Mayer Brown Publishes an AI Agent Governance Framework. Here's What It Means for Enterprise.

When a law firm that advises Fortune 500 companies on billion-dollar transactions publishes a governance framework for AI agents, it's not a thought exercise. It's a signal that the legal liability clock has started ticking.

In February 2026, Mayer Brown — one of the world's largest and most respected law firms — published a comprehensive framework for governing agentic AI systems. The same month, they released a companion paper arguing that traditional SaaS contracts are fundamentally inadequate for agentic AI, proposing a shift toward BPO-style service agreements. And independently, MIT Sloan Management Review published research showing that 69% of international AI experts believe agentic AI requires entirely new management approaches.

Three signals. Same message: the era of deploying AI agents without governance infrastructure is over.

For those of us who have been building governance frameworks for autonomous AI systems, this isn't surprising. But it is significant — because when the legal profession codifies something, enterprises listen. And what Mayer Brown codified maps remarkably well to what we've been advocating at GalacticaIA.

What Mayer Brown Proposes: Six Pillars

Mayer Brown's framework identifies six core components for governing agentic AI systems. What's notable is that they explicitly position this as an extension of existing AI governance, not a replacement — a pragmatic approach that reduces the organizational resistance that kills most governance initiatives.

1. AI Governance Team

Every agentic AI deployment needs a cross-functional oversight structure with clearly defined roles: decision-makers who set policy and define boundaries, product teams who implement and monitor, cybersecurity and privacy teams who integrate agents into existing security procedures, and frontline employees who can identify and escalate issues.

This isn't a committee that meets quarterly. It's an operational governance function — which is exactly the kind of oversight gap we identified in our analysis of agent sprawl.

2. Data Governance

Here's where it gets interesting for data leaders. Mayer Brown explicitly calls out that agentic AI systems must be governed through the lens of data governance: ensuring representative datasets, minimizing bias in autonomous decisions, and — critically — applying the principle of least privilege to agent data access.

This is the dimension most AI governance conversations still miss. An AI agent is, at its core, a data consumer and data producer. If your data governance framework doesn't extend to your agents, you have a gap that no amount of model evaluation will close.

3. Legal Compliance

The framework maps agentic AI against existing regulatory obligations — the EU AI Act, Colorado AI Act, Texas Responsible AI Governance Act, GDPR, CCPA, and sector-specific regulations in healthcare, finance, employment, and critical infrastructure. The key insight: agentic AI doesn't create entirely new legal categories. It triggers existing obligations that organizations may not realize apply to autonomous systems.

For LATAM enterprises, this mapping exercise is equally urgent. Brazil's LGPD, Colombia's Law 1581, and Mexico's LFPDPPP all impose requirements on automated data processing — requirements that most organizations haven't mapped against their AI agent deployments.

4. Risk Assessments

Mayer Brown identifies seven specific risk categories for agentic AI, including erroneous autonomous actions, unauthorized decisions, biased outputs, sensitive data exposure, cascading system disruptions, decisions made without domain knowledge, and misaligned reward functions causing market harm.

What's powerful about this list is its specificity. Most enterprise risk frameworks treat AI as a generic category. Mayer Brown's assessment forces organizations to evaluate risks unique to autonomous systems — the kind of risks that don't show up in traditional model evaluation checklists.

5. Mitigation Measures

Three layers of defense: transparency (inform users they're interacting with AI, disclose capabilities and limitations, provide human contact), human oversight (define action boundaries requiring human approval, mitigate automation bias), and technical controls (strict input formats, least-privilege tool access, pre-deployment testing, real-time monitoring with intervention capability).

The emphasis on real-time intervention capability is particularly important. It's not enough to monitor agents after the fact. Organizations need the ability to stop an agent mid-execution when it deviates from its defined boundaries.

6. Accountability

Documentation of all governance practices through policies, procedures, technical documentation, and logs. Mayer Brown frames this bluntly: in a regulatory investigation, having an auditable record demonstrates reasonable action. Not having one suggests the organization only considered governance after the incident.

The Contracting Revolution: From SaaS to Services

Mayer Brown's companion paper may be even more consequential for enterprises. It argues that when organizations contract for agentic AI solutions, the traditional SaaS model — "here's the platform, you're responsible for how you use it" — is fundamentally broken.

Their proposed shift: treat agentic AI contracts like Business Process Outsourcing (BPO) agreements. The logic is compelling. When an AI agent autonomously processes invoices, handles customer inquiries, or manages supply chain decisions, the provider isn't licensing a tool. They're delivering a service. And services require:

• Defined delegation of authority — what the agent can and cannot do, with explicit escalation triggers
• Performance warranties — not just "the platform will be available," but "the service will be performed in a professional, diligent manner"
• Outcome-based SLAs — accuracy, timeliness, and quality metrics, not just uptime percentages
• Broader indemnification — if the agent makes a costly autonomous decision within its delegated scope, who bears the loss?
• Governance and audit rights — the customer's right to inspect, audit, and enforce guardrails
• Data ownership clarity — who owns the data the agent generates, transforms, and acts upon?

This reframing has immediate practical implications. If your legal team is still reviewing AI vendor contracts through a SaaS lens, they're missing the core risk: your vendor's AI agent is acting on your behalf, with your data, affecting your customers. That's a services relationship, not a software license.

What MIT Sloan Confirms: New Management Models Required

MIT Sloan Management Review's research, conducted with BCG across 1,221 global executives and an expert panel, provides the academic validation. Key findings:

69% of AI experts agree that agentic AI requires fundamentally new management approaches. The traditional management models — designed for deterministic systems and human-paced decisions — cannot handle autonomous systems that operate at machine speed with goal-oriented reasoning.

Shelley McKinley, Chief Legal Officer at GitHub, captured it precisely: "Today's workflows were not built with the speed and scale of AI in mind, so addressing gaps will require new governance models, clearer decision pathways, and redesigned processes that make it possible to trace, audit, and intervene in AI-driven decisions."

The research also surfaces a critical organizational challenge: the era of managing AI solely within IT is over. Governance must be cross-functional — IT, HR, finance, operations, and legal collaborating on a unified framework. This echoes exactly what Mayer Brown prescribes in their first pillar.

The Bridge: From Data Trust to Agent Trust

Here's the pattern we see across all three publications: the organizations that will govern AI agents successfully are the ones that already have mature data governance.

This isn't coincidental. Data governance teaches organizations the fundamental disciplines that agent governance requires:

This is the journey we describe as "From Data Trust to Agent Trust" — and it's why we believe organizations with strong data governance foundations have a 12-18 month head start in the race to govern autonomous AI.

Mayer Brown's second pillar — Data Governance — isn't just one of six components. It's the foundation that makes the other five possible. You can't assess risk on agents whose data access you don't understand. You can't demonstrate accountability without data lineage. You can't enforce boundaries without data access policies.

What CDOs and Data Leaders Should Do Now

The legal profession has spoken. The academic research confirms. The governance gap is documented and quantified. Here's the immediate action plan:

1. Map Mayer Brown's six pillars against your current AI governance. Most organizations will find they have partial coverage — model evaluation and usage policies — but critical gaps in governance team structure, agent-specific risk assessments, and accountability documentation.

2. Extend your data governance to cover AI agents. Every agent that consumes or produces data should be subject to the same governance rigor as any other data asset — ownership, access policies, quality monitoring, lineage tracking.

3. Audit your AI vendor contracts. If you're purchasing agentic AI solutions under SaaS terms, engage your legal team to evaluate the BPO-style clauses Mayer Brown recommends — especially delegation of authority, performance warranties, and data ownership.

4. Establish an Agent Registry. You can't govern what you can't see. Document every AI agent operating in your organization: its purpose, owner, data scope, action boundaries, and review date.

5. Build cross-functional governance. Follow Mayer Brown's governance team model. This isn't an IT initiative — it requires decision-makers, product teams, security, privacy, legal, and business stakeholders operating as a unified function.

The Bottom Line

When Mayer Brown publishes a governance framework, it's not a suggestion. It's a preview of what regulators, auditors, and opposing counsel will expect you to have in place when something goes wrong. When MIT Sloan confirms that 69% of experts say new management approaches are required, it's not speculation — it's consensus.

The organizations that treat these publications as early warnings will build governance infrastructure before they need it. The ones that don't will build it in response to an incident, a regulatory inquiry, or a contract dispute — at ten times the cost and with none of the strategic advantage.

Data governance took a decade to mature. Agent governance doesn't have that luxury. The agents are already deployed, the legal frameworks are being written, and the window to govern proactively is narrowing.

The question isn't whether your organization needs agent governance. Mayer Brown just answered that. The question is whether you'll build it from a position of strength — leveraging your existing data governance foundation — or scramble to retrofit it after the first autonomous decision goes wrong.

Ronald Mego is the founder of GalacticaIA, specializing in Data & AI Governance frameworks for enterprises navigating the transition from data trust to agent trust. Previously published: Agent Sprawl: The New Shadow IT Crisis and AI Workers Are Not AI Agents.

Frequently Asked Questions

What is Mayer Brown's AI agent governance framework? Mayer Brown's framework identifies six core components for governing agentic AI systems: (1) establishing an AI governance team, (2) applying data governance techniques, (3) evaluating legal compliance, (4) conducting AI risk assessments, (5) implementing mitigation measures including transparency, human oversight, and technical controls, and (6) documenting accountability through policies and audit trails.

Why does agentic AI need different governance than traditional AI? Traditional AI generates outputs for humans to interpret. Agentic AI autonomously executes tasks, makes decisions, accesses data, calls external APIs, and takes actions with real business consequences — all with limited human oversight. This autonomy requires governance controls that address delegation of authority, action boundaries, real-time intervention, and accountability for autonomous decisions.

How does data governance relate to AI agent governance? Data governance is the foundation of agent governance. Every AI agent is a data consumer and producer. The disciplines of data governance — cataloging, access control, quality monitoring, lineage tracking, and lifecycle management — map directly to the requirements of governing autonomous AI agents. Organizations with mature data governance have a significant head start.

What is the shift from SaaS to BPO contracting for AI? Mayer Brown argues that when AI agents autonomously perform tasks on a company's behalf, the relationship is a service — not a software license. This requires BPO-style contract clauses: defined delegation of authority, performance warranties, outcome-based SLAs, governance and audit rights, and clear data ownership terms.

What regulations apply to AI agents? AI agents can trigger obligations under the EU AI Act, Colorado AI Act, Texas Responsible AI Governance Act, GDPR, CCPA, LGPD, and sector-specific regulations in healthcare, finance, employment, and critical infrastructure. Organizations must map their agents' use cases against applicable regulations before deployment.

Sources

1. Mayer Brown. "Governance of Agentic Artificial Intelligence Systems." February 2026. Link
2. Mayer Brown. "Contracting for Agentic AI Solutions: Shifting the Model from SaaS to Services." February 2026. Link
3. MIT Sloan Management Review & BCG. "Agentic AI at Scale: Redefining Management for a Superhuman Workforce." 2026. Link
4. MIT Sloan Management Review. "The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI." November 2025. Link
5. MIT AI Agent Index. Link