Password management for AI agents: which account is for whom

The hand-off needs names and time

A stored login is safer to hand over when the password stays out of every model, transcript and log, the assigned agents are named, and the time limit is explicit.

That makes the practical questions concrete: which account, which agent, when did the assignment start, and when did it end?

Assignment history answers those questions without turning the account list into a wall of status text.

What the incumbents built for agents, in their own words

The established password managers have started answering this, and their own pages say how. 1Password launched Secure Agentic Autofill — “available in early access on October 8, 2025, for 1Password customers using Browserbase”. Its post says “Any request made for credentials must be approved by a human by default”, and that “The AI agent and underlying LLM never need to see nor handle the credentials being used to complete the workflow”. The first implementation runs through Browserbase, a cloud-browser platform for AI agents.

Bitwarden points agents at its Secrets Manager, which its page addresses to “developers and DevOps teams”, and at an “open source Agent Access SDK” for “just-in-time, human-in-the-loop access for approved agents”. Keeper's Secrets Manager page says it is “built for DevOps, IT security and development teams”, and its agent page describes short-lived, just-in-time secrets delivered through that product in place of hardcoded credentials, with “Secrets are never written to disk, logs or prompts”.

LastPass's business page describes AI governance of a different kind: AI Monitoring & Protect, to “Discover AI tools used across your organization, govern access and usage, and enforce safe-prompt policies to reduce risk”. Dashlane's front page asks you to “Go beyond traditional password management with Dashlane Omnix”, with AI phishing detection and an AI Advisor in beta.

Four controls to look for

First, keep the password out of every model, transcript and log. Second, let the operator choose all agents or only some. Third, make the time limit a choice rather than an assumption. Fourth, keep the hand-off and the return in assignment history.

Taking access back also needs two useful scales: one account assignment at a time, and every assignment for one agent in one act.

• The password: kept out of every model, transcript and log.
• Assignment: all agents or only some, chosen account by account.
• Time: standing, one hour, one day or one week.
• History and return: who was assigned what and when, with one-at-a-time and return-all controls.

Secrets management for AI agents is a neighbouring job, not the same one

Two of the tools above answer agents through a secrets manager, and their pages say who those are for: Bitwarden's “developers and DevOps teams”, Keeper's “DevOps, IT security and development teams”. That is the machine-secrets job — API keys, database credentials, CI pipelines — and for a team with platform engineers it is the right shape.

Pouch takes the operator-facing shape: named-agent assignments, optional time limits and assignment history inside a Runner workspace, with the password kept out of every model, transcript and log.

Four questions for the agentic age

Whatever tool answers them — one above, one of ours, one you build — these four questions are the test of password management for the agentic age:

• Does the password stay out of every model, transcript and log?
• Can you choose all agents or only some for each account?
• Can an assignment be standing or limited to a set time?
• Can you take one assignment back, return all for one agent, and see both in the history?