initializ From description to deployment
A conversational skill builder turns a natural-language goal into a working skill — from a curated catalog or AI-generated. Skills stay editable conversationally after deployment.
The platform
One control plane for every agent you run.
initializ is the enterprise control plane for AI agents — a self-hosted platform to build, run, and govern every agent in your estate. Describe an agent and the platform builds, hardens, and deploys it into an isolated workspace — with policy, egress control, guardrails, audit, and cost controls enforced by the runtime itself.
Your cluster
Control plane
ConsolePolicyIdentityAudit
Describe a goal, in plain language
Policy gate risk-scored before build
Build image built in-cluster
Deploy isolated workspace namespace
Run on Forge hardened runtime
Observe usage, events, audit
01
Build agents from a description No Dockerfile, no CI setup — and a policy gate before anything ships.
In-cluster image builds
Images build inside your cluster — source never leaves the platform. Push to GHCR, or credential-less Amazon ECR via IAM/IRSA. No static registry secrets anywhere.
Bring your own image
The initializ CLI deploys pre-built agent images from any pipeline. CI-managed agents are locked against console drift — config changes only via the pipeline.
Fail before you ship
A pre-build policy gate fails a forbidden configuration at build time with a precise message — not a crash-loop in production. Live build progress and failure diagnostics surface in the console.
02
A hardened runtime Every platform-built agent runs on Forge — and connects to the chat channels your teams already use.
Deny-all egress by default
Explicit allowlists enforced by NetworkPolicy and in-process. Your security team sets the exceptions.
Tamper-evident audit
A hash-chained, per-invocation sequence covering every LLM call, tool execution, egress decision, and guardrail verdict.
Context compression
Reversible compression of tool output and history cuts billed input tokens 30–60% in our validation runs (all values tokenizer estimates) — savings reported per org, workspace, and agent.
Identity end to end
Platform-minted per-org agent tokens verified server-side; OIDC for users.
Per-tool authentication modes
Each tool is independently platform-authenticated, user-authenticated (acting as the requester), or gated behind human approval — chosen per tool, enforced by the runtime.
Live tool health
Every tool resolves to ready, degraded, or unavailable — agents degrade gracefully rather than failing hard.
03
Multi-agent workflows Any framework, any model — with humans in the loop where it matters.
Plan from a goal
The planner drafts a multi-step pipeline across your agents (A2A JSON-RPC dispatch), editable on a canvas.
Schedule and replay
Cron scheduling, execution history with a per-step runtime timeline, and run inputs as a plain-text form — not raw JSON.
No silent failures
Workflows referencing a removed agent are marked orphaned — visible, editable, refused at run time.
04
Bring your own agents Works with the agents you already have — any A2A-compliant agent, anywhere.
Register by Agent Card
URL or inline; the card’s declared security schemes drive the credential form.
Credentials stay sealed
AES-256-GCM, attached by the orchestrator on every dispatch — never readable back.
First-class steps
External agents join workflows alongside platform agents, with SSRF-guarded card fetching and a workspace-scoped registry.
Zero-config MCP registration
Connect an MCP server with just its URL — OAuth discovery and dynamic client registration are automatic. Credentials are stored encrypted, never in agent config.
05
An intelligent console
Ask the platform
From the Home screen or the ⌘K palette, a built-in intent router answers questions grounded in your live platform data — usage, events, agents, workflows — with links into the right screens.
Create by describing
“Watch our status page and alert #incidents” becomes a seeded agent or workflow draft, with one-click correction if the intent was misread.
06
Observability & Ops Real-time visibility into what agents do — and what they cost.
Grouped by invocation
Per-agent activity, bounded container logs, and health diagnostics in one operations view.
AI-assisted Diagnose
One click gathers health, build errors, recent events, and logs — and returns an explainable diagnosis with citations into that evidence.
Usage & FinOps
Token consumption by org, workspace, agent, provider, and model — trends, execution counts, and compression savings (tokens avoided and savings rate).
07
Enterprise deployment Self-hosted, inside your boundary.
Your cloud, your cluster
Helm chart install, verified on EKS with Amazon DocumentDB and ECR. Works behind enterprise LLM gateways (OpenAI-compatible) or direct provider endpoints — with managed multi-provider access via the built-in gateway.
Isolated by namespace
One Kubernetes namespace per workspace; deploy agents to the platform cluster or to remote clusters registered as Environments.
Versioned and contained
Semver-tagged releases; browser-only access through a single console — backends are never exposed to the browser.
The third pillar
Governance is the differentiator.
Agent-semantic policy, typed guardrails, token budgets, and hash-chained audit — enforced fail-closed on unmodified agents.
Explore governance →08
Platform questions
What is the initializ platform?
initializ is the enterprise control plane for AI agents — the layer that orchestrates, governs, and observes agents in production, independently of how each agent was built. It manages identities, tool permissions, model access, policy enforcement, and audit from one self-hosted platform.
How does initializ orchestrate multi-agent workflows?
initializ composes, routes, and supervises multi-agent workflows from one control plane — across any framework or model, with human-in-the-loop steps where they matter.
What observability does initializ provide?
Real-time tracing, cost, and latency across every agent at scale — plus a hash-chained, tamper-evident audit trail you can hand to security and auditors.
Where does initializ run?
In your own Kubernetes cluster, authenticating against your own OIDC provider. There’s no shared control plane and no shared secret store — data residency stays inside your boundary.
Do my agents have to be built with Forge to use initializ?
No. initializ is framework-agnostic — it governs Forge, Strands, LangGraph, and custom agents, unmodified.
Do I need a CI pipeline to deploy an agent?
No. Describe the agent and the platform generates its skill, builds a hardened image in-cluster, and deploys it — no Dockerfile, no CI setup. Teams with existing pipelines can deploy pre-built images with the initializ CLI; CI-managed agents are locked against console drift.
See the control plane on your own cluster.
Governance, security, and observability for every AI agent you run — on your terms.