Skip to main content

​
The problem

AI agents are shipping to production. They sign into APIs, browse the web, talk to databases, and act on behalf of real users. But credential handling around those agents is still usually one of four bad patterns:
  • Hardcoded — secrets embedded in code, prompts, or config files
  • Over-shared — plaintext environment variables available to everything on the machine
  • Over-privileged — agents given broad standing access to an entire vault
  • Invisible — agent actions hidden behind shared human or service identities
When something leaks or an agent takes an unauthorized action, there is no way to trace it, scope the blast radius, or revoke just that agent’s access. This is exactly the failure surface security guidance is calling out. OWASP’s Top 10 for LLM applications lists sensitive information disclosure and insecure plugin/tool design as major risks for LLM-powered systems.

​
Why now

The adoption curve is no longer hypothetical:
  • On August 26, 2025, Gartner predicted that 40% of enterprise applications would include task-specific AI agents by the end of 2026, up from less than 5% in 2025. Gartner
  • On March 24, 2026, the Cloud Security Alliance reported that 85% of organizations already use AI agents in production and 74% say agents often receive more access than necessary. Cloud Security Alliance
The category is forming now. The gap is a developer-first control plane that combines scoped runtime access, policy checks, connectors, and an interface surface that feels native to how agent builders already work.

​
Who abadge is for

abadge is built for teams shipping:

Browser agents

Agents that sign in and operate websites on behalf of users.

Workflow automation

Pipelines that call third-party APIs with customer credentials.

Internal copilots

Assistants that need scoped access to company systems.

B2B platforms

SaaS products acting on behalf of customer accounts.
It is especially useful when an agent needs customer-specific credentials, organization secrets, or privileged internal access without being handed broad standing permissions.

​
What abadge is — and isn’t

abadge is the secure access layer between AI agents and credentials. It stores native secrets (or connects to external vaults), grants least-privilege access just in time, supports approval flows for sensitive actions, and keeps every access attributable and auditable.
For v1, abadge is not positioned as:
  • a generic password manager
  • a browser autofill product
  • a zero-knowledge vault platform
  • an agent orchestration system
Those stories are either too crowded, not supported by the current implementation, or not the sharp wedge. Native credential storage matters, but it supports the access-control story rather than replacing it.