The operating system for enterprise AI, with DevSecOps as the first use case

Argy sits between your teams, your toolchain, and LLM providers to govern AI usage, build AI apps and workflows, and industrialize DevSecOps through reusable modules and golden paths.

Explore next: automatable actions, use cases, why Platform Engineering.

Argy — the operating system for enterprise AI

Argy sits between teams, models, and the Your toolchain is your set of tools (Git, CI/CD, cloud, IaC, security). Argy doesn’t replace them: it orchestrates them and standardizes the experience.: an LLM Gateway for every request, governed agents, and An Argy module is a versioned workflow made of actions (nodes) and connections, with inputs/outputs schemas. It can behave like an agent with tools through its actions, including the Argy AI action (a module-specific subagent). that industrialize A workflow is an orchestrated sequence of actions (DAG). In Argy, workflows become versioned modules that can be governed and reused..

Argy is the enterprise AI OS: standardize once, govern all AI usage, and ship safely across In Argy, a project groups an application/service and its environments (dev/staging/prod). It’s the unit where you apply modules, deployments, and policies. and environments.

Portal/CLI → LLM Gateway + Modules → Models & Toolchain → Governed outcomes

How Argy works

A product interface on top of your toolchain: the platform team defines the path, product teams consume in self-service, and governance stays built-in.

1. Standardize

Define golden paths

Ship a consumable capability: parameters, guardrails, documentation, and run readiness. Version it to evolve without forks.

2. Consume

Governed self-service

Developers pick the right path, fill a clear schema, and get a repeatable outcome (delivery + run) without ticketing.

3. Govern

Control, audit, continuous improvement

Policies, approvals, traceability, observability baselines: security and compliance become the default path—and adoption becomes measurable.

Go further: automatable actions, designing golden paths and consuming them as a developer.

Tenant configuration, made clear

Set identity, LLM providers, and notifications once—then scale with plan-based guardrails.

Identity

SSO + SCIM

Connect Azure AD, Okta, or Google Workspace. Sync users and roles per tenant.

AI

LLM providers & routing

Enable providers, configure routing, quotas, and security filters.

Alerts

Notifications

Route workflow events to Slack or Teams with real-time delivery.

Governance

RBAC & approvals

Apply roles, approval flows, and policy gates across modules and environments.

Launch

Publish the first path

Expose a golden path in the catalog, then onboard teams through self-service.

See the admin guide: tenant configuration.

A pragmatic rollout

The goal is not to “rebuild a platform”. The goal is to ship a first standardized path, then expand with measurable adoption.

1) Prioritize

• Define first outcomes and pilot teams
• Pick one critical workflow (provisioning, delivery, run)
• Set standards (environments, approval policies, audit expectations)

2) First golden path

• Clear configuration schema
• Standardized actions (cloud, CI/CD, security, notifications)
• Approvals and audit built in

3) Adoption & governance

• Team onboarding
• RBAC/SSO and approval workflows
• Manage by usage (costs, quotas, audit)

4) Expand & optimize

• Add new automations
• Observability: DORA metrics, SLOs, drift, incidents
• AI governance: filters, routing, quotas, audit

What you get

An operating layer that speeds up delivery and structures run—without replacing your toolchain.

Guardrailed self-service

Versioned golden paths with approvals and auditability built in.

Observable execution

Pipelines with statuses, real-time logs, artifacts, and outputs.

LLM governance

Multi-provider LLM Gateway with routing, quotas, security filters, and full auditability.

Visual studio + AI assistant

Module Studio to design workflows, simulate, publish, and version modules.

Platform observability

DORA metrics, SLO tracking, drift detection, and incident management.

Custom branding

Custom branding (plan-dependent) to fit your organization.

Sovereign deployment

SaaS (EU), hybrid, or on-prem depending on your network and compliance constraints.

Built for demanding environments

Designed for large enterprises and fast‑growing scale‑ups.

Operational excellence

Governance, auditability, and adoption metrics included.

Standardization at scale

Reusable modules and reduced variability across teams.

Sovereignty & independence

EU hosting, on-prem LLM Gateway, and model-provider flexibility.

Is Argy right for you?

Perfect for

Scale‑ups industrializing quickly.
Enterprises modernizing their toolchain.
Multi‑team, multi‑cloud organizations.

Not for

Very small teams with no automation needs.
Companies seeking a black‑box PaaS.
Projects without standardization goals.

Trust & transparency

Technical depth and clear boundaries: what Argy does, and what it deliberately doesn't.

API-first (doesn't replace your tools)

Argy orchestrates your existing CI/CD, cloud and Kubernetes stack instead of becoming a new black box.

Open standards

Open standards by design: OpenTelemetry, OIDC/SAML, SCIM, MCP, and an OpenAI-compatible LLM API.

European SaaS

Procurement-friendly: GDPR posture, clear hosting assumptions, and enterprise-grade governance.

Governed AI Core

Argy's LLM Gateway is the unified entry point to LLMs, with routing, quotas, audit, and tenant-aware RAG (Retrieval-Augmented Generation) augments prompts with passages retrieved from your documents to deliver grounded, context-aware responses. on your internal knowledge.

1Complete AI governance: quotas, audit, filtering, and document RAG (Retrieval-Augmented Generation) augments prompts with passages retrieved from your documents to deliver grounded, context-aware responses..
2Multi-provider routing for vendor independence without client changes.
3Governed agents and assistants for real enterprise A workflow is an orchestrated sequence of actions (DAG). In Argy, workflows become versioned modules that can be governed and reused..
4OpenAI-compatible API with dynamic model discovery (GET /v1/models).

FAQ

Common questions.

Does Argy replace your existing tools?⌃

No. Argy integrates with your stack (Git, CI/CD, cloud, Kubernetes, observability, identity). Argy’s role is to standardize, automate, and govern through versioned modules (golden paths).

What is an Argy 'module'?⌃

An Argy module is a versioned workflow made of actions (nodes) and connections (DAG), with inputs/outputs schemas. It can behave like an agent with tools through its actions, including the Argy AI action (a module-specific subagent).

What is a Golden Path?⌃

A Golden Path is a versioned module that is validated and approved by the organization. It captures best practices and enables self-service with governance.

How does Argy govern LLM usage?⌃

Through the LLM Gateway: multi-provider routing, fallback chains, quotas, security filters (PII/secrets/prompt injection/forbidden topics), and full request auditability.

What about compliance and traceability?⌃

Approval policies, exportable audit logs (CSV), 90-day minimum retention, correlation IDs, and multi-tenant isolation (PostgreSQL RLS, Redis key prefixes, validated x-tenant-id/x-org-id headers).

Is Argy suitable for large enterprises?⌃

Yes. Argy is built for demanding environments: passwordless-first IAM, RBAC, approval workflows, full auditability, SaaS/hybrid/on‑prem options, and EU sovereignty posture (EU hosting).

Ready to get started with Argy?

Start with the Free plan. Upgrade when you're ready, or contact us for an enterprise rollout.

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