AI Observability
Track adoption, quality, and cost across your AI
Run AI you can explain and improve. Trace any response to the steps behind it, find what went wrong, and know what to fix.
Turn AI visibility into
better outcomes
Keep AI quality on track
Maintain quality and reliability as usage grows, with clear signals when performance changes.
Improve answers faster
Spend less time investigating what happened and more time making the right changes.
Invest where AI delivers value
Focus time and budget on the AI experiences worth improving, expanding, or scaling.
From AI performance to the reason
behind every answer
usage
Understand AI usage at scale
- See query volume, active users, and active workspaces over time.
- Compare adoption and engagement across agents and skills.
- Track quality trends, token usage, and cost.
Tracing
Inspect every step of an interaction
- See which skills were considered and activated.
- Inspect the knowledge and memory used at each step.
- See where execution failed, with timing, model, iteration, and token details.
Intelligence
Find recurring patterns in conversations
- Get an automatic summary and issue flags for individual interactions.
- See which problems recur across conversation history.
- Get recommended changes to knowledge, semantic models, or configuration.
Everything in one place, from adoption to root cause
deployment
Fit observability into your existing stack
AI Observability runs as a managed workspace using the interaction data your AI already generates.
Built for every team
responsible for AI
Data & Analytics
See where data, definitions, or semantic logic affect AI quality and prioritize what to improve.
AI Engineering
Debug AI behavior with evidence from real interactions and improve reliability.
Product
Understand adoption and identify which AI experiences are worth scaling.
Compliance
Review AI behavior with the traceability and audit history needed for oversight.
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Common Questions
AI observability is the ability to see how an AI system behaves in production: how much it's used, how well it performs, what it costs, and what happened inside any individual response. It treats AI agents as systems you can measure and inspect rather than black boxes, so teams can find problems and improve results with evidence instead of guesswork.
Monitoring tells you that something changed, usually through high-level metrics and alerts. Observability tells you why, by letting you trace an individual response down to the steps behind it, the skills, knowledge, and memory it used, and where it failed. Monitoring is the dashboard view; observability adds the ability to investigate any single interaction and act on what you find.
It gives you visibility into usage, quality, consumption, and individual AI interactions. You can track adoption across workspaces, agents, and users, watch quality and cost trends, inspect how any single response was produced, and surface recurring issues across your conversation history.
Yes. Open any interaction to see the steps behind it, including which skills were considered and activated, what knowledge and memory were retrieved, the model calls, and per-step timing, iterations, and token usage. This is how you find exactly where and why a response went wrong.
No. Usage Analytics works with aggregate usage and performance metrics, not conversation content, so it's safe to roll out broadly without a privacy decision. When you need to investigate a specific response, individual interactions are opened separately, on demand.
Yes. Start with prebuilt dashboards and build additional views on the same interaction data to match your own monitoring and reporting needs.
No. AI Observability uses the interaction data your AI already generates, so there's no separate analytics pipeline to build. It runs as a managed workspace with prebuilt dashboards, and you can build your own views on the same data.
Yes. AI Observability supports cloud deployment as well as deployment on your own infrastructure, so your interaction data can stay inside your environment.
