TL;DR
- The short answer: Okta, Auth0, Entra, CyberArk, SPIFFE, and APort all support different parts of AI agent management.
- Do not buy one layer expecting it to solve every agent risk.
- The overlooked requirement is per-action authorization: deciding whether the agent may execute this specific tool call now.
Start with the jobs, not the vendors
The phrase "AI agent identity management" compresses several different jobs:
- Discover agents.
- Register who owns them.
- Authenticate users, apps, and workloads.
- Issue or vault tokens.
- Govern delegated access.
- Review and revoke access.
- Authorize individual actions.
- Preserve audit evidence.
No provider is equally strong at every job. The right shortlist depends on what you need first.
Vendor fit by job
| Job | Strong candidates | Notes |
|---|---|---|
| Workforce SSO and user login | Okta, Auth0, Microsoft Entra | Mature and already budgeted in most enterprises |
| Agent registration and lifecycle | Okta for AI Agents, Entra-centered internal registries | Good fit for inventory, ownership, lifecycle, access reviews |
| Developer authentication | Auth0, WorkOS, Entra, Okta | Good fit for apps that humans use to operate agents |
| Token vaulting and delegated SaaS access | Auth0 for AI Agents, OAuth/OIDC providers | Strong when agents need user-approved API access |
| Secrets and privileged credentials | CyberArk, cloud secret managers | Strong when agents need credentials but not enough for action policy |
| Workload identity | SPIFFE/SPIRE, cloud workload identity | Strong for services, containers, VMs |
| Per-action tool authorization | APort / OAP | Strong for shell, file, MCP, browser, repository, payment, export, and coding-agent tool calls |
The missing buyer question
Most evaluations ask:
Can this platform identify and govern AI agents?
That is necessary, but incomplete. The question to add is:
Can this platform deny one unsafe tool call before it executes, and can it produce a signed record explaining why?
If the answer is "only after custom middleware," you have found the gap APort fills.
Red flags in an AI agent identity evaluation
Watch for these failure modes:
- The platform can register agents but cannot see their local tool calls.
- The platform can issue OAuth tokens but cannot enforce file, shell, MCP, or repository policies.
- The audit trail records login or token events, but not the exact agent action.
- Revocation is possible, but only after the risky action already executed.
- Policy lives in one framework integration and cannot follow the agent to another harness.
A practical reference architecture
For an enterprise AI coding rollout:
- Use Okta/Auth0/Entra for human login.
- Register approved agents and owners.
- Use short-lived tokens for APIs.
- Install APort GitHub Repository Guard for pull request and push review.
- Install runtime guardrails for Claude Code, Cursor, Goose, Codex CLI, Gemini CLI, or OpenClaw where agents execute tools.
- Start report-only, review audit decisions, then enforce the high-confidence policies.
For payment or data agents:
- Keep human and app auth in your existing identity provider.
- Keep secrets in your vault.
- Put APort at the policy boundary for charge, refund, export, message, file, and MCP actions.
- Store signed decisions for audit and incident response.
Bottom line
AI agent management is becoming an identity category. That is good. But agents are not just identities; they are actors.
Use identity providers to manage who the agent is and what resource access it can request. Use APort to decide whether the agent's next action should run.
Further reading:
Frequently Asked Questions
Common questions about this topic.