Argy AI: multi-provider LLM governance for enterprise workflows

One LLM Gateway across providers: routing, quotas, security filters, and auditability for agents, chat, and modules.

Enterprise AI adoption 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.

Next: read the docs Argy AI and Building Modules, or request a demo.