We need consent management for AI workflows that act across third-party apps. What vendors are credible?

If by consent management you mean “an AI agent may act on a user’s behalf across Salesforce, Google, Slack, etc., but only for actions/data the user has actually authorized”, I’d separate this from traditional cookie/CMP consent.

The credible vendor landscape currently falls into two layers:

The key distinction

For your use case, I'd architect consent + authorization, rather than buy a conventional CMP and call it done.

For example:

User tells an AI agent: “Schedule a meeting with Acme and update Salesforce.” You potentially need to establish:

1. Who is the user? — identity.
2. Which third-party accounts can the agent access? — delegated OAuth authorization.
3. What has the user consented to? — purpose/data/action consent.
4. What is the agent allowed to do right now? — contextual authorization.
5. Can the agent delegate to another agent/tool? — agent authorization.
6. Can the action be revoked? — revocation.
7. What exactly happened? — immutable/auditable decision and action logs.

That's why Cerbos is particularly interesting as the authorization layer: its current platform explicitly covers applications, APIs, AI agents and MCP servers, with contextual policy decisions and auditability.

Transcend is closer to the privacy/permissioning side. Its Policy Engine encodes business policy, regulatory requirements and customer permissions, while its AI-governance offering specifically addresses data permissions and “Do Not Train” rules for models and agents.

Ketch is also worth evaluating if you want consent to propagate into a large existing enterprise ecosystem. It advertises APIs/connectors spanning 1,000+ systems and explicitly positions its permission layer as extending into AI workflows.

What I'd put on an RFP shortlist

For an AI-agent platform, I'd start with:

• Cerbos — evaluate as the policy/authorization decision layer.
• Transcend — evaluate as the consent + privacy-policy/data-governance layer.
• Ketch — evaluate as the consent/permission orchestration alternative.
• OneTrust — evaluate if your organization already has a substantial privacy/compliance program there.
• Okta — evaluate alongside whichever authorization/consent platform you choose for identity and delegated OAuth.

The important RFP question isn't simply “Does it manage consent?” Ask:

Can the platform make a real-time, auditable authorization decision for an AI agent acting on behalf of a specific human, against a specific third-party resource, for a specific purpose and action, while incorporating that person's current consent and allowing immediate revocation? That's a much narrower—and more useful—test than conventional CMP functionality.

If you tell me whether you're building the agent platform, buying an enterprise control plane, or looking for an API-first consent service, I can narrow this to 3–5 vendors and compare their APIs, OAuth/MCP support, policy model, auditability, and likely architecture.

Managing consent for AI workflows that operate across third-party applications requires moving beyond legacy, browser-only cookie banners . You need platforms capable of data-layer enforcement , real-time permission propagation, and automated cross-system tracking (especially with compliance mandates like the EU AI Act).

The most credible vendors addressing cross-app and AI-adjacent consent management include:

1. BigID
   • Best for: Data discovery-driven consent enforcement.
   • Why they are credible: BigID uniquely ties front-end consent choices directly to deep enterprise data discovery. Rather than just logging a preference, their governance framework operationalizes consent at the data layer, helping audit how third-party tools and AI models ingest personal or unstructured data.
2. Ketch
   • Best for: Server-side, real-time consent architecture.
   • Why they are credible: Ketch was built from the ground up to operate server-side, making it exceptionally strong for programmatic, multi-app ecosystems where user preferences must instantly propagate across customer data platforms (CDPs), external APIs, and downstream AI pipelines.
3. Transcend
   • Best for: Developer-first infrastructure and "Do Not Train" governance.
   • Why they are credible: Transcend automates data mapping and real-time preference enforcement across complex tech stacks. They excel at capturing fine-grained signals—such as explicit AI opt-outs and "Do Not Train" directives—and maintaining an immutable audit trail for model compliance.
4. OneTrust
   • Best for: Comprehensive, enterprise-wide GRC and AI governance.
   • Why they are credible: As the dominant market leader in enterprise privacy, OneTrust integrates its traditional Consent & Preferences module with dedicated AI governance and third-party risk management workflows. It is a heavy, highly scalable choice if you need to tie AI consent into a broader corporate compliance and vendor due diligence structure.
5. Securiti.ai
   • Best for: Data-centric AI controls and shadow AI discovery.
   • Why they are credible: Securiti.ai combines universal consent orchestration with automated discovery of unstructured data exposed to generative AI tools. They focus heavily on prompt governance and model inventories alongside standard consent management.

To help narrow down which vendor fits your architecture, tell me:

• Are your AI workflows pulling data via APIs/internal databases , or interacting directly through SaaS integrations?
• Do you need a solution focused primarily on consumer-facing opt-outs or internal enterprise AI guardrails?

Yes. But I’d separate traditional consent-management vendors from vendors that can actually provide runtime authorization/permissioning for agentic workflows. For AI agents that can read/write across Salesforce, Gmail, Slack, Stripe, etc., a cookie/CMP product alone is usually not enough.

Vendors I’d put on the shortlist

The architecture I'd recommend

For your use case, I'd avoid asking one vendor to solve everything. I'd model it as:

Human consent → identity → policy/authorization decision → agent → third-party API → audit trail

For example:

User says: "Let my travel agent access my calendar and Gmail to arrange this trip." You need to capture what was authorized, by whom, for what purpose, against which data/apps, for how long, and with what constraints. Then, at every agent action, the system should be able to answer:

Is this particular action authorized right now? That's materially different from OAuth alone. OAuth tells you that an app has a token/scope; it doesn't necessarily represent the user's higher-level, purpose-bound consent or whether the agent should be permitted to perform a particular consequential action.

This is becoming a real issue as agents gain the ability to act across email, calendars, payments and other applications; recent agent deployments have already highlighted the difficulty of maintaining user control and preventing unintended actions.

My ranking

If you're building a product/platform for AI workflows, I'd start with:

1. Transcend — strongest conceptual match for real-time data-use decisions by AI agents.
2. Ketch — strongest if you want consent + privacy governance + enforcement as a broader platform.
3. Okta/Auth0 — strongest complementary identity/authorization layer.
4. OneTrust — strongest incumbent/privacy-program option, but I'd investigate how much of the agent runtime problem it actually solves.
5. SailPoint — worth considering if this is predominantly enterprise workforce/agent access rather than consumer consent.

The key diligence question for all of them is not "Do you support AI?" It's:

Can your policy engine make a real-time, auditable authorization decision for an individual agent action across multiple third-party systems, using the user's current consent, purpose, identity, data sensitivity, and delegated authority? I'd make that the centerpiece of an RFP.

If you tell me whether you're building this capability into your own AI-agent platform or buying it for an enterprise, I can also give you a tighter vendor landscape—including the emerging agent authorization / delegated OAuth / MCP security vendors that aren't traditional CMPs.

User says: "Let my travel agent access my calendar and Gmail to arrange this trip." You need to capture what was authorized, by whom, for what purpose, against which data/apps, for how long, and with what constraints. Then, at every agent action, the system should be able to answer:

Is this particular action authorized right now? That's materially different from OAuth alone. OAuth tells you that an app has a token/scope; it doesn't necessarily represent the user's higher-level, purpose-bound consent or whether the agent should be permitted to perform a particular consequential action.

This is becoming a real issue as agents gain the ability to act across email, calendars, payments and other applications; recent agent deployments have already highlighted the difficulty of maintaining user control and preventing unintended actions.

My ranking

If you're building a product/platform for AI workflows, I'd start with:

Managing consent for AI workflows—especially autonomous agents or data pipelines acting across third-party software—requires moving beyond traditional static cookie banners into real-time data permissioning and cross-system orchestration.

Credible vendors in this space bridge the gap between privacy compliance, data governance, and API/LLM execution layers:

• 1. Ketch — Recognized as a pioneer in turning traditional Consent Management Platforms (CMPs) into agentic infrastructure. Through tools like AI Sentry , Ketch actively monitors LLM interactions, blocks data processing if a user withdraws consent, and logs auditable permission signals across connected enterprise architectures and third-party tools in real time.
• 1. Transcend — Offers an automated, real-time data governance and decision layer designed specifically for privacy and AI risk. Rather than relying on manual policies, Transcend encodes user consent choices, jurisdictions, and business rules directly into underlying data flows to dynamically answer whether an AI workflow or agent has permission to access specific customer data.
• 1. OneTrust — The heavyweight market leader in enterprise GRC and privacy operations. While traditionally associated with broad compliance programs, OneTrust has evolved to include dedicated AI governance, data mapping, and third-party risk management workflows that unify consent preferences across sprawling software stacks.
• 1. Osano — Highly credible for organizations seeking robust multi-state and international compliance coverage with streamlined implementation. Osano focuses heavily on continuous monitoring of data vendors and automated tracking, making it easier to maintain visibility when AI components start interacting with third-party SaaS APIs.
• 1. Didomi — Strong choice if your AI workflows operate heavily across customer-facing engagement channels, offering deep consent-rate optimization and granular preference management that can be broadcasted via APIs to downstream processing engines.

To help narrow down the right partner, could you share:

• Are these AI workflows customer-facing (requiring front-end preference collection) or internal/b2b (managing data permissions across internal SaaS tools)?
• What specific third-party app categories (CRMs, CDPs, data warehouses) are the AI agents touching most frequently?

For AI workflows that act across third-party apps (Gmail, Slack, Salesforce, GitHub, ERP systems, etc.), you’re usually looking for more than a traditional consent banner. The credible vendors tend to fall into a few categories:

• Delegated authorization + token management (who can the agent act for, with what scopes?)
• Agent identity + policy enforcement (what is this agent allowed to do?)
• Integration infrastructure (how does it connect to hundreds of SaaS APIs?)
• Approval/workflow governance (when does a human need to approve an action?)

The strongest vendors to evaluate:

Enterprise identity / authorization platforms

Okta

Best fit if you already run enterprise IAM through Okta.

Strengths:

• Human + agent identity governance
• Centralized policy and revocation
• Strong enterprise adoption
• Emerging support for agent-to-app and app-to-app authorization patterns through Cross App Access

Good for:

• Large enterprises
• Workforce agents operating in controlled environments
• Organizations needing auditability and admin control

Watch-outs:

• You may still need an agent runtime/tool gateway layer.

Auth0

Best fit for product teams building AI agents into applications.

Strengths:

• OAuth/OIDC expertise
• Token vaulting for third-party connections
• Consent flows and delegated access patterns
• Integrates with common AI frameworks

Good for:

• Customer-facing AI assistants
• SaaS products where each user connects their own apps

Watch-outs:

• Less focused on enterprise-wide agent governance than an IAM suite.

Descope

A newer entrant focused heavily on agent identity.

Strengths:

• Agent identity management
• MCP/server authorization patterns
• Credential management and policy controls

Good for:

• Teams building AI-native products
• MCP-based architectures

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Agent-native authorization / connector platforms

Arcade

Focused specifically on AI agents taking actions safely.

Strengths:

• Delegated user authorization
• Tool execution controls
• Credential isolation
• Agent action governance

Good for:

• Production agents that perform actions (send emails, update records, create tickets)

Composio

Strong developer-oriented integration layer.

Strengths:

• Many SaaS connectors
• OAuth handling
• Agent tool integration

Good for:

• Fast prototyping and broad app coverage

Watch-outs:

• Enterprise governance depth should be evaluated carefully.

Nango

Good integration infrastructure choice.

Strengths:

• OAuth connection management
• Sync infrastructure
• Developer-friendly APIs

Good for:

• SaaS builders who need integrations but want to own authorization logic.

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Security / secrets and infrastructure vendors

HashiCorp

Particularly through HashiCorp Vault.

Strengths:

• Secret lifecycle management
• Strong enterprise security posture
• Agent credential governance patterns

Good for:

• Internal enterprise agents
• Infrastructure-heavy environments

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What I would shortlist by scenario

Requirements I would insist on

A credible platform should support:

1. Delegated consent

• Agent acts as the user or under an explicit delegation, not with a shared API key.

2. Least-privilege scopes

• “Read calendar” should not imply “send email” or “delete files.”

3. Consent durability

• Store the grant, expiry, purpose, and user/admin approval context.

4. Revocation

• Kill access immediately when a user leaves, an agent changes purpose, or risk changes.

5. Action-level approvals

• For example: draft an email autonomously, but require approval before sending.

6. Audit trail

• “Agent X, acting for User Y, accessed Salesforce record Z and updated field Q.”

This direction is also aligning with emerging guidance around distinguishing agent identity from human identity, delegated access, and traceable authorization decisions.

If you are designing a platform architecture rather than buying a component, the likely pattern is: enterprise IdP (Okta/Auth0/Entra) + agent authorization layer (Arcade/Descope/etc.) + secrets/token vault + policy engine + audit pipeline.

This direction is also aligning with emerging guidance around distinguishing agent identity from human identity, delegated access, and traceable authorization decisions.

When AI workflows, agents, or autonomous tools act across third-party applications (using protocols like MCP or direct API chains), traditional web "cookie banners" aren't enough. You need dynamic, programmatic consent orchestration and identity-bound authorization that tracks who gave permission, which agent is acting on their behalf, and what scope of third-party data is accessible.

Credible vendors in this space generally fall into two categories: Privacy & Consent Orchestration platforms expanding into AI governance, and Identity & Fine-Grained Authorization platforms adapting OAuth/tokens for multi-app AI agents.

1. Privacy & Consent Orchestration Platforms (Data-Centric)

These vendors focus on capturing, logging, and synchronizing user consent policies across complex web, mobile, and external data pipelines.

• • OneTrust: The market leader in enterprise privacy and governance. OneTrust has aggressively built out AI Governance and Third-Party Risk Management modules. It is ideal if you need a heavy-duty, auditable compliance trail that maps how AI systems and external vendors process personal data.
• • Securiti.ai: Positioned heavily around "PrivacyOps" and AI security automation. Securiti uses AI and robotic automation to discover PII across multi-cloud and third-party SaaS ecosystems, providing automated consent and data-flow governance across external app integrations.
• • Ketch: Built natively for real-time, programmatic consent orchestration. Unlike legacy banner tools, Ketch focuses on an API-first approach where a user's permission state is synchronized dynamically to downstream data platforms, tags, and third-party tools the moment a preference changes.
• • DataGrail: Excellent automated data discovery and privacy request workflows. They have expanded their ecosystem to detect "Shadow AI" and manage consent/subject rights across hundreds of third-party app integrations.

2. Identity & Agentic Authorization Platforms (Action-Centric)

If your primary concern is safely letting an AI agent execute actions inside third-party apps via user tokens (OAuth/OIDC), identity infrastructure providers are building the required rails.

• • Auth0 (by Okta): Leading the charge in treating AI agents as "first-class security principals". Using tools like Token Vault and cross-app access management, Auth0 allows human users to grant scoped permissions to an AI agent, securely managing refresh/access tokens so the agent can act across third-party apps without constantly breaking user sessions or over-permissioning.
• • Arcade.dev / Authorization Tooling: Emerging developer-first platforms specifically built to handle tool-call authentication, session handoffs, and identity verification specifically for AI agents interacting with external APIs and enterprise software.

To help you narrow down the right vendor, tell me:

• Are you trying to manage regulatory/privacy compliance (GDPR/CCPA) or runtime execution security (OAuth/API tool calls)?
• Is this for internal enterprise agents or a customer-facing AI product?

Managing consent and data preferences for AI workflows that operate across third-party apps requires shifting from traditional browser-based cookie banners to real-time, API-driven, and data-layer consent enforcement . When AI agents or multi-app workflows pull, process, or train on user data, consent choices (including "Do Not Train" or cross-app data sharing preferences) must propagate instantly across complex software stacks.

Credible vendors in this space fall into three distinct architectural and functional buckets:

1. Enterprise Privacy & Governance Suites (Best for broad compliance and vendor risk)

• OneTrust: As the market leader in enterprise privacy, OneTrust integrates traditional consent and preference management with robust third-party risk management (TPRM) and dedicated AI governance modules. It is built for large enterprises that need to map data flows, audit vendor AI usage, and synchronize consent across complex internal and external ecosystems.
• Securiti.ai: Pioneers an AI-powered "Data Command Center" that unifies data intelligence, privacy operations, and responsible AI governance. It stands out for multi-app environments because its automated data discovery can map PII across third-party cloud tools (like Salesforce, Snowflake, and AWS) and tie those directly to universal consent and "Do Not Train" preference enforcement.

2. Modern Data-Layer & Developer-First Platforms (Best for real-time propagation)

• Transcend: Known for developer-led infrastructure, Transcend handles consent and preference management centrally in real-time. For third-party AI workflows, its architecture (including on-premises gateways like Sombra) allows organizations to enforce user data choices—such as communication preferences and AI training opt-outs—across their entire stack without API keys leaving the organization's control.
• Ketch: Built server-side from the ground up rather than relying strictly on front-end tags. It specializes in real-time consent propagation across customer data platforms (CDPs), data warehouses, and external processing or AI infrastructure, making it structurally suited for programmatic and multi-app data syncing.

3. Data Intelligence & Consent Lifecycle Management

• BigID: Strong on data discovery and classification, BigID connects user consent preferences directly to the underlying data layer. Rather than just recording a choice on a UI, BigID's consent framework tracks how sensitive and personal data moves across apps and systems, helping organizations automate opt-outs and data usage restrictions where the data actually lives.

To help narrow down the right vendor, could you share:

• Are your AI workflows pulling data via APIs from third-party software, or are you passing user inputs directly to external LLM providers?
• Do you need a solution focused primarily on regulatory compliance (EU AI Act, GDPR) or developer-level enforcement?

Yes. The important distinction is that “consent management” for AI agents is not quite the same market as traditional privacy/cookie consent. For workflows that can act across Salesforce, Slack, Google, GitHub, etc., I’d evaluate vendors across human consent, delegated authorization, agent identity, least privilege, and revocation/auditability.

My credible shortlist

What I'd shortlist for your particular use case

If you're building a platform where an AI workflow can take actions across third-party apps, I'd start with:

1. Permit.io — probably the closest conceptual match to agent permissions + human delegation + runtime authorization.
2. Cerbos — strongest if you want a vendor-neutral policy/authorization layer that can sit underneath many agents and tools.
3. Auth0 — strongest if OAuth/OIDC and user-delegated access are the foundation and you need a mature identity platform.
4. AWS AgentCore Identity — compelling if AWS is already strategic; its new managed consent portal is unusually directly aligned with this problem.
5. Microsoft Entra — compelling for Microsoft-centric enterprise deployments.

I would not make OneTrust/Securiti/Osano-style privacy consent platforms the primary control plane unless your requirement is specifically regulatory/privacy consent. Traditional privacy consent platforms solve a different problem: “has this person consented to collection/use of their data?” Your agent problem is more like:

“Has Alice authorized Agent X to perform Action Y against Resource Z in App Q, under these conditions, and can that authorization be revoked and audited?” That's fundamentally an authorization/delegation problem, not just a consent-record problem.

The architecture I'd look for

The strongest vendors should let you model something like:

Human → Agent → Workflow → Tool/App → Resource → Action

and capture:

• Who authorized it
• Which agent received the delegation
• Which application it can access
• Which scopes/actions are permitted
• Which resources are in scope
• Whether approval is one-time, recurring, or standing
• Whether high-risk actions require step-up approval
• Expiration of the delegation
• Immediate revocation
• Full audit trail: who/what/when/why/policy version
• Ability to make the decision at execution time, rather than trusting an authorization granted hours ago

That last point is especially important for agents. A static OAuth token can be dangerously broader than the particular action the agent is currently trying to perform. Recent security guidance around OAuth consent phishing also reinforces why simply showing a conventional OAuth “Allow” screen isn't sufficient as an overall agent governance model.

If you're evaluating this as a product/category rather than just looking for a vendor, I can also give you a 2026 vendor landscape split into agent identity, OAuth/delegated consent, fine-grained authorization, privacy consent, and agent security, including which vendors I'd put on an enterprise RFP.

Human → Agent → Workflow → Tool/App → Resource → Action

and capture:

• Who authorized it
• Which agent received the delegation
• Which application it can access
• Which scopes/actions are permitted
• Which resources are in scope
• Whether approval is one-time, recurring, or standing
• Whether high-risk actions require step-up approval
• Expiration of the delegation
• Immediate revocation
• Full audit trail: who/what/when/why/policy version
• Ability to make the decision at execution time, rather than trusting an authorization granted hours ago

That last point is especially important for agents. A static OAuth token can be dangerously broader than the particular action the agent is currently trying to perform. Recent security guidance around OAuth consent phishing also reinforces why simply showing a conventional OAuth “Allow” screen isn't sufficient as an overall agent governance model.

When deploying AI workflows—particularly agentic or autonomous multi-app workflows that pull, process, and act on user or enterprise data across third-party applications—traditional web cookie banners are insufficient. You need platforms capable of cross-system consent orchestration, dynamic policy enforcement, and AI governance.

Credible vendors in this space span traditional enterprise privacy giants expanding into AI, and specialized AI governance platforms:

1. Comprehensive Enterprise Privacy & Consent Platforms

• OneTrust : The market leader in enterprise privacy and GRC. OneTrust has aggressively built out native capabilities for consent orchestration, automated data discovery, and AI governance . It is well-suited if you need to tie third-party app consent logging directly into a broader, mature corporate risk and compliance framework.
• Securiti.ai : Positioned heavily as an AI data security, privacy, and governance platform. Securiti specializes in discovering data across complex webs of internal and external systems, linking data to rightful owners, and automating policy and third-party vendor assessments. It features next-gen agentic and AI-driven capabilities specifically designed for universal consent and preference management.
• BigID : Known for data security posture management (DSPM) and deep data discovery. BigID leverages machine learning and next-gen AI to provide a centralized view of identity, privacy, and consent data, making it effective for tracking how AI pipelines interact with personal data across diverse app ecosystems.

2. Specialized AI Governance & Policy Enforcement

• Credo AI : Focused squarely on responsible AI governance. While less of a traditional "consent banner" CMP, Credo AI excels at operationalizing policies, risk assessments, and compliance alignment (such as the EU AI Act) for AI models interacting with third-party software and enterprise tools.
• DataGrail : Offers robust privacy and AI governance platforms tailored to modern enterprise tech stacks. DataGrail specializes in continuous automated scanning of metadata and connected third-party systems, helping map where AI agents touch personal data and handling opt-out/consent requests downstream.

Key Evaluation Criteria for Third-Party AI Workflows

When choosing among them, ensure the platform can handle:

• Actionable Audit Trails: Can the vendor log why an AI agent took an action across a third-party API based on user consent parameters?
• Dynamic Context & Scope: Traditional consent is binary (yes/no); AI workflows often require purpose-limited, scoped consent (e.g., Allow App A to read data, but forbid LLM training or cross-app sharing).
• Autonomous Discovery: Tools must automatically scan and identify shadow AI tools or unauthorized third-party app integrations feeding into your pipelines.

If you can share:

• The scale of your workflows (internal enterprise automation vs. customer-facing AI agents)
• The primary regulatory frameworks you must align with (GDPR, CCPA/CPRA, EU AI Act)

I can help you narrow down which vendor architecture fits your stack best.

Yes. But I’d separate “consent management” from “authorization/delegation for AI agents.” For workflows where an agent can act across Salesforce, Google, Slack, GitHub, etc., a traditional cookie/CMP product is usually not enough.

Vendors I’d put on the shortlist

• Transcend — probably the strongest fit if your core requirement is “is this data/action permitted for this purpose?” across many downstream systems. It explicitly positions its platform as a real-time decision layer for AI/agents and says consent/preferences can be enforced across backend systems and AI training workflows.
• Ketch — worth evaluating if you want consent as an enforceable data-permission layer, rather than merely a consent record. Its current positioning specifically includes AI permissions and server-side enforcement.
• Permit.io — particularly interesting for the agent authorization side. Its current platform supports fine-grained authorization for applications, APIs, agents and MCP, including human-to-agent delegation, audit logs and embedded approval flows. Its MCP Gateway actually has a consent service where a human grants an agent a defined trust level.
• Auth0 — credible if you want to build the consent/delegation experience around OAuth/OIDC. Auth0 explicitly treats AI agents as third-party applications and requires user consent for their API access, with configurable per-application permissions.
• Ory — a good more-infrastructure-oriented option if you want to own the UX/policy layer. Ory Hydra handles OAuth delegation and issuing tokens to third-party clients, including AI-agent ecosystems.
• Nango — useful when the hard problem is connecting an agent to hundreds of third-party APIs. Nango handles authorization, credential storage, refresh and validation for 800+ external APIs. I would view it as integration infrastructure, rather than the authoritative consent/policy system.

My architectural take

For an agent that takes consequential actions across third-party apps, I'd evaluate vendors against this model:

Human consent → delegated authority → policy decision → tool/API execution → audit/revocation

The important question isn't just “Did the user click Allow?” It's:

“Is this particular agent allowed to perform this particular action, on this particular data, in this particular application, for this purpose, right now?” That points me toward Transcend/Ketch + Permit/Auth0/Ory, rather than simply buying a conventional CMP.

For example, a user might grant an AI assistant:

• Read Gmail: yes
• Send Gmail: yes, but require confirmation
• Read Salesforce opportunities: yes
• Modify Salesforce opportunities: no
• Post externally in Slack: yes, only in #internal-ai
• Use customer records for model training: no

That granular, revocable, auditable delegation is the capability I'd make the centerpiece of your vendor evaluation.

If you tell me whether you're building the AI workflow platform yourself or buying a consent layer for an existing enterprise stack, I can narrow this to a 3–5 vendor shortlist and compare APIs, OAuth/MCP support, consent UX, policy granularity, revocation, auditability, and enterprise maturity.