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Anosys AI Operational Intelligence Platform

Anosys helps teams operate production AI systems with evidence. It connects user behavior, application events, agent traces, model calls, tools, APIs, infrastructure, cost, evals, and business KPIs so teams can understand what happened, why it mattered, what it cost, and what action should happen next.

Observability is one capability inside Anosys. The broader goal is operational intelligence: turning fragmented telemetry into root cause, cost attribution, quality monitoring, governance, and reusable investigations.

How It Works

Anosys accepts signals from AI frameworks, backend services, IoT devices, web applications, business processes, and enterprise systems through OpenTelemetry, SDKs, REST/HTTP events, JavaScript, pixels, files, and custom pipelines. Those signals are correlated into root cause, cost attribution, quality monitoring, governance evidence, and owner-routed workflows.

Anosys Anosys AI Operational Intelligence
01

Ingest

OpenTelemetry, REST, SDKs, logs, evals, costs, product events, and business process signals.

02

Correlate

Join user, application, agent, model, tool, infrastructure, and KPI context in one timeline.

03

Explain

Connect anomalies, root cause, regressions, quality gaps, and spend drivers to evidence.

04A

Debug AI failures

Find the slow span, bad tool call, failed handoff, release change, or infrastructure issue.

04B

Act

Route incidents, trigger workflows, create reports, and close the loop with owners.

04C

Control AI spend

Attribute token and model costs to agents, workflows, teams, users, and outcomes.

04D

Improve outcomes

Compare production behavior against evals, safety, latency, user experience, and business KPIs.

04E

Govern production AI

Monitor quality, policy, sensitive data controls, SLAs, ownership, and audit trails.

AI Agent Debugging and Monitoring

AI agents are non-deterministic, multi-step, and expensive. A single coding session or agent run can consume thousands of tokens and still produce a poor customer outcome. Anosys gives you full-stack tracing, cost intelligence, evals, and root-cause analysis for agentic workflows out of the box.

What You Get Why It Matters
End-to-end traces See every reasoning step, tool call, and model invocation in a single timeline
Token usage metrics Track input/output tokens per session, per model, and over time
Latency breakdowns Identify slow calls and bottlenecks across your agent's execution
Error classification Structured logs with automatic error grouping and alerting
Cost attribution Per-session, per-user, per-team, per-workflow, and per-project cost estimates based on real usage

Supported Frameworks

Anosys integrates with the leading AI agent frameworks:

  • OpenAI Agents — Auto-instrument the OpenAI Python and JavaScript SDKs with a few lines of code. Captures model parameters, tool calls, streaming events, and per-request cost breakdowns.
  • OpenAI ChatKit Apps — Monitor ChatKit-powered chat widgets with a single React hook. Captures every conversation turn, time-to-first-token, and user actions.
  • Claude Code — One-command SDK hook installation for full session observability — token usage, cost attribution, subagent tracing, and optional content redaction. Also supports OTEL-only configuration.
  • Any LLM Provider — Integrate Google Gemini, Meta Llama, Mistral, Cohere, AWS Bedrock, and any other provider via OpenTelemetry or the REST API.

Beyond Observability — Operational Intelligence

Anosys is not limited to model traces or LLM telemetry. The platform supports any signal that helps explain operational behavior:

  • Infrastructure — Monitor servers, containers, and network devices via OpenTelemetry or custom API calls.
  • Web & Mobile — Track user behavior and application performance with lightweight JavaScript pixels or image beacons.
  • IoT — Collect sensor data and device telemetry through HTTP GET/POST endpoints.
  • Business Processes — Monitor order flows, onboarding, claims, support escalations, supply-chain steps, and back-office workflows.
  • Custom Workflows — Use Python decorators, REST APIs, OTLP exporters, files, or pipelines to instrument anything.

Use the HTTP, OTEL, JavaScript, pixel, and file ingestion reference when you know how the signal will be sent, or explore the Use Cases page for examples including AI agent debugging, cost intelligence, release monitoring, business process monitoring, website analytics, IoT, and custom KPIs.

Implementation Paths

Use these guides when you already know what you want to build.

Goal Start Here
Trace an AI agent run span by span Agent Tracing
Alert on failures, cost spikes, eval drops, and SLA risk Alerts
Score AI outputs automatically AutoJudge
Run quality, safety, regression, and business outcome checks Evals
Build executive, product, ML, and operations views Custom Dashboards
Investigate incidents in plain English AI Assistant
Transform, enrich, route, and schedule telemetry work Custom Pipelines
Model reusable operational workflows Process Units
Monitor business processes and client pain points Business Process Monitoring
Attribute token and model spend to teams, users, workflows, and outcomes Cost Intelligence
Govern production AI with policies, ownership, and audit trails Governance
Protect sensitive telemetry with encryption controls Data Encryption

Key Capabilities

AI Observability & Agent Tracing

Follow every prompt, completion, model call, tool call, retry, and handoff in a single timeline. Use Agent Tracing to connect production behavior to latency, cost, evals, user experience, and business KPIs.

Custom Dashboards & Visualization

Build real-time dashboards for any ingested metric or field. See Custom Dashboards for health views, cost dashboards, reliability scorecards, SLA panels, and business process views.

AI Assistant

Ask questions about your data in plain English and get answers backed by traces, logs, metrics, evals, costs, and business context. See AI Assistant for investigation workflows.

Alerts & AutoJudge

Go beyond simple threshold alerts with eval-based checks. Use Alerts, AutoJudge, and Evals to catch quality regressions, policy failures, spend anomalies, and client-impacting issues.

Root Cause Analysis

When something breaks, drill down from a KPI, alert, or business outcome to the exact request, model call, tool call, process step, or configuration change that caused it. Start with Agent Tracing, AI Assistant, and Process Units.

Custom Pipelines

Build data processing pipelines that transform, enrich, route, and schedule telemetry work. See Custom Pipelines for enrichment, anomaly jobs, eval runs, incident routing, and report generation.

Business Process Intelligence

Model operational workflows as process units, monitor SLA and KPI health, evaluate outcomes against policies, and root-cause client pain points in seconds. See Business Process Monitoring.

Cost Intelligence

Analyze token usage patterns, identify waste, compare model cost-efficiency, attribute spend by team/workflow/user/model/tool, and optimize agent configurations. See Cost Intelligence.

Governance & Data Protection

Define ownership, policies, audit trails, retention controls, and sensitive data protection for production AI. See Governance and Data Encryption.

Getting Started

Get up and running in minutes:

  1. Sign up at console.anosys.ai and create your workspace.
  2. Create a pixel — choose your integration type (Agentic AI, OpenTelemetry, API, or Web).

  3. Send data — follow the guide for your integration:

  4. Explore insights — data appears in your Anosys dashboards within seconds.

Security & Compliance

Your data is encrypted with AES-256 in transit and at rest. Anosys supports SOC 2 compliance and role-based access control (RBAC) for multi-user environments. Choose your cloud region — U.S., EU, or APAC — powered by Google Cloud.

Questions?

Check the FAQ or reach out to support@anosys.ai.