AI agent observability: autonomous doesn't mean invisible
See what your AI agents do, why they do it and where things go wrong. Trace every agent run, LLM call and tool call, monitor cost and latency, and turn insights into action.
END-TO-END VISIBILITY
End-to-end tracing for every agent, model and tool call
Follow every execution across agents, models, tools and workflows. Understand what happened, where time and money were spent and what caused a failure.
PRODUCTION CONTROL
AI agent monitoring: detect failures and act on them
Identify failures, latency spikes and unexpected behavior. Trigger alerts and route problematic executions for review before issues spread.
From visibility to intervention, without losing the audit trail.
CONTINUOUS IMPROVEMENT
Improve AI agents with production traces, feedback and evals
Connect traces, feedback and evaluations to understand why agents succeed or fail. Use real production signals to improve quality over time.
ENTERPRISE READINESS
Enterprise LLM observability with controlled data exposure
Monitor AI operations with configurable logging, sensitive-data masking and integration into your existing observability stack.
PII masking
Flag sensitive input variables as PII to keep their values out of stored logs.
Logging controls
Choose whether custom traces capture function inputs and outputs with the Python SDK.
OpenTelemetry
Send OpenTelemetry traces from your applications to Orq.ai using OTLP over HTTP.
Trace custom functions with the Orq.ai Python SDK, or instrument supported AI frameworks.
What is AI agent observability?
AI agent observability gives you visibility into how AI agents operate, make decisions and interact with models and tools. It helps teams monitor performance, investigate failures and maintain control over AI workflows in production.
Can I trace multi-agent workflows from start to finish?
Yes. Orq.ai provides visibility across agent executions, model calls and tool interactions, helping teams understand how workflows progress, where issues occur and how individual steps contribute to the final outcome.
How do I investigate failed agent executions?
Orq.ai helps teams inspect execution traces, errors and model interactions to identify where and why a workflow failed. This makes troubleshooting faster and reduces the guesswork involved in debugging complex AI systems.
Can observability help improve Software Factory workflows?
Yes. By tracking agent execution, performance and failures across planning, building, testing and deployment, teams can identify bottlenecks, improve workflow reliability and continuously optimize software delivery. Explore Software Factory.
How does Orq.ai protect sensitive data in logs?
Orq.ai supports controlled access to observability data through governance and permission policies. Sensitive information should be protected through appropriate logging configurations, access controls and data-handling policies, helping organizations balance visibility with security.
If agents are doing the work, you need to see the work.
Understand every execution. Improve every workflow. Keep your autonomous operations under control.