AI Observability

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AI Observability

Tracing, monitoring, cost & token visibility.

You cannot operate what you cannot see. We instrument your AI systems with full observability — every prompt, every tool call, every decision, every cost. When something goes wrong at 2am, your team has the traces, dashboards, and alerts they need to diagnose and fix it fast.

What we deliver

  • Tracing and logging architecture across all agent interactions
  • Cost and token monitoring with per-agent and per-workflow attribution
  • Prompt and response analytics for quality and drift detection
  • SLA and performance dashboards for operations and executive reporting
  • Anomaly detection setup with configurable alerting thresholds
Example engagement

Instrumented full prompt-to-action tracing across 40+ production agents for a banking client. Reduced debugging time by 50%, achieved 100% cost visibility per workflow, and surfaced 3 latency regressions before they impacted SLAs.

40+
Agents Instrumented
−50%
Debug Time
100%
Cost Visibility
Tools & frameworks
LangSmithLangfuseOpenTelemetryPrometheusGrafanaDatadogCustom Dashboards

Common questions

Tracing captures the full execution path of an agent run — the input prompt, every tool call made, the data retrieved, the reasoning steps, the final output, and the latency and cost of each step. It is the equivalent of distributed tracing for microservices, applied to AI workflows. Without it, debugging a misbehaving agent is guesswork.

We build a unified cost attribution layer that normalizes token usage and pricing across providers (OpenAI, Anthropic, Google, Azure OpenAI, etc.) and attributes costs to specific agents, workflows, users, or business units. This gives you the data to optimize spend and charge back costs accurately.

Yes. We instrument existing agents regardless of the framework they were built on — LangChain, LangGraph, CrewAI, AutoGen, custom implementations. The instrumentation is typically non-invasive and does not require changes to agent logic.

Prompt drift is when the distribution of inputs to your agent shifts over time — users start asking different types of questions, or upstream data changes in ways that degrade agent performance. We set up statistical monitoring on input and output distributions so you detect drift before it causes visible quality degradation.

Other practices