AI observability platforms
Latency is green while task success collapses. Teams cannot see why an agent looped, which tool failed, or when retrieval quality drifted.
Why common approaches fail
Infra APM and chat logs are not an AI failure taxonomy. Without traces tied to tasks, evals, and tool outcomes, operators guess.
Activation insight
Low latency does not prove an AI system is healthy.
How Knackline solves it
We instrument the AI path: spans for plan, retrieve, tool, gate, and deliver; eval drift signals; and taxonomies operators can act on.
System components
Task-level traces
Correlate steps to whether the task actually succeeded.
Eval drift monitoring
Catch regressions when prompts, indexes, or models change.
Tool-error taxonomy
Classify misuse, timeouts, and permission failures.
Operator views
Run-level visibility for the people who get paged.
Operational hardening
SLOs beyond latency, alert on task failure rates, and join observability to containment so bad runs can be stopped.
Evidence from reports
Agent observability: traces that show what the agent did, not only latency
When something goes wrong, latency dashboards cannot tell you which tool call or plan step caused the damage.
AI Agent Security and Observability
Unreconstructable AI agent failures
SLO Design for AI Agent Workflows: Beyond Latency
Green p95 latency hides collapsing task success and rising human escalations
Eval gates in CI: shipping prompt and index changes like code
A Friday prompt edit that improves three demos can tank the failure set that actually runs in support.
FAQ
- Is this just logging prompts?
- No. Prompt logs without task outcomes and tool taxonomies do not tell operators what to fix. We design observability around failure modes.
- What should we alert on?
- Task success rate, tool misuse rate, retrieval groundedness, eval drift, and containment events, not only p95 latency.