Observability: every agent action, recorded at the point it happens
We built Observability where ArmorIQ already sits: the enforcement boundary between agent and tool/data. Every step gets recorded — what the agent planned, what it actually called, what we allowed or blocked, and the reason, plus latency and token cost per run.
An agent run isn’t a black box anymore. It’s a sequence you can replay: plan → action → decision → cost. When something gets blocked, you see the exact step and the exact reason, not a generic “denied.”
LangChain support:- Shipped the LangChain integration in the TS and Python SDKs (0.6.x), model-agnostic. Adding ArmorIQ to any LangChain agent is a five-line, callback-based drop-in: one callback mints a signed intent token from the model’s chosen plan, another allows, holds for human approval, or blocks each tool call before it runs, all driven by the user’s ArmorIQ policy. No changes to the agent or its tools, the provider is irrelevant to enforcement. Tested end-to-end on a real LangChain agent driving a live Stripe MCP integration with OpenAI (gpt-5.4-mini): per the configured ArmorIQ policy, reads were allowed, refunds held for human approval, and unpermitted tools blocked.
documentation:- https://docs.armoriq.ai/sdk/integrations/langchain
