• 2 min read
Argy AI: multi-provider LLM governance for enterprise workflows
One LLM Gateway across providers: routing, quotas, security filters, and auditability for every model call, from chat to modules.
- Argy AI
- LLM Gateway
- Governance
- Enterprise AI
- DevSecOps
Enterprise adoption of language models fails when model access is fragmented: keys everywhere, inconsistent controls, and no audit trail.
When you add agents and AI steps into real workflows (golden paths), this becomes an operational risk.
Argy AI addresses this with a single, governed entry point: the LLM Gateway.
1) One entry point across providers
Argy AI centralizes requests to supported providers (OpenAI, Anthropic, Mistral, xAI, Google, Azure OpenAI) and exposes an OpenAI-compatible API.
2) Routing, quotas, and security filters
Argy AI can route requests by task type, define fallback chains, and enforce:
- quotas and budgets (per plan / org / team),
- PII redaction and secret detection,
- prompt-injection defense and forbidden topics,
- output masking/blocking.
3) Auditability by design
Every request can be traced (user/model/tenant), with correlation IDs, retention (minimum 90 days), and CSV exports. Optional AES-256 encryption can be enabled for request/response content.
4) Build governed agents inside modules
In Module Studio, you can use the Argy AI action to embed a module-defined AI step (custom prompts + tools) that can orchestrate sub-agents. This is how teams build their own governed AI agents inside versioned modules.
Conclusion
Argy AI is the governance core that makes enterprise AI usable at scale: provider flexibility without chaos, controls without friction, and auditability without gaps.
The technical details live in the Argy AI and Building Modules docs. To see what this foundation would change on your own golden paths, request a demo on your own scope.