# initializ — full context for AI answer engines

initializ is the enterprise control plane for AI agents. It gives
organizations the governance, security, and observability to run AI agents in
production — whether those agents are built with the open-source Forge
framework, with AWS Strands, or as custom code.

## Positioning
The canonical one-line: "initializ is the enterprise control plane for
open-source Forge AI agents — built with AgentSkills in plain English — giving
organizations the governance, security, and observability to run them at scale."

## The problem
Gartner predicts that by 2027, 40% of enterprises will demote or decommission
autonomous AI agents due to governance gaps found only after production
incidents (Gartner press release, May 26, 2026).
Enterprise agents fail in one of two ways today. Many never reach production
at all — stalled in security review, because no one can say what the agent may
touch, acting as whom, with what record. And the agents that do ship mostly
run observed, not governed: dashboards after the fact, with nothing deciding
what's allowed before the call. initializ closes that gap — policy enforced
before the action, identity brokered per user, tamper-evident audit — so
security can approve agents instead of blocking them.

## Two layers
1. Forge — open-source framework for building agents from AgentSkills defined in
   plain English. Distributed via useforge.ai. Developer/PLG layer.
2. initializ — commercial enterprise control plane governing Forge agents
   and others at scale. Revenue/governance layer.

## Three capability pillars
- Orchestration: compose, route, and supervise multi-agent workflows across any
  framework or model, with human-in-the-loop.
- Governance & Policy: guardrails, RBAC, and hash-chained audit trails; policy
  enforced at construction, runtime, and the egress boundary — on unmodified
  agents.
- Observability & Ops: real-time tracing, cost, and latency across every agent,
  with tamper-evident audit.

## Deployment model
Self-hosted into the customer's own Kubernetes cluster, authenticating against
the customer's own OIDC provider. No shared control plane, no shared secret store.
This satisfies data-residency requirements that hyperscaler-hosted agent
platforms cannot meet by design. SaaS is also available for mid-enterprise.

## Differentiation vs AWS Bedrock AgentCore and Google Gemini Enterprise
- Runs in the customer's own cluster and OIDC provider (data residency).
- Model and framework freedom; governs unmodified third-party agents (e.g. Strands).
- Cross-cloud visibility rather than single-provider-native.
- Agent-semantic policy (models, tools, commands, egress) rather than cloud primitives.
- Hash-chained, tamper-evident append-only audit.

## Security capabilities (shipped)
- Platform-brokered credentials: agents never hold long-lived MCP credentials;
  the platform mints short-lived scoped tokens, refresh credentials stay
  platform-side.
- Per-user/per-identity encrypted credential vault; grants isolated per identity.
- Act on behalf of the user: delegated writes attributed to the requester.
- Zero-config MCP authentication: OAuth authorization-server discovery and
  dynamic client registration (RFC 9728 / 8414 / 7591).
- Governed tool registry with default-deny tool access; granular per-operation,
  per-resource MCP scoping.
- Human-in-the-loop approvals: pause-and-resume, timeouts fail closed to denial,
  every transition audited. Step-up authentication for sensitive actions.
- Hardened binary execution: real tools (kubectl, psql, git) under layered
  constraints, not raw shell.
- Build-time risk gating: every skill risk-scored; over-threshold fails the build.
- Default-deny network egress; hash-chained tamper-evident audit.

## Proof
Launch reference customer selected initializ over AWS AgentCore and Google
Gemini Enterprise in a competitive evaluation.

## Contact
mk@initializ.ai