AI Observability
Monitoring, tracing, evaluation, and audit for every agent and workflow — every action logged immutably, token, cost, and latency visible, quality and hallucination measured — so organisation-wide AI stays accountable.
Trace Every Action
End-to-end tracing of agents and workflows — every step, tool call, and decision captured so you can see exactly what happened.
Cost, Token & Latency Visibility
Live visibility into token usage, cost, and latency across agents and workflows, so AI spend and performance stay under control.
Quality & Hallucination Evals
Continuous evaluation of output quality and hallucination, so you measure whether AI answers can be trusted — not just that they ran.
AI observability (also called LLM observability) is the monitoring, tracing, evaluation, and audit of AI agents and workflows in production. On the AI OS it means every agent action is captured in an immutable audit trail with actor and IP context, token usage, cost, and latency are visible end to end, and outputs are continuously evaluated for quality and hallucination — so organisation-wide AI stays accountable, debuggable, and under control.
You cannot govern what you cannot see. As agents and workflows act across the organisation, you need to know what they did, what it cost, how well they performed, and whether their answers can be trusted. AI observability gives the AI OS that visibility: full traces of every agent and workflow step, immutable audit of every action, live token, cost, and latency metrics, and quality and hallucination evaluations. It is the same machinery that powers governance and compliance — turning AI from an opaque black box into an accountable system you can monitor, debug, and prove.
AI Observability in the Scrydon platform
One integrated, sovereign architecture. Here is where AI Observability sits — highlighted against the full stack it works with.
The AI OS for Humans & AI Agents to enable your processes
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Link your processes, knowledge & data to ontologies.
Unified storage, structured compute, and secure multi-modal data processing.
Autonomous operatives with specialised skills executing tasks across systems.
Sovereign pipelines, federated APIs, and seamless connector meshes.
Secure domain federation, trusted data sharing, and cross-boundary intelligence.
Monitoring, tracing, evaluation, and audit
AI observability instruments every agent and workflow on the platform. Each action is traced and logged, performance and cost are measured, and outputs are evaluated for quality — so the people accountable for AI can see what it is doing, why, and how well, in real time and after the fact.
Distributed tracing — Follow a request across agents, tool calls, and workflow steps end to end, so failures and slow steps are easy to pinpoint.
Immutable audit — Every agent and workflow action is logged immutably and queryably, with full actor and IP context and sensitive fields redacted.
Cost and performance metrics — Token usage, cost, and latency are tracked per agent, workflow, and model, so spend and SLAs stay visible.
Quality and hallucination evals — Outputs are evaluated for accuracy, grounding, and hallucination, so quality is measured continuously rather than assumed.
Accountable AI at organisation scale
When AI runs across the whole organisation, opacity is a risk: undetected drift, runaway cost, silent failures, and answers no one can trace. AI observability removes that opacity. The same immutable audit, tracing, and evaluation that keep AI debuggable also make it accountable — providing the evidence governance and compliance require, and the visibility teams need to operate AI safely. Observability is what lets you trust organisation-wide AI in production, not just in a demo.
Frequently asked questions
What is AI observability?+
How is LLM observability different from traditional monitoring?+
Can I track AI cost, tokens, and latency?+
How does observability help detect hallucination?+
Is there a complete audit trail of agent actions?+
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