The operating system for your AI. Traces, experiments, flags, security — one event stream, everything connected.
SOUND FAMILIAR?
When a 4-agent pipeline fails at step 3, you need to see the exact handoff that broke — not grep through logs across services hoping to find a clue.
63% of multi-agent failures go undetected for over 24 hours. By the time your team notices, users have already churned.
Grep logs across 4 services. Check each agent individually. Correlate timestamps manually. Hope you find it before the next standup.
One click. Full trace. The exact span where the handoff failed, with inputs, outputs, and the error message. Fixed in minutes.
One agent quietly consumes 10x the tokens of every other agent combined. Your monthly bill tells you the total — but never which agent caused it.
Unpredictable cost is the #1 reason agentic AI projects get cancelled by leadership. Gartner, 2025.
End-of-month surprise. $4,000 bill. No idea which agent, which model, or which prompts consumed the most. No way to set limits.
Per-agent, per-model cost breakdown in real time. Budget alerts before they spike. Know exactly where every dollar goes.
Agent A delegates to B, B delegates to C, C delegates back to A. The cycle repeats silently, burning thousands of tokens with no output.
A single undetected loop can burn $50+ in API costs in under a minute. No existing observability tool detects delegation cycles.
Your API bill spikes. You notice hours later. You can't tell if it's legitimate load or a loop. There's no tool that even checks for this.
Automatic DFS cycle detection across the delegation chain. Amber warning fires the instant a loop is detected. Configurable depth limits.
A teammate updates a system prompt. Eval scores drop 23% overnight. Users start complaining about quality — your team finds out from support tickets.
The average time to detect a prompt regression is 3.2 days. By then, user trust has already eroded.
No baseline. No monitoring. No alert. You learn about quality drops from angry customers, not from your tooling.
Continuous eval scoring against baselines. Alert fires within minutes of a regression. Automatic diff shows exactly which prompt change caused it.
Your new prompt seems better in manual testing. But 'seems better' isn't data. Without controlled experiments, you're gambling with production quality.
78% of teams ship prompt changes with zero statistical validation. Most don't even know what their current success rate is.
Manual testing with 5 examples. 'Looks good to me.' Ship to production. Cross fingers. Find out from users if it was actually worse.
Controlled A/B experiments with traffic splitting. Statistical significance calculated automatically. Guardrails prevent shipping regressions.
Your agents handle customer data, API keys, and sensitive information. A single prompt injection or PII leak can mean regulatory fines and lost trust.
EU AI Act enforcement begins 2026. Organizations face fines up to 7% of global revenue for non-compliance.
No input validation. No output scanning. PII leaks discovered days later. Prompt injections succeed silently. Jailbreaks go unlogged.
Real-time input/output scanning. PII auto-redaction. Prompt injection blocking. Every security event logged with full trace context.
Agent OS was built to solve all six — from day one.
Most teams stitch together 4-6 separate tools. Here, everything connects through one event stream.
Every LLM call, tool use, and agent handoff — reconstructed as a navigable span tree.
Cost, latency, error rates across all agents. Click any data point to drill into the trace.
A/B test prompts, models, and configs with built-in statistical significance.
Gradual rollouts targeted by user segment or agent type. Instant kill switches.
Prompt injection, PII detection, and jailbreak blocking — in real time.
Map multi-step agent workflows. See where they drop off and why.
All connected through one unified event stream
We're onboarding founding teams one at a time. Three lines of code. Five minutes to your first trace.
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