ARMS (Agent Resource Management System) is an AI observability and governance platform for production agentic workflows. Trace prompts, tool calls, retrieval steps, model outputs, latency, token usage, and costs across every agent execution. Detect failures, monitor policy compliance, enable human oversight, and maintain audit-ready records. Built for enterprises running AI agents at scale across LangGraph, LangChain, OpenAI Agents, AutoGen, and custom frameworks.

👋 Hi Product Hunt, We're excited to launch ARMS (Agent Resource Management System). As more organizations move AI agents into production, we kept seeing the same challenge: when something goes wrong, teams often struggle to understand what happened, why it happened, and who is accountable. Traditional monitoring tools were never designed for agentic systems. ARMS was built to give teams visibility into every prompt, tool call, retrieval step, decision, policy check, and outcome across AI workflows. Our goal is simple: ✅ Make agent behavior observable ✅ Make AI operations governable ✅ Keep humans in control of high-impact decisions Whether you're running a single agent or a large multi-agent ecosystem, ARMS helps engineering, operations, and compliance teams understand what's happening in production. We'd love your feedback: • What are your biggest challenges with AI observability today? • How are you monitoring agents in production? • What governance capabilities are most important for your organization? Thanks for checking out ARMS. We're looking forward to hearing your thoughts.

AI Observability
LLM Cost Management to show a token cost for agents processing
Guardrails for Policy enforcement

They are actually scaling ARMS for enterprise AI operations. All good.

It gives end-to-end visibility of what agents are doing, token cost, confidence score of output agents generated, elsai actually automating operations as an agentic solution along with ARMS, so trustworthy and getting real ROI using it.

I've been using elsai ARMS during development to monitor LLM applications, particularly for healthcare AI solutions, and it has significantly simplified observability for AI workloads. The SDK is straightforward to integrate, requiring minimal code changes while automatically capturing key metrics such as token usage, latency, model calls, costs, and execution traces. From a developer perspective, what stands out is that ARMS focuses on practical operational metrics instead of just logging requests. Having centralized visibility into model performance, execution history, and resource consumption makes debugging and optimization much easier during development and production. For healthcare applications, where AI powers clinical documentation, medical summarization, patient support, and healthcare workflow automation, ARMS provides the visibility needed to monitor performance, optimize resource utilization, and maintain reliable AI operations. Overall, ARMS provides a solid observability layer for LLM applications and continues to evolve with useful features and integrations.

Easy to integrate, centralised data storage and easy to navigate dashboards.

AI Observability
LLM Cost Management to show a token cost for agents processing
Guardrails for Policy enforcement

They are actually scaling ARMS for enterprise AI operations. All good.

Easy to integrate, centralised data storage and easy to navigate dashboards.

elsai ARMS

Observe, govern, and control AI agents in production

Observe, govern, and control AI agents in production

What's great

What needs improvement

vs Alternatives

What's great

vs Alternatives

What's great

What needs improvement

vs Alternatives

What's great

vs Alternatives