Alter Pitches Per-User Access Control for AI Agents
Alter, a Y Combinator S25 company, describes its product as a system that authenticates, authorizes, and logs every request made by AI agents. The description appears on its homepage.
The company says its SDK, the developer toolkit it supplies, handles credential storage, token refresh, policy evaluation, and audit in a single call, across 140 API providers. According to the same site, the platform places four features on one request path between an agent and the API it calls. Alter also advertises first-class integrations with LangChain, MCP (the Model Context Protocol, a standard for connecting AI models to external tools), and AWS, and publishes use-case guides with code examples.
The homepage frames the product as privileged access management (PAM), the discipline of limiting and monitoring high-value access, applied to AI agents. In that framing, Alter controls what an agent can do on behalf of each user, enforces policy, keeps credentials away from the agent, and audits every action. The enterprise page adds a security review guide for AI agents, listing privileged access controls that enterprise security teams can inspect in agents built on Alter.
The developer documentation includes a guide on giving an agent scoped access, meaning permissions limited to a defined set of actions. As described there, an operator provisions a managed agent, binds it to credentials, and lets it call providers under its own identity, with its own audit trail, within what the documentation calls a Multi-User Server concept. The documentation guide is the primary reference for that model.
Alter has also open-sourced an OpenClaw plugin for multi-user agents. The company's blog describes it as providing delegated access, secure credentials, isolated memory, and runtime policies.
Y Combinator's own listing describes Alter as a zero-trust identity and access control platform built for AI agents, meaning one that verifies every request rather than trusting a caller because it sits inside the network. The listing says the platform wraps each agent tool call in strong authentication and fine-grained authorization. Y Combinator's official accounts have made similar claims, saying the product lets teams run agents in production without over-scoped credentials or compliance gaps. Another Y Combinator company, Multifactor (F25), is building zero-trust authentication, authorization, and auditing for AI agents, which places Alter in a category drawing more than one startup.
The mechanics Alter describes will look familiar to anyone who has worked in workforce identity. Scoped credentials, token lifecycle management, policy decision points, and append-only audit logs have long existed in privileged access tooling. What is new is the caller. The principal making the API request is a model-driven agent acting for a human user, not a service account or an engineer holding a shared key.
In my view, the more interesting question for security teams is whether agent identity can be made auditable at the level of the individual user and the individual tool call. A per-user, per-request trail would let an organization answer who authorized an action, which credential was used, and under what policy. That is difficult to reconstruct when agents share one service credential. Alter's documentation suggests this is the model it is building toward. Whether security teams adopt the enterprise review guide as a baseline will depend on how these controls hold up under their own testing, and that work sits outside the company's pages.
The practical upside is clear enough. If agents can be given narrow, revocable, observable access, teams can hand them more useful work without handing over the keys to an entire account. That is the bet Alter and its peers are making, and the coming year of production deployments will show how far it extends.
