progression timeline has direct implications for health tech companies, as the regulatory requirements become substantially more stringent at Level 2 and above, requiring formal registration, safety audits, and continuous monitoring.

The paper's "Autonomy Passport" proposal represents the most significant regulatory innovation, establishing a mandatory federal registration system for AI agents operating at Level 2 or higher. This system would require companies to file detailed dossiers documenting their agents' mission envelopes, tool access permissions, autonomy classifications, security validation results, and emergency contact information before deployment. The US AI Safety Institute would set technical standards and maintain a public registry of approved agents, while accredited private firms would conduct the required safety audits. For health tech entrepreneurs, this represents a fundamental shift from the current largely self-regulated environment to a formal pre-market approval process similar to FDA device clearance but potentially more complex and time-consuming.

The continuous monitoring and enforcement mechanisms proposed in the paper would create ongoing compliance obligations throughout an AI agent's operational lifecycle. High-capability agents would need to operate within digital sandboxes that enforce pre-approved action lists, attach tamper-evident signatures to every outbound action, and remain subject to emergency recall authority by the Cybersecurity and Infrastructure Security Agency. These requirements mirror some aspects of existing healthcare IT security frameworks but extend far beyond current practice in terms of real-time monitoring and federal oversight authority. Healthcare organizations that have invested heavily in AI agent deployments would need to retrofit their systems to comply with these new monitoring requirements, potentially creating significant technical and financial challenges.

The human oversight mandate for critical systems represents perhaps the most directly relevant aspect of Kwon's framework for healthcare applications. The paper proposes that when AI agents make recommendations in domains that normally require professional licensing or regulatory approval, qualified humans must review and approve those recommendations before execution. In healthcare contexts, this would mean that licensed healthcare professionals must approve AI agent recommendations for prescription changes, treatment modifications, diagnostic interpretations, and other clinical decisions. While this aligns with existing medical practice standards and liability frameworks, it may limit the efficiency gains that make AI agents attractive to healthcare organizations facing staffing shortages and cost pressures.

The workforce impact research mandate represents the final pillar of Kwon's regulatory framework, directing federal agencies to produce annual reports tracking job displacement and wage trends related to AI agent adoption. For healthcare, this is particularly relevant given projections that up to 40% of jobs in advanced economies show high exposure to AI-driven automation, with college-educated roles facing even higher displacement rates. Healthcare organizations are already reporting significant productivity gains from AI agent deployments, with some companies achieving 15% developer productivity improvements and double-digit reductions in call handling times. The proposed research mandate would create systematic tracking of these workforce impacts, potentially informing future regulatory interventions or support programs.

Current healthcare AI agent deployments demonstrate both the transformative potential and regulatory complexity that Kwon's framework aims to address. Revenue cycle management represents one of the most mature application areas, with companies like CodaMetrix using natural language processing to automate medical coding across 200 hospitals and 50,000 providers. These systems continuously learn from clinical data while incorporating payer rules and compliance requirements, achieving accuracy levels that reduce manual intervention while maintaining regulatory compliance. Under Kwon's proposed framework, these systems would likely require Level 2 classification and full Autonomy Passport registration, given their ability to make consequential financial decisions autonomously.

Clinical documentation represents another rapidly evolving application area where AI agents are fundamentally changing healthcare workflows. Companies like Augmedix deploy ambient documentation tools that capture natural clinician-patient conversations and convert them into structured medical notes, serving nearly half a million clinicians. These systems operate continuously during patient encounters, making real-time decisions about what information to capture and how to structure clinical documentation. The proposed regulatory framework would likely require human oversight for clinical documentation agents that generate billable diagnosis codes or treatment recommendations, potentially limiting their autonomous capabilities while ensuring clinical accountability.

Diagnostic and imaging applications showcase the potential for AI agents to operate at higher autonomy levels while maintaining safety and accuracy. Companies like Qure.ai have deployed AI systems across 4,500 sites in over 100 countries to automate interpretation of X-rays, CT scans, and ultrasounds, particularly in areas with limited radiology expertise. These systems can identify and prioritize conditions like tuberculosis, lung cancer, and stroke, often detecting anomalies that human radiologists might miss. However, under Kwon's framework, such systems would require human radiologist approval before final diagnostic decisions, maintaining the current standard of care while potentially limiting efficiency gains.

The regulatory impact analysis for health tech companies reveals several critical considerations that will shape strategic planning and investment decisions. Compliance costs represent the most immediate concern, as companies would need to invest in safety auditing, continuous monitoring systems, human oversight integration, and emergency response capabilities. These costs are not one-time expenses but ongoing operational requirements that scale with system complexity and deployment breadth. For early-stage health tech startups, these compliance requirements could represent existential challenges, as the cost of Autonomy Passport registration and continuous monitoring may exceed available funding runway.

Time-to-market implications are equally significant, as the proposed pre-deployment review process could add months or years to product development cycles. Healthcare AI companies currently benefit from relatively rapid iteration and deployment capabilities, particularly for administrative and operational applications that don't require formal FDA approval. The Autonomy Passport system would introduce a formal gate that must be cleared before any Level 2 or higher AI agent can be deployed, potentially slowing innovation cycles and reducing the first-mover advantages that characterize the current competitive landscape.

Market access considerations add another layer of complexity, as the proposed framework would require major cloud providers and app stores to block any AI agent that doesn't appear on the federal green list. This creates a binary approval system where companies either achieve full market access through successful registration or face complete market exclusion. For health tech companies that rely on cloud-based deployment models, this represents a fundamental shift in go-to-market strategy and risk management. Companies would need to build stronger relationships with accredited auditing firms and develop more robust quality assurance processes to ensure successful registration.

The competitive landscape implications of Kwon's regulatory framework are particularly nuanced for health tech companies. Large, well-capitalized organizations with existing regulatory compliance capabilities may benefit from increased barriers to entry that limit competition from smaller, more agile startups. Companies like Epic, Cerner, and other established healthcare IT vendors have extensive experience navigating complex regulatory environments and may be better positioned to absorb the costs and complexity of Autonomy Passport compliance. This could accelerate market consolidation and reduce the likelihood of disruptive innovation from startup companies.

However, the framework also creates opportunities for companies that can develop specialized compliance capabilities or serve as regulatory technology providers. The requirement for accredited private auditing firms creates an entirely new service category that doesn't currently exist in the AI industry. Health tech companies with strong regulatory expertise could potentially pivot to serve this emerging market, while specialized consulting firms could emerge to help smaller companies navigate the compliance requirements. The standardization aspects of the framework could also benefit companies by creating clearer requirements and reducing regulatory uncertainty.

Strategic implications for health tech entrepreneurs require fundamental rethinking of business model assumptions and development priorities. Product development strategies must now account for regulatory compliance from the earliest stages, rather than treating compliance as a post-development consideration. This means integrating safety-by-design principles, building comprehensive audit trails, and designing human oversight capabilities as core system features rather than add-on components. Entrepreneurs must also consider whether their target applications justify the increased development and compliance costs, potentially shifting focus toward higher-value use cases that can support the additional regulatory burden.

Funding strategy considerations become more complex under the proposed regulatory framework, as investors will need to factor compliance costs and regulatory risks into their investment decisions. Early-stage companies may need larger seed and Series A rounds to fund regulatory compliance activities, while later-stage companies may face longer development cycles that delay revenue generation and extend time-to-exit scenarios. Entrepreneurs must be prepared to articulate clear regulatory strategies to potential investors and demonstrate deep understanding of the compliance requirements that affect their specific applications.

Partnership and acquisition strategies may become more attractive under increased regulatory complexity, as smaller companies may find it more efficient to partner with or be acquired by larger organizations with established regulatory capabilities. This could lead to earlier exit opportunities for entrepreneurs but potentially at lower valuations if regulatory uncertainty reduces buyer enthusiasm. Strategic partnerships with established healthcare IT companies or device manufacturers may become essential for smaller companies that lack the resources to navigate complex regulatory requirements independently.

Investment considerations for health tech venture capital and private equity firms must evolve to address the new regulatory landscape that Kwon's framework would create. Due diligence processes must now include detailed assessment of regulatory compliance strategies, evaluation of management team regulatory expertise, and analysis of competitive positioning in a more heavily regulated market environment. Investors need to develop capabilities to evaluate the technical feasibility and cost implications of Autonomy Passport compliance, human oversight integration requirements, and continuous monitoring system development.

Portfolio construction strategies may shift toward companies with stronger regulatory moats and away from pure-play technology companies that lack healthcare domain expertise. The increased barriers to entry created by regulatory compliance requirements may make incumbent healthcare IT companies more attractive investment targets, while making early-stage AI startups riskier investments unless they demonstrate exceptional regulatory preparation. Investors may also need to increase reserve requirements for follow-on funding to support compliance activities that were not previously necessary.

Risk assessment frameworks must incorporate regulatory change risk as a primary factor in investment decisions. The Kwon framework represents just one possible regulatory outcome, and investors must consider scenarios ranging from no additional regulation to even more restrictive requirements. Companies with greater regulatory flexibility and stronger compliance capabilities will be better positioned to adapt to various regulatory scenarios, making them more attractive investment targets. Geographic diversification considerations also become relevant, as regulatory requirements may vary significantly across different jurisdictions and create opportunities for regulatory arbitrage.

Compliance framework development represents a critical operational priority for health tech companies preparing for potential regulatory changes. Legal and regulatory affairs capabilities must be substantially enhanced, with companies needing either in-house expertise or reliable external counsel with deep understanding of AI regulation, healthcare compliance, and emerging technology governance. The interdisciplinary nature of the proposed requirements means that companies need legal expertise spanning technology law, healthcare regulation, employment law, and federal administrative procedure.

Technical infrastructure development for compliance requires significant engineering investment in monitoring systems, audit trail capabilities, human oversight integration, and emergency response mechanisms. These systems must be designed for regulatory compliance from the ground up, rather than retrofitted onto existing architectures. Companies must also develop capabilities to work with third-party auditing firms and maintain detailed documentation of system capabilities, safety testing results, and operational procedures. The technical complexity of implementing tamper-evident signatures, sandbox containment, and real-time monitoring represents substantial development effort that must be factored into product roadmaps.

Quality assurance and risk management processes must be elevated to meet the standards expected by federal regulators and third-party auditors. This includes implementing formal software development lifecycle processes, conducting comprehensive security testing, maintaining detailed change control documentation, and developing incident response procedures. Companies must also establish governance structures that can demonstrate appropriate oversight of AI system development and deployment decisions, potentially requiring board-level committees with relevant expertise.

The competitive landscape transformation that would result from Kwon's regulatory framework creates both risks and opportunities across different market segments. Enterprise healthcare IT companies may benefit from increased barriers to entry and the opportunity to leverage existing regulatory relationships and compliance capabilities. However, they also face substantial costs to retrofit existing AI systems for compliance and may need to slow innovation cycles to accommodate regulatory review processes. The requirement for human oversight in critical applications may limit the automation benefits that justify AI investments, potentially reducing market demand for certain categories of AI solutions.

Startup and emerging company impacts are more complex and depend heavily on specific business models and target applications. Companies focused on non-critical administrative applications may benefit from lower regulatory barriers, while those targeting clinical decision support or autonomous diagnostic applications face more stringent requirements. The requirement for pre-deployment registration could eliminate the rapid iteration and continuous deployment models that many AI startups rely on for competitive advantage. However, successful navigation of the regulatory framework could also create significant competitive moats for companies that achieve compliance.

International competitiveness considerations become relevant as US companies may face compliance costs and development delays that don't affect international competitors. However, the proposed framework's alignment with emerging international standards could also position US companies advantageously in global markets that adopt similar regulatory approaches. The paper specifically mentions coordination opportunities with the UK AI Safety Institute, the G7 Hiroshima Process, and emerging OECD frameworks, suggesting that regulatory harmonization may reduce the competitive disadvantage of early compliance.

Future scenario analysis reveals several possible trajectories that health tech stakeholders must consider in strategic planning. The base case scenario assumes partial adoption of Kwon's recommendations, with Congress implementing some form of AI agent registration and oversight but potentially with modified requirements or longer implementation timelines. This would create a transitional period during which companies can adapt their systems and processes while maintaining competitive dynamics similar to current conditions.

The accelerated regulation scenario envisions rapid adoption of the full framework, potentially in response to a significant AI-related incident that creates political pressure for immediate regulatory action. This scenario would create severe challenges for companies that haven't prepared for compliance requirements, potentially triggering market consolidation and significant competitive reshuffling. Companies with advanced regulatory preparation would gain substantial competitive advantages, while unprepared companies could face existential challenges.

The expanded regulation scenario considers the possibility that initial AI agent regulations could be followed by additional requirements covering broader aspects of healthcare AI deployment. This could include integration with existing FDA device regulation, expansion of human oversight requirements, or additional reporting obligations related to clinical outcomes and patient safety. Companies must consider how initial regulatory compliance capabilities could be expanded to address future requirements and whether their regulatory strategies are sufficiently flexible to adapt to evolving requirements.

Risk assessment and mitigation strategies must address both direct regulatory compliance risks and indirect competitive and market risks. Regulatory non-compliance risks include the possibility of market exclusion, civil penalties, criminal liability for executives, and reputational damage that affects customer relationships and investor confidence. Companies must develop comprehensive compliance monitoring systems and legal risk assessment capabilities to identify and address potential violations before they result in enforcement actions.

Market and competitive risks include the possibility that regulatory requirements could reduce market demand for AI solutions, increase customer acquisition costs, or create competitive advantages for non-AI alternatives. Companies must maintain flexibility in their product development strategies and consider pivot options that could reduce regulatory exposure while maintaining market position. Financial risks include the possibility that compliance costs could exceed revenue generation capabilities, particularly for early-stage companies with limited resources.

Technology and operational risks focus on the possibility that required compliance systems could interfere with product functionality, reduce system performance, or create new security vulnerabilities. The integration of human oversight requirements with autonomous AI systems presents particular technical challenges that must be carefully managed to maintain both regulatory compliance and operational effectiveness. Companies must also consider the risks associated with third-party dependencies, including auditing firms, cloud providers, and monitoring system vendors.

Recommendations and action items for health tech entrepreneurs center on proactive preparation for potential regulatory requirements while maintaining operational flexibility. Immediate actions should include comprehensive assessment of current AI system autonomy levels using the five-level framework proposed by Kwon, evaluation of which systems would require registration and oversight under the proposed rules, and development of preliminary compliance strategies for high-risk applications. Entrepreneurs should also begin building relationships with potential auditing partners and regulatory advisors while monitoring Congressional activity related to AI regulation.

Medium-term strategic actions should focus on integration of compliance capabilities into product development processes, including implementation of audit trail systems, human oversight interfaces, and monitoring capabilities that could satisfy regulatory requirements. Companies should also consider geographic expansion strategies that could provide regulatory diversification and develop contingency plans for different regulatory scenarios. Investment in regulatory expertise, either through hiring or external partnerships, becomes essential for companies targeting applications that would face significant oversight requirements.

Long-term strategic positioning requires consideration of how regulatory compliance capabilities could become competitive advantages and revenue generators. Companies that develop strong compliance capabilities may be able to serve as regulatory technology providers for other organizations or provide consulting services to help smaller companies navigate complex requirements. The standardization aspects of the proposed framework could also create opportunities for companies that develop best-practice approaches that can be scaled across the industry.

Investment recommendations for venture capital and private equity firms emphasize the need for enhanced due diligence capabilities and risk assessment frameworks that account for regulatory change scenarios. Investors should develop expertise in AI regulation and healthcare compliance or partner with specialized advisors who can provide detailed assessment of regulatory risks and opportunities. Portfolio companies should be encouraged to begin compliance preparation activities immediately, even in the absence of final regulatory requirements, to maintain strategic flexibility and competitive positioning.

The health tech industry stands at a critical juncture where technological capability advancement intersects with emerging regulatory frameworks that could fundamentally reshape competitive dynamics and business model viability. Joe Kwon's comprehensive policy framework represents the most detailed roadmap yet proposed for AI agent governance, with implications that extend far beyond simple compliance requirements. For entrepreneurs and investors in the health tech space, success in this evolving landscape will require not just technological innovation but also regulatory sophistication, strategic flexibility, and deep understanding of the complex interplay between automation capabilities and human oversight requirements.

The companies that thrive in this new regulatory environment will be those that view compliance not as a burden but as a source of competitive advantage, building regulatory capabilities that enable them to deploy AI agents safely and effectively while creating barriers to entry for less prepared competitors. The investment firms that succeed will be those that can accurately assess regulatory risks and opportunities, support portfolio companies through complex compliance requirements, and identify emerging opportunities created by the new regulatory landscape. As the health tech industry continues its rapid evolution toward AI-powered automation, the intersection of innovation and regulation will increasingly determine which companies and investors achieve sustainable success in this transformative market.