Credential chaos in multi-agent AI — unified secrets, policy & routing

Enterprises running multiple cooperating AI agents face a fast-growing operational problem: credential sprawl, inconsistent policy enforcement, and fragile routing of secrets between agents. This is most acute in large IT estates — an addressable base of roughly 280,000 mid‑and‑up enterprises paying $30K ACV implies an $8.4B market — where Dev, Sec, and Ops teams must coordinate runtime secrets, agent-level permissions, and auditable policies across orchestration layers and cloud IAMs. You could build a unified platform that provides an org‑wide secrets mesh plus agent-aware policy, dynamic credential injection/rotation, and routing/translation primitives so agents get exactly the credentials and claims they need at runtime. The product would combine a central secrets service, a lightweight agent SDK and sidecar, a policy-as-code engine tailored to multi‑agent flows, and connectors for Vault, AWS IAM, Azure AD, and popular orchestration layers. The timing is compelling: LLM orchestration adoption is accelerating, DevSecOps expectations increasingly demand runtime governance, and cloud IAMs now expose APIs that make secure dynamic injection feasible; those trends underpin a market score of 95/100 and revenue potential of 88/100. Buyers will pay for reliable, auditable enforcement because a single breach or misrouted credential can cost millions and regulatory fines add urgency. To stand out you must focus on practical interoperability and developer ergonomics rather than re‑inventing secrets storage — ship battle‑tested connectors, an intuitive policy model for cross‑agent flows, and rich observability for auditors. Challenges are real: deep integrations across heterogeneous stacks, initial adoption friction, and proving low‑latency, secure runtime injection at scale; success depends on first winning a few vertical references and making the platform hard to replace.