How Sullivan & Cromwell Can Transform Capital Markets Legal Work and M&A Advisory with Agentic AI

Agentic AI for capital markets and M&A is quickly becoming the difference between experimenting with generative tools and building a repeatable deal execution system. In a practice where timelines compress, documents multiply, and precision is non-negotiable, the biggest gains won’t come from prettier drafts. They’ll come from systems that can plan work, move it through the right tools, verify it against sources, and escalate to lawyers at the right moments.

For a firm like Sullivan & Cromwell, the opportunity is especially clear: capital markets and M&A matters are structured, checklist-driven, and document-heavy. That combination makes them ideal for legal AI agents that can reduce rework, catch inconsistencies earlier, and keep the deal team focused on judgment, negotiation, and client counseling.

What follows is a practical look at what agentic AI in legal services can do across the deal lifecycle, what a realistic architecture looks like, and how to deploy it safely with the governance standards BigLaw requires.

What “Agentic AI” Means in a BigLaw Context (and what it doesn’t)

Definition: agentic AI vs. copilots vs. traditional automation

Traditional automation follows rigid rules. Copilots generate helpful text when a user prompts them. Agentic AI for capital markets and M&A goes a step further: it can take a goal (for example, “prepare the S-1 for filing readiness” or “produce a first-pass diligence issue list”), break that goal into steps, use tools to execute those steps, verify outputs, and then escalate decisions to humans.

In legal practice, that last piece matters. Agentic AI isn’t “autonomous lawyering.” It’s human-in-the-loop orchestration built around controlled actions, clear permissions, and verifiable outputs. Done well, it reduces the amount of time lawyers spend hunting for information, reconciling versions, and doing repetitive checks across dense documents.

A concise way to recognize legal AI agents in practice is that they can:

• Plan a multi-step workflow from a defined objective

• Retrieve and use firm-approved knowledge (precedent, playbooks, checklists)

• Execute actions across tools (document systems, trackers, redlines, email summaries)

• Verify claims against sources before presenting conclusions

• Escalate uncertainty or high-risk decisions to a lawyer

• Log actions and approvals for auditability

This model aligns with what legal teams actually need: precision, speed, and accuracy without sacrificing defensibility. In day-to-day execution, AI agents can support attorneys by extracting obligations, comparing agreements to standard templates, validating compliance requirements, and summarizing key details across contracts, emails, and evidence while keeping the lawyer in control.

Why capital markets + M&A are ideal for agentic systems

Capital markets and M&A matters tend to follow repeatable patterns:

• Standard deliverables (filings, disclosure schedules, closing sets)

• Known timelines and dependencies (board approvals, comfort letters, auditor signoffs)

• Heavy reliance on precedent and defined “house views”

• High coordination overhead across internal teams and counterparties

This is exactly where agentic AI for capital markets and M&A shines. It can take structured workflows and reduce friction inside them: fewer missed checklist items, fewer late-stage consistency fixes, and faster movement from draft to review-ready work product.

The Capital Markets Workflow: Where Agentic AI Can Drive Step-Change Gains

Capital markets work rewards teams that manage detail at speed. The filings are long, the disclosure is interdependent, and small inconsistencies create outsized review cycles. AI for capital markets legal work becomes most valuable when it functions as a continuous quality and coordination layer rather than a one-time drafting tool.

IPO and equity offerings: from drafting to filing readiness

An IPO is a machine of parallel workstreams: business description, risk factors, MD&A, capitalization, underwriting, exhibits, and more. Agentic AI for capital markets and M&A can support that machine in ways that feel like adding a tireless senior coordinator plus a meticulous checker.

Practical capabilities for IPO readiness workflow support include:

1. Draft scaffolding from approved sources The agent can build section outlines and initial drafting frameworks using only firm-approved precedent, prior filings, and client-provided source materials. This is not about inventing disclosure; it’s about accelerating the assembly of a complete, reviewable first version.

2. Consistency checks across the S-1 A single fact change can ripple across risk factors, business, MD&A, and notes. An agent can track key data points and defined terms and flag mismatches, outdated references, or conflicting statements.

3. Comment-letter workflow assistance When the SEC issues comments, the process becomes part legal analysis and part project management. An agent can:

4. Filing readiness coordination Agents can manage checklists, reminders, and dependencies while keeping lawyers in control of substantive decisions. This is a form of deal lifecycle automation applied to the IPO path: fewer dropped balls, clearer handoffs, and less last-minute scrambling.

Ongoing reporting (10-K/10-Q/8-K): continuous disclosure quality control

Periodic reporting is where “diff-aware” systems pay for themselves. Many issues aren’t novel legal questions; they’re inconsistencies introduced by iterative edits, version drift, or rushed updates.

Agentic AI for capital markets and M&A can support SEC filing automation for 10-K, 10-Q, and 8-K workstreams by:

• Running diff-based comparisons to prior filings to flag unexpected language shifts

• Identifying missing updates across related sections (for example, risk factor updates that don’t match MD&A trends)

• Checking for definitional consistency (defined terms, exhibit references, cross-references)

• Surfacing “where else does this appear?” for key facts and metrics

Even when the agent doesn’t “decide” what should be disclosed, it can dramatically shorten the time it takes a team to find where disclosure needs a lawyer’s judgment.

Debt offerings + indentures: accelerating issue-spotting and precedent alignment

Debt deals are full of patterns: covenants, events of default, baskets, carveouts, and negotiated fallback positions. A merger agreement review AI toolset gets attention, but in many firms, the same leverage exists in debt documentation.

Agentic AI for capital markets and M&A can:

• Compare a draft indenture or purchase agreement against house precedent clause-by-clause

• Flag deviations and classify them (market move vs. counterparty push vs. drafting artifact)

• Suggest fallback language options based on internal playbooks

• Create a negotiation-ready issue list that ties each deviation to the relevant precedent and a recommended position

This kind of precedent alignment is where legal AI agents are at their best: fast, thorough, and grounded in approved sources.

The M&A Advisory Workflow: Agentic AI as a Deal Team Multiplier

M&A is where speed and judgment collide. The goal isn’t to read fewer documents; it’s to surface the right issues, early, with enough context to act. AI for M&A advisory becomes valuable when it reduces senior review burden without turning diligence into a black box.

Diligence triage that actually reduces senior review load

Due diligence automation often fails when it outputs generic summaries instead of structured, citation-backed findings. The bar in M&A is higher: the team needs an issue list that points to the exact clause, the exact exception, and the reason it matters.

A practical agentic diligence flow looks like this:

1. Intake and classification The agent ingests documents and classifies them into categories such as:

2. Extraction into structured fields Instead of paragraph summaries, it extracts obligations, termination rights, assignment/change of control, exclusivity, non-competes, SLAs, indemnities, and other target fields that match the deal’s diligence checklist.

3. Risk scoring with explainable support The agent can prioritize review queues, but it must show its work: quotes, page references, and a short explanation tied to the diligence checklist.

4. Gap detection and follow-ups Based on what’s missing (for example, no change-of-control language surfaced for a top revenue contract), the agent can draft follow-up requests for the diligence Q&A list.

This is where due diligence automation becomes credible: it doesn’t replace the lawyer’s analysis, but it cuts down the time spent getting to the relevant text.

Drafting and negotiation support for core deal documents

M&A documentation is where small inconsistencies create big downstream problems: defined terms drift, disclosure schedules lag, and negotiated changes don’t propagate. Agentic AI for capital markets and M&A can provide drafting and negotiation support across the core documents:

• Merger agreement / SPA issue spotting The agent reviews for:

• Negotiation prep briefings Before a call, the agent can assemble a redline briefing that includes:

This is where merger agreement review AI becomes more than a clause checker. It becomes a deal-team memory system that keeps positions coherent and reduces “reinventing the wheel” across matters.

Closing management and post-signing execution

Closings are a coordination test: conditions, deliverables, signatures, certificates, third-party consents, and last-minute updates. Deal lifecycle automation powered by agents can keep the process clean without taking control away from lawyers.

High-impact agentic support includes:

• Conditions checklist tracking with responsible parties and evidence links

• Bringdown and closing certificate alignment checks against diligence findings and late-stage disclosure updates

• Post-close covenant tracking (notices, filings, earn-out milestones, integration obligations)

A well-designed agent doesn’t just remind people of deadlines; it keeps the closing set coherent and highlights where the documents don’t match the deal reality.

A Practical Agentic AI Architecture for Sullivan & Cromwell

Enterprise-grade agentic AI in legal services requires more than a model in a chat box. It needs a controlled system with retrieval, tooling, verification, and audit.

The “agent stack” (conceptual blueprint)

A realistic architecture for agentic AI for capital markets and M&A typically includes:

• Orchestrator Plans tasks, routes work, and manages multi-step workflows. It also decides when to stop and escalate.

• Retrieval layer Secure access to a firm’s document management system, precedent bank, clause library, and matter workspaces with strict permissions.

• Tools layer Connectors to the systems lawyers actually use: Word workflows, DMS, redlining processes, e-sign platforms, checklists, and trackers.

• Verification layer Enforces rules like quote-to-source requirements, definitional consistency checks, numeric sanity checks, and section-to-section consistency tests.

• Audit layer Logs actions, versions, approvals, and data lineage so the firm can prove what happened, when, and why.

This is the foundation that separates an “AI drafting helper” from a governed deal execution system.

Data sources that matter most (and how to segment them)

In capital markets and M&A, the most valuable data is also the most sensitive. Segmentation is the difference between a useful system and an unusable risk.

High-leverage sources include:

• Firm precedent, sanitized and permissioned The best training data is the firm’s own work product, but it must be carefully controlled by matter, client, and practice group boundaries.

• Client-specific playbooks Negotiation positions, risk tolerances, disclosure preferences, and client-specific language.

• Knowledge management assets Checklists, training notes, model forms, internal memos, and practice guidance.

• Public materials SEC filings, press releases, merger proxies, and other market disclosures that can support benchmarking and pattern recognition without privilege risk.

Build vs. buy: what should be bespoke for S&C

For a firm like Sullivan & Cromwell, differentiation is rarely in generic OCR or basic extraction. The bespoke advantage is in firm-specific precedent, playbooks, and verification standards.

A useful rule of thumb:

• Bespoke is worth it when the “house view” matters Precedent alignment, fallback positions, drafting patterns, and risk framing.

• Commodity is fine when it’s infrastructure OCR, basic document ingestion, scheduling, and generic connector plumbing.

The goal is not to build everything. It’s to build the components that encode the firm’s judgment and quality constraints.

Governance, Confidentiality, and Professional Responsibility (the “can we do this safely?” section)

In capital markets and M&A, the real question is not whether agentic AI can help. It’s whether it can help under the confidentiality, privilege, and professional responsibility standards clients expect.

Confidentiality and privilege safeguards

Law firm AI governance needs to start with access boundaries, not model selection. Core controls typically include:

• Matter-level permissioning and strict access controls

• Data isolation by client and team, with least-privilege principles

• Redaction and sanitization workflows for any reuse of prior materials

• Clear policies on what can be stored, for how long, and where

For many firms, deployment options matter as much as features. Depending on the risk profile, secure options may include private cloud, on-prem, VPC deployments, and confidential computing for legal AI use cases where additional isolation is needed.

Hallucination-resistant legal workflows

The most effective defense against hallucinations is workflow design. In other words: make it impossible for the system to succeed unless it shows its work.

Practical controls include:

• No-citation, no-claim outputs for diligence findings, disclosure assertions, and issue lists

• Quote-to-source requirements for any extracted obligation or risk statement

• Numerical checks for metrics that appear in multiple places

• Defined-term and cross-reference checks across documents

• Mandatory human approval for external-facing filings, responses, and client deliverables

This is how legal AI agents become trustworthy: not because they never err, but because the system forces errors to surface before they become work product.

Model risk management and auditability

Model risk management for law firms should feel familiar: versioning, audit trails, incident response, and vendor diligence. Key elements include:

• Logging of outputs, prompts/instructions, retrieval sources, and approvals

• Version control for models and agent workflows so changes are traceable

• Rollback plans when behavior changes unexpectedly

• Vendor due diligence and contractual protections aligned to client expectations

A firm doesn’t need perfect automation. It needs defensible automation.

Checklist: 10 governance controls for agentic AI in BigLaw

1. Matter-level access controls and data isolation

2. Least-privilege permissions to tools and repositories

3. Explicit data retention policies aligned to firm requirements

4. No training on client data unless explicitly approved and governed

5. Quote-to-source requirement for legal conclusions and issue lists

6. Human approval gates for external-facing outputs

7. Versioning for models, prompts/instructions, and agent workflows

8. Continuous monitoring and incident response procedures

9. Secure deployment options for sensitive matters (private cloud/VPC/on-prem as needed)

10. Periodic audits of output quality, bias, and failure modes

Implementation Roadmap for a Firm Like S&C (90 days → 12 months)

Transformation happens when agentic AI for capital markets and M&A is deployed in phases, each with measurable outcomes and controlled expansion.

Phase 1 (0–90 days): pilot use cases with measurable outcomes

Choose 2–3 workflows that are frequent, painful, and measurable:

• SEC filing consistency checks (S-1, 10-K, 10-Q, 8-K)

• Diligence triage and issue list drafting with citations

• Closing checklist automation and deliverables tracking

Define success in operational terms, not hype:

• Cycle time reductions (draft-to-review, review-to-file)

• Rework rate reductions (late-stage fixes, partner “send it back” loops)

• Partner review time improvements

• Error rate reductions (missed cross-references, definitional drift)

This phase proves whether agentic AI in legal services can meet firm standards.

Phase 2 (3–6 months): expand to connected workflows

Once the pilots work, connect the workflows so output in one stage becomes reliable input for the next:

• Integrate with document systems and matter workspaces

• Create firm-approved agent playbooks and standardized instruction patterns

• Build repeatable verification gates (citations, rule checks, consistency tests)

• Launch training so teams understand what the agents do, and where they must intervene

This is where the firm starts to see compounding returns.

Phase 3 (6–12 months): standardize and scale

Scaling requires governance as much as technology:

• Establish a Center of Excellence (practice leads, KM, IT, risk)

• Create an “AI suitability” rubric for matter intake and tooling decisions

• Standardize dashboards that track work status, risk flags, and approvals in a permissioned way

At this stage, the firm moves from pilots to a platform approach.

Measuring ROI Without Compromising Quality

The most persuasive ROI story in BigLaw is not “we drafted faster.” It’s “we caught issues earlier, reduced rework, and improved consistency under intense time pressure.”

Metrics that matter in capital markets

AI for capital markets legal work can be evaluated with practical, matter-level measures:

• Reduced draft-to-file timelines

• Fewer late-stage disclosure inconsistencies

• Faster comment-letter response cycles, or higher quality responses with fewer revisions

• Reduced time spent on cross-checks and reference reconciliation

Metrics that matter in M&A

For AI for M&A advisory workflows, strong metrics include:

• Faster diligence turnaround without sacrificing issue quality

• Better prioritization (fewer “noise issues,” more decision-relevant findings)

• Faster first drafts of issue lists, disclosure schedule frameworks, and closing deliverables

• Cleaner negotiation rounds due to improved consistency and precedent alignment

Client value narrative (without overpromising)

Clients tend to value outcomes they can feel:

• More predictable timelines and fewer last-minute surprises

• More consistent work product across teams and matters

• Better leverage of senior lawyer time on judgment calls, not manual checks

• Stronger defensibility through audit trails and source-grounded outputs

The Competitive Angle: What Agentic AI Enables That “GenAI Drafting” Doesn’t

From drafting assistance to execution systems

Drafting tools help produce text. Agentic AI for capital markets and M&A helps execute a matter. The difference is orchestration plus verification:

• Work moves through defined steps

• Outputs are checked against sources and rules

• Tasks are routed to the right people at the right time

• Everything is logged for defensibility

That’s how firms reduce operational risk while increasing throughput.

Differentiation in client experience

When agentic systems work, clients notice:

• Faster turnaround on routine deliverables

• Clearer status reporting because checklists and trackers stay updated

• Fewer “we need to fix this before closing” moments

• More consistent positions across deals due to playbook enforcement

This is a real competitive advantage in high-stakes capital markets and M&A work.

Potential pitfalls competitors underestimate

Agentic AI in legal services fails most often for predictable reasons:

• Integration complexity with legacy document management systems

• Governance overhead that’s treated as an afterthought

• Poor-quality precedent libraries that scale bad templates and outdated language

• Lack of verification gates, leading to untrustworthy outputs

• Change management gaps, where lawyers don’t know when to trust the system

Avoiding these pitfalls is less about ambition and more about disciplined engineering and governance.

Conclusion: A Realistic Path to Agentic Advantage at S&C

Agentic AI for capital markets and M&A is most powerful when it’s treated as a governed execution layer, not a drafting novelty. For Sullivan & Cromwell, the most ready-now transformations are the ones that combine repeatability with strict verification:

• Filing consistency and verification for SEC documents

• Diligence triage and issue lists with citations and structured outputs

• Closing execution automation that keeps deliverables coherent and on track

The practical path forward is to start with a small set of workflows, define success metrics that reflect both speed and quality, and scale only after governance and auditability are proven in real matters.

If you’re evaluating how to deploy agentic AI in legal services safely and effectively, book a StackAI demo: https://www.stack-ai.com/demo