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

Yes. But I’d distinguish consent management from ordinary OAuth/identity. For AI workflows acting across SaaS apps, you need to answer:

1. Who authorized the agent?
2. What exact apps/data/actions did they authorize?
3. Can the agent ask for additional permission mid-workflow?
4. Can an admin impose policy over user consent?
5. Can every action be attributed to both the human and the agent?
6. Can credentials be short-lived, scoped, revoked, and audited?

As of 2026, I’d put these vendors on the credible shortlist:

My strongest recommendation: Okta/Auth0

This is the vendor I'd investigate first if your requirement is "AI agents need permission to act across third-party applications, with enterprise-grade consent and auditability."

Auth0's current AI-agent offering includes agent identities, token vaulting, fine-grained authorization, short-lived tokens, and explicit human consent for sensitive actions. It also supports asynchronous authorization—e.g. an agent can continue working and request approval on a user's phone when it reaches a sensitive operation.

More importantly, Okta is pushing Cross App Access (XAA) as an open protocol for governing agent-to-app and app-to-app connections. Its 2026 ecosystem includes integrations with companies such as Anthropic, Asana, Atlassian, Canva, Figma, Glean, Slack and Zoom.

That makes it particularly relevant if your architecture looks like:

Human → AI agent → Salesforce + Slack + Google Drive + Jira + payment system rather than:

Human → your application → your own APIs

AWS is worth serious consideration

AWS AgentCore Identity now supports OAuth access to third-party applications, including explicit user-consent flows, and maintains the resulting credentials in a token vault.

I'd shortlist it if your agents are already running on AWS and you don't want to introduce another identity control plane.

Don't overlook the integration-layer vendors

There's an important distinction here.

Auth0/Okta/Entra/etc. answer:

"Is this agent/user allowed to do this?" Whereas Nango, Paragon, Pipedream, Composio, Arcade, etc. increasingly answer:

"How does my agent securely connect to Salesforce/Slack/GitHub/etc. and obtain/manage the OAuth credentials?" The latter category is becoming quite mature. A recent 2026 market comparison identifies Nango, Paragon, Pipedream, Arcade and others specifically around managed OAuth and agent-to-SaaS connectivity.

For a product company, Nango + an authorization/identity layer is potentially a more attractive architecture than trying to make your IAM platform also be your entire integration fabric.

One important architectural warning

I wouldn't buy a product merely because it says "AI agent identity."

You want to test whether it supports delegated, per-action authorization, not just giving every agent a machine identity.

For example, suppose Alice tells an agent:

"Clean up my CRM and email the customers whose contracts expire next month." The security model should ideally distinguish:

• Alice authorized the workflow.
• Agent X is acting on Alice's behalf.
• Agent X may read specified CRM records.
• Agent X may draft emails.
• Agent X may not send emails without another approval threshold.
• Agent X may not access unrelated CRM records.
• The authorization expires/revokes cleanly.
• The audit record identifies Alice → Agent X → Salesforce → specific action.

That's substantially more sophisticated than conventional OAuth.

My 2026 shortlist

If I were running an RFP, I'd start with:

1. Okta/Auth0 — best overall enterprise control-plane candidate.
2. Microsoft Entra — especially if your enterprise is already Microsoft-centric.
3. AWS AgentCore Identity — especially for AWS-native agent infrastructure.
4. Nango — strongest candidate to investigate for the third-party OAuth connection layer.
5. Paragon — particularly compelling for embedded/customer-facing integrations.
6. Google Cloud Agent Identity — worth evaluating for GCP-centric environments.

I'd also explicitly evaluate XAA / enterprise-managed authorization for MCP rather than locking yourself into a proprietary consent model. The ecosystem is moving toward standardized ways for enterprises to control which agents can connect to which MCP servers.

If you tell me whether you're building an internal enterprise agent platform or selling a SaaS product where your customers' agents will act in their third-party accounts, I can narrow this to a 3–4 vendor shortlist and give you a concrete consent/authorization architecture + RFP scorecard.

Human → AI agent → Salesforce + Slack + Google Drive + Jira + payment system rather than:

Human → your application → your own APIs

AWS is worth serious consideration

AWS AgentCore Identity now supports OAuth access to third-party applications, including explicit user-consent flows, and maintains the resulting credentials in a token vault.

I'd shortlist it if your agents are already running on AWS and you don't want to introduce another identity control plane.

Don't overlook the integration-layer vendors

There's an important distinction here.

Auth0/Okta/Entra/etc. answer:

"Is this agent/user allowed to do this?" Whereas Nango, Paragon, Pipedream, Composio, Arcade, etc. increasingly answer:

"How does my agent securely connect to Salesforce/Slack/GitHub/etc. and obtain/manage the OAuth credentials?" The latter category is becoming quite mature. A recent 2026 market comparison identifies Nango, Paragon, Pipedream, Arcade and others specifically around managed OAuth and agent-to-SaaS connectivity.

For a product company, Nango + an authorization/identity layer is potentially a more attractive architecture than trying to make your IAM platform also be your entire integration fabric.

One important architectural warning

I wouldn't buy a product merely because it says "AI agent identity."

You want to test whether it supports delegated, per-action authorization, not just giving every agent a machine identity.

For example, suppose Alice tells an agent:

"Clean up my CRM and email the customers whose contracts expire next month."

Managing consent for AI workflows—especially autonomous or agentic setups that pull from, write to, or act across multiple third-party SaaS apps—requires tools that do more than just drop a cookie banner . You need platforms capable of dynamic policy orchestration, data discovery, and API-driven consent propagation across downstream systems.

Credible vendors in this space generally span enterprise privacy/GRC leaders and specialized data-orchestration platforms:

• OneTrust — Widely recognized as the heavyweight for enterprise GRC and privacy. It combines standard consent management with a dedicated OneTrust Responsible AI Governance module, making it ideal if you need to tie third-party app consent and data lineage to a massive, auditable compliance framework.
• Securiti.ai — Strong focus on "AI-driven automation" combining data security, privacy, and governance. Their platform uses a fabric-based approach to discover data across a web of internal and external/third-party systems, mapping user identities to consent flows which fits agentic AI architectures well.
• Ketch — Built specifically for policy-driven consent orchestration. Unlike legacy tools meant only for web traffic, Ketch focuses on synchronizing user preference and consent states in real-time across data warehouses, customer touchpoints, and downstream third-party application APIs.
• BigID — Exceptional at deep, machine learning-driven data discovery and classification. BigID connects consent governance directly to your data infrastructure, which is critical when you need your AI agents to know precisely what data they are authorized to touch across connected third-party apps.
• DataGrail — Known for smooth integrations with hundreds of third-party SaaS applications and an agile approach to modern privacy operations. They feature automated system detection and an DataGrail AI Governance solution designed to track where personal data flows across modern tool stacks.

To help narrow down the right fit, tell me:

• Are these workflows driven by internal enterprise AI agents or customer-facing AI products?
• Which key third-party apps (e.g., Salesforce, Slack, Google Workspace) do these workflows primarily integrate with?

Yes. But I’d distinguish “consent management” from “authorization for AI agents to act across apps.” They overlap, but they’re not the same control plane.

For an AI workflow such as “let this agent read Gmail, update Salesforce, create a Jira ticket, and send Slack messages on my behalf,” I’d shortlist these vendors:

My take

If your core requirement is “an AI agent needs permission to act for a human across third-party applications”, I would start with Auth0/Okta-style authorization infrastructure, rather than a traditional CMP.

The important model is:

Human → Agent → Specific app → Specific actions/data → Specific purpose → Time limit → Revocation/audit For example, don't capture merely:

“I consent to Agent X accessing my Google account.” Capture something closer to:

“Agent X may read calendar availability and create calendar events for the next 30 days, but may not read email; authorization expires in 30 days and can be revoked.” Auth0's third-party application implementation is particularly relevant here because it explicitly treats AI agents and MCP clients as third-party applications and requires explicit authorization with granular API permissions.

Transcend becomes especially compelling if you also need the privacy side: “Is this data actually permitted to be used by the agent for this purpose?” Its current platform explicitly positions consent, permissions and business policy as a runtime decision layer, including AI/“Do Not Train” policies.

So I'd probably evaluate a two-layer architecture:

1. Authorization layer: Auth0 / Okta — Can this agent perform this action for this user?
2. Privacy/policy layer: Transcend / OneTrust / Securiti — Is this use of this data permitted?
3. Agent runtime: enforce both decisions before each consequential tool call.
4. Audit layer: record who authorized what, which agent acted, which app/action was invoked, what policy decision was made, and when authorization expires/revokes.

If you're building this as a product/platform rather than implementing it internally, I can also give you a tighter vendor landscape of ~10 companies specifically doing agent consent/delegated authorization, including newer AI-native vendors—not just traditional IAM/privacy companies.

My take

If your core requirement is “an AI agent needs permission to act for a human across third-party applications”, I would start with Auth0/Okta-style authorization infrastructure, rather than a traditional CMP.

The important model is:

Human → Agent → Specific app → Specific actions/data → Specific purpose → Time limit → Revocation/audit For example, don't capture merely:

“I consent to Agent X accessing my Google account.” Capture something closer to:

“Agent X may read calendar availability and create calendar events for the next 30 days, but may not read email; authorization expires in 30 days and can be revoked.” Auth0's third-party application implementation is particularly relevant here because it explicitly treats AI agents and MCP clients as third-party applications and requires explicit authorization with granular API permissions.

When AI agents and multi-step workflows interact dynamically with third-party SaaS apps, databases, and APIs, traditional cookie banners or static privacy policies fall short. You need data-layer consent orchestration —systems capable of intercepting API calls, verifying dynamic user purpose-based consent, and propagating opt-outs across external infrastructure in real-time.

Credible vendors addressing this specific intersection of AI workflow orchestration, privacy infrastructure, and granular data permissions include the following:

• Ethyca Stands out for its developer-first and API-driven approach to AI governance. Built on an open-source taxonomy foundation (Fides), Ethyca offers purpose-based access control and real-time consent engines (like Janus ) that actively intercept and enforce data policies before third-party systems or AI agents pull data . Check out their capabilities on the Ethyca Platform.
• Transcend Highly regarded for deep infrastructure integration and fine-grained data permission management. Transcend treats AI consent contextually—tracking specific processing activities (e.g., opting out of AI model training vs. general personalization) and automatically syncing those choices across data silos, warehouses, and active AI pipelines. Explore more via Transcend Data Permission Management.
• OneTrust The dominant incumbent for broad enterprise trust intelligence. OneTrust has aggressively built out AI governance, automated vendor risk tiering, and enterprise data discovery tooling. While historically heavier and more workflow/bureaucracy-oriented than developer-first tools, it remains the standard choice if you need to tie AI third-party app consent directly into a massive, audit-ready corporate compliance framework. Learn more on the OneTrust Platform.
• DataGrail Known for modern, automated privacy workflows with low-friction integration. DataGrail specializes in continuous system scanning to surface hidden third-party apps and automating data subject rights and preference architecture. Review their approach on the DataGrail Platform.

To help narrow down which vendor fits your architecture, could you share:

• Are your AI workflows internal productivity agents or customer-facing features?
• Do you primarily need to control access to internal data warehouses or external third-party SaaS tools (like Salesforce, Slack, or Google Workspace)?

Managing consent and data flow permissions for AI workflows (especially autonomous agents or orchestrators pulling/pushing data across third-party SaaS apps) requires moving beyond traditional front-end cookie banners . You need server-side consent orchestration, dynamic data discovery, and API-level access enforcement.

Credible vendors in this ecosystem span three overlapping categories: Consent & Data Orchestration Platforms (which handle real-time preference propagation), AI-Native GRC & Governance Platforms (which map AI tool behavior to risk frameworks), and Developer-First Privacy Tools.

1. Dynamic Consent & Data Orchestration (Best for Real-Time API/Data Propagation)

These platforms focus on ensuring that when a user revokes consent or restricts data usage, that choice immediately propagates downstream to any connected data pipeline, CRM, or AI processing layer.

• Ketch: Stands out architecturally because it was built server-side from the ground up. Rather than just dropping a UI banner, Ketch features real-time consent propagation across data pipelines, customer data platforms (CDPs), and external infrastructure, making it heavily suited for dynamic AI data access.
• Transcend: Highly regarded by developer-heavy and engineering-driven teams. Transcend handles data silos well, offering automated data mapping and real-time governance primitives that can be hooked directly into internal microservices and third-party API integrations.
• Ethyca: Focuses deeply on developer-first data privacy infrastructure. Their tooling automates discovery and consent enforcement programmatically via APIs, which fits well if your AI workflows are calling third-party apps dynamically and need programmatic compliance gates.

2. AI-Native Governance & Policy Engines (Best for Agentic & Cross-App Auditing)

If your primary concern is tracking what third-party apps an AI workflow accesses, auditing model behavior, and complying with emerging standards (like the EU AI Act or ISO 42001), you need dedicated AI GRC (Governance, Risk, and Compliance) platforms:

• Credo AI: A prominent leader in responsible AI governance. It specializes in contextualizing AI use cases, tracking third-party model/data integrations, and enforcing internal policies across enterprise AI workflows.
• Trustible: Purpose-built for AI governance and compliance orchestration, bridging the gap between traditional privacy workflows (like DPIAs) and runtime AI risk tracking.
• IBM watsonx.governance: An enterprise-grade, highly scalable platform that tracks model behavior, data lineage, and policy compliance across a massive variety of regulatory frameworks.

3. Traditional Enterprise Privacy Giants (Best for Heavy Compliance & Legal Frameworks)

If your third-party app workflows require heavy compliance tracking, vendor risk assessments, and formal audit logging for strict enterprise requirements:

• OneTrust: The behemoth of the compliance space. While heavy and sometimes overkill for lightweight agentic workflows, it features comprehensive data mapping, vendor risk management, and integrated consent preference centers if you need a single system of record for enterprise accountability.
• Osano: Well-liked in the mid-market space for combining standard consent management with a proprietary Vendor Risk Index that actively scores third-party SaaS tools on their privacy behavior.

If you'd like to narrow this down, tell me:

• Are these workflows internal automation agents or customer-facing AI features?
• Do you need runtime API blocking (stopping a call if consent is missing) or primarily audit logging and policy tracking?

I can help zero in on the exact architectural pattern you need.

Yes. If by “consent management” you mean letting an AI agent act on a user’s behalf across SaaS apps while controlling exactly what it may do, getting explicit approval for sensitive actions, and being able to revoke/audit that access, there are several credible vendors—but they sit at different layers.

My shortlist

The important distinction

I wouldn't evaluate these as generic “consent-management platforms.” For agentic workflows, there are really four separate capabilities:

1. Delegated consent — “I authorize this agent to access my Salesforce.”
2. Fine-grained authorization — “It can read opportunities, but cannot delete them.”
3. Step-up/human approval — “It can prepare the wire transfer, but I must approve it.”
4. Lifecycle governance — “Who owns this agent, what apps does it have access to, and how do we revoke everything?”

Auth0 is especially interesting for #1–3. Okta and Entra are stronger for #4, particularly in an enterprise environment. Aserto is attractive as the programmable policy layer for #2.

A useful emerging pattern is therefore:

Human → Agent identity → Consent/authorization layer → OAuth/token broker → SaaS apps

rather than giving the agent persistent credentials to every SaaS application.

Google's current agent tooling illustrates the same architecture: 3-legged OAuth obtains user consent, the resulting credentials are stored in a managed vault, and short-lived credentials are supplied to the agent when it invokes a tool.

If I were shortlisting vendors for an RFP

I'd put Okta, Auth0, Microsoft Entra, and Aserto on the first-round list, but ask them all the same hard questions:

• Can consent be scoped to individual tools/actions, not just an application?
• Can consent expire automatically?
• Can a user approve a single high-risk action without granting standing permission?
• Can the agent operate asynchronously and come back for approval later?
• Can admins revoke an agent's access centrally?
• Are OAuth refresh tokens kept outside the agent/runtime?
• Can authorization decisions incorporate user + agent + resource + action + context?
• Is there an immutable audit trail tying every action to the human who authorized it and the agent that executed it?
• Does it support MCP as well as ordinary OAuth APIs?
• Can policies be enforced across applications from different vendors?

One particularly interesting development is Okta's Cross App Access: Okta says it moves OAuth consent management from repeated end-user prompts into centrally administered policies, specifically targeting AI-agent-to-application access.

Bottom line: if you're building a product that needs to obtain and manage users' delegated SaaS permissions, I'd start with Auth0. If you're an enterprise trying to govern a fleet of agents across existing applications, I'd start with Okta/Entra. If the core problem is a sophisticated, vendor-neutral authorization policy engine, I'd investigate Aserto alongside them.

If you tell me whether you're building an AI-agent platform, an enterprise internal workflow system, or a consent/authorization product, I can narrow this to ~5 vendors and compare their APIs, OAuth/MCP support, deployment model, and likely pricing.

Yes. But I’d separate “consent management” from ordinary IAM/authentication. For AI workflows that can act across Slack, Google, Salesforce, Jira, etc., the important capability is delegated authorization: a human grants an agent narrowly scoped, revocable authority, and every downstream action remains attributable to that human + agent + workflow.

Vendors I’d put on the serious shortlist

My take

If your requirement is specifically:

“A user says yes to an AI workflow doing X across several external SaaS systems, with least privilege, expiration, revocation, and an audit trail.”

I'd evaluate Permit.io, Auth0 FGA, and Cerbos first.

Permit.io is probably the most directly aligned with consent for agentic workflows today. Its consent service is actually an OAuth 2.1 authorization server, and its gateway can bind a human's approval to a particular workflow and impose an administrator-defined maximum trust level.

Auth0/Okta is the safer enterprise bet if you're already deep in the identity ecosystem. The interesting piece isn't just Auth0 authentication; it's FGA + agent identity + task-based authorization.

Cerbos is attractive if you want the consent decision to be part of a broader, vendor-neutral authorization architecture. It can evaluate the human, agent, resource, requested action, and delegation chain at runtime rather than treating consent as a one-time OAuth checkbox.

One architectural warning

I wouldn't buy a product merely because it says “AI agent security” or “consent management.” For your use case, I'd require these capabilities:

1. Human → agent delegation — not just user login.
2. Action/resource-level scopes — e.g. “create Jira tickets in project X,” not “access Jira.”
3. Downstream delegation — authority survives safely when Agent A invokes Agent B or an MCP tool.
4. Attenuation — a sub-agent cannot acquire more authority than its parent.
5. Short-lived grants + revocation — ideally without waiting for OAuth token expiry.
6. Explicit approval for high-risk actions — payments, deletion, external messaging, permission changes, etc.
7. Continuous authorization — re-check policy at execution time, rather than assuming the original consent is still valid.
8. Full provenance — human → agent → workflow → tool → resource → action.
9. Cross-app auditability — one coherent audit trail despite Slack/Salesforce/Jira/etc. having different authorization models.
10. OAuth 2.1 / MCP / emerging agent protocols rather than a proprietary agent identity scheme.

That distinction matters because OAuth consent alone isn't sufficient. OAuth can establish that a user authorized an application, but your policy layer still needs to answer “Is this particular agent allowed to perform this particular action on this particular resource right now?” Auth0 FGA, Cerbos, Aserto and Permit are much closer to that authorization layer.

If you're evaluating this for an enterprise architecture, my initial bake-off would be Permit vs. Auth0 FGA vs. Cerbos, with Stytch as the fourth option if you primarily need to build the OAuth/consent UX yourself.

Managing consent and permissions for AI workflows—especially autonomous or multi-step agents operating across third-party SaaS apps, APIs, and data silos—requires a shift from traditional front-end cookie banners to data-layer consent orchestration, dynamic token scoping, and AI risk governance.

Credible vendors in this space bridge traditional privacy operations (CMP) with modern AI data tracking and agentic oversight:

• BigID (Consent for AI) : Known for its data discovery core, BigID extends its universal consent framework specifically to track and enforce AI opt-outs and data usage restrictions across complex system webs, flagging unmanaged AI data pipelines as active risks.
• Credo AI (GAIA) : Excellent for enterprises building or deploying autonomous multi-step agents. Its Governance, AI, and Agentic (GAIA) capabilities focus heavily on agent inventory, tool-use permissioning, and tracing cross-app actions mapped against standards like the EU AI Act and NIST AI RMF.
• Ketch : Highly rated for enterprise consent orchestration. It moves beyond front-end collection to provide server-side permissioning infrastructure that propagates user choices and AI access governance rules dynamically across third-party data flows and customer data platforms.
• Securiti.ai : Offers a unified privacy and AI governance platform. Securiti combines automated data discovery graphs across internal/external third-party apps with next-generation agentic orchestration to handle dynamic consent withdrawal and in-app tracking.
• Ethyca : A strong privacy-engineering choice that utilizes a data-layer approach (via its Fides/Janus frameworks) to propagate and enforce real-time consent and purpose-limitation rules directly into backend data pipelines rather than just capturing preferences at the UI level.
• OneTrust (Data & AI Governance) : The legacy market leader that has heavily expanded its platform to cover AI-ready governance, tracking how data moves from standard enterprise repositories into internal and third-party AI models with consolidated audit logs.

To help narrow down which vendor fits your architecture, could you share:

• Are these internal AI agents accessing company SaaS tools, or are you building customer-facing agents that require end-user consent collection?
• Do you need developer-first APIs/SDKs or a no-code enterprise compliance dashboard?

Managing consent and data permissions for AI workflows that operate across third-party apps requires shifting from traditional static cookie/banner consent to server-side permissioning, dynamic authorization, and non-human identity governance .

Credible vendors in this space bridge the gap between traditional privacy/consent management platforms (CMPs) and modern AI/agentic governance.

Top Credible Vendors for AI Consent & Cross-App Governance

• Ketch (Best for real-time consent propagation across the data stack)
  Unlike standard browser-only banners, Ketch is architected for server-side permissioning. It dynamically enforces user consent and opt-outs as data flows downstream into third-party tools, data warehouses, and AI training/inference pipelines. They feature purpose-built modules like Ketch AI Sentry for monitoring active data sharing.
• Securiti.ai (Best for data-centric AI governance and discovery)
  Securiti combines a universal consent platform with deep, AI-driven sensitive data intelligence. It excels at discovering where personal data lives across multi-cloud and external SaaS environments, automatically linking data to its owner, and applying dynamic consent policies to in-app or cross-app AI workflows.
• BigID (Best for data discovery paired with AI consent risk tracking)
  BigID applies machine learning to data cataloging and extends it with a Consent Governance App. It provides a way to track whether third-party or internal AI systems actually respect a user’s AI opt-out preferences, flagging unaligned datasets or disconnected apps as compliance risks.
• OneTrust (Best for comprehensive enterprise-scale compliance)
  OneTrust has heavily expanded its GRC footprint into responsible AI governance. It is a strong fit if you need an enterprise inventory for AI models, agents, and data flows coupled with robust third-party vendor risk assessments and standardized consent collection.
• Oleria / Reco (Best for agent-to-app access and identity guardrails)
  If your primary concern is managing the actions of autonomous AI agents utilizing OAuth tokens, APIs, and protocols like Model Context Protocol (MCP) across third-party apps, specialized AI access gateways like Oleria or SaaS security platforms like Reco provide real-time policy-led credential brokering and tool-level scoping.

To help narrow down the right platform, tell me:

• Are you looking to manage end-user consent (letting customers opt out of their data being processed by AI) or internal workforce/agent consent (scoping what third-party apps your company's internal AI agents can touch)?
• What infrastructure or orchestration layer are your AI workflows built on?

Yes. If by consent management you mean “an AI agent can act on a user’s behalf across Salesforce, Google, Slack, GitHub, etc., while the user explicitly grants/limits/revokes authority and the enterprise can audit every delegated action”, I’d look at a somewhat different vendor set than traditional OAuth/IAM.

My current shortlist

The important distinction

I would not treat this as merely an OAuth problem.

For an agent, there are at least four separate questions:

1. Identity: Who is the human?
2. Delegation: Is the agent actually acting on behalf of that human?
3. Consent: What did the human authorize the agent to do?
4. Runtime authorization: Is this particular action, on this particular resource, appropriate right now?

Traditional OAuth mostly handles #1–3. Agentic workflows make #4 crucial.

For example, a user might consent to:

“Let my AI assistant manage my Salesforce opportunities.”

That shouldn't automatically mean:

“The model can delete every opportunity in Salesforce forever.”

You want policy that can narrow authority by action, resource, tenant, purpose, time, risk, and user intent, and ideally require human approval for high-impact operations. Recent research is explicitly identifying this gap between static integration permissions and the user's actual intent.

Who I'd prioritize

If I were doing an enterprise vendor evaluation today, I'd put the first calls in this order:

1. Arcade — if you're looking for a purpose-built agent authorization/delegation layer.
2. Okta — if this needs to become part of the enterprise IAM/IGA stack.
3. Cerbos — if you need a powerful, vendor-neutral authorization policy layer underneath your agents.
4. WorkOS — if the immediate problem is primarily OAuth connections and user consent rather than deep runtime authorization.

There's also an interesting architectural option: WorkOS/Okta for identity + consent, Cerbos for policy enforcement, and your agent platform for execution. That avoids making one vendor responsible for the entire authorization stack.

One thing I'd specifically test in demos is whether the vendor can represent and audit:

Human → Agent → Sub-agent → Tool → SaaS resource → Action

rather than merely:

Human → OAuth token → SaaS API.

That distinction is likely to matter a lot as your workflows become more autonomous. Cerbos, for example, explicitly describes capturing the delegation chain and policy decision for each AI request.

If you tell me whether you're buying this for an enterprise, building an AI-agent platform, or building the consent product itself, I can narrow this to ~5 vendors and compare their APIs, deployment model, third-party-app coverage, auditability, and pricing.