Full-chain tracing, real-time evaluation, AI-powered debugging, and human-in-the-loop annotation — all built into the Agent OS at zero extra cost.
OBSERVABILITY TOOLKIT
From production tracing to offline regression testing, every tool you need to understand, evaluate, and debug your agents — built in, not bolted on.
Every decision, visible
Visualize every decision, tool call, and model response for every step of every agent mission. Drill into token usage, latency, and cost at each node.
LLM-as-Judge, real-time
Real-time scoring of production traffic across dimensions: hallucination, reasoning quality, safety, and more. Catch regressions the moment they appear.
Ship with confidence
Run offline evaluation and regression test suites before every deploy. Compare versions side-by-side to ensure updates never degrade quality.
AI-powered failure analysis
An AI agent that automatically analyzes your traces, clusters failure patterns, and surfaces emerging issues before they become systemic problems.
Ask your traces a question
Query your trace data with natural language. Ask "Why did this agent retry three times?" and get a structured answer with the exact spans that caused the issue.
Human-in-the-loop review
Low-scoring traces are automatically routed to human reviewers. Build a continuous quality feedback loop that makes your agents smarter over time.
Debug and iterate online
Online prompt debugging and version comparison. Test prompt variations side-by-side, compare outputs, and iterate without redeploying your agents.
Data-driven optimization
Run A/B experiments comparing different prompts, models, and strategies. Statistical significance testing ensures you ship the variation that actually performs better.
Monitor what matters
Pre-built monitoring dashboards for cost, latency, and error rates with configurable alert thresholds. Know the health of every agent at a glance.
DEEP DIVE 01
An AI agent that watches your AI agents. It continuously analyzes trace data, clusters failure patterns, and proactively surfaces emerging issues — so you fix problems before users notice them.
DEEP DIVE 02
Stop reading JSON traces manually. Ask questions in plain English — "Why did this mission fail?" — and get a structured root-cause analysis with the exact spans, tool calls, and model decisions that led to the issue.
DEEP DIVE 03
The bridge between automated evaluation and human judgment. Low-scoring traces are automatically routed to a review queue where domain experts label, correct, and feed data back into your evaluation pipeline — creating a continuous improvement loop.
DATA FLOW
Every agent mission flows through a six-stage observability pipeline — from trace collection to continuous optimization.
INTEGRATIONS
Export traces and metrics to your existing observability infrastructure via OpenTelemetry, or use the built-in dashboards and alerts to monitor everything in one place.
Native OTel support — send traces and metrics to any OTel-compatible backend
Anomaly detection with PagerDuty, Webhook, and Slack integrations
Pre-built cost, latency, and error rate views with configurable thresholds
Works alongside Datadog, New Relic, Grafana, and other APM tools