AI Ops Observability Stack

The model-to-system interface that lets an LLM trigger external actions.

Forcing AI responses into predictable schemas that software can use.

Monitoring inputs, model calls, outputs, cost, latency, and failures across AI workflows.

Runtime checks that constrain LLM inputs and outputs to keep behavior safe and on-spec.

Track agent runs, failures, cost, and review queues from one operational surface.

Generate weekly business reports from operational data with AI commentary.

Classify operational alerts, identify likely causes, and route fixes automatically.

Version, evaluate, and reuse prompts as operational assets rather than loose text snippets.

This stack uses an AI browser, an LLM, and an orchestration layer to autonomously monitor IT systems, identify anomalies from web-based dashboards, and generate actionable insights for operations teams. It enhances proactive problem resolution.

This stack provides tools to monitor, analyze, and optimize the economic performance of AI agents, focusing on token costs, performance, and ROI.

This stack provides tools and services for monitoring, analyzing, and controlling the operational costs associated with AI agent deployment and LLM usage.

Traces, cost, evals and quality drift for AI systems in production.

Design safe tool schemas before connecting an AI model to real actions.

Identify weak points, missing controls, and automation risks in a workflow.

Classify alerts and route incidents with evidence and recommended next steps.

A lean operations team replaced manual reporting with an AI reporting dashboard.

Invoice automation gained anomaly triage and human approvals for high-risk cases.

Engineering team wires an LLM into PR review as a first-pass gate.