Agent Compass
Your AI agent’s truth graph: From symptoms to solutions

AI Agent Compass
Your agent’s truth graph: From symptoms to solutions

How it works

Zero-config eval, 4 lines of code in, full agent story out! Agent compass clusters failures and hallucination across runs, uncovers root causes with evidence, and prescribes fixes, so you can debug in minutes and ship reliable agents faster.

Cluster

Automatically group similar failures and hallucinations into 5–10 actionable patterns.

Diagnose

Uncover confidence-ranked root causes with span-level evidence across runs.

Fix

Apply fix recipes: prescriptive steps, suggested experiments, and workflow integrations to ship changes quickly.

Analyze

Insights on real users impacted, narrative timeline of incidents and aggregated performance views across entire agent fleet,

Problem

Span-by-span metrics don’t explain system-level failures

Enterprises building agentic systems struggle to pinpoint bottlenecks across complex, multi-tool flows. Metrics are siloed, forcing teams to waste hours chasing latency spikes, prompt drift, tool-call errors, and reactively and at scale.

Solution

From noisy traces to clear causal answers

Agent compass transforms thousands of traces into a handful of failure patterns, then provides root-cause explanations with linked evidence (prompt drift, API latency, retrieval gaps, model/version drift, missing guardrails). Finally, fix recipes turn diagnosis into guided steps you can ship.

Key capabilities

Zero-config evaluation

4-line install for instant health insights, No evaluators to write.

Zero-config evaluation

4-line install for instant health insights, No evaluators to write.

Zero-config evaluation

4-line install for instant health insights, No evaluators to write.

Pattern-first debugging

Automatic clustering highlights recurring issues and shared root causes.

Pattern-first debugging

Automatic clustering highlights recurring issues and shared root causes.

Pattern-first debugging

Automatic clustering highlights recurring issues and shared root causes.

Root-cause graphs

Confidence-ranked cause paths eliminate the “now what?” moment.

Root-cause graphs

Confidence-ranked cause paths eliminate the “now what?” moment.

Root-cause graphs

Confidence-ranked cause paths eliminate the “now what?” moment.

Incident timeline

Feed-style history with drill-down context and evidence.

Incident timeline

Feed-style history with drill-down context and evidence.

Incident timeline

Feed-style history with drill-down context and evidence.

System-level reliability views

Aggregate reliability across agents, scenarios, and releases, not just spans.

System-level reliability views

Aggregate reliability across agents, scenarios, and releases, not just spans.

System-level reliability views

Aggregate reliability across agents, scenarios, and releases, not just spans.

Actionable orchestration

Fix Recipes + PR/Jira hooks turn insights into shipped fixes.

Actionable orchestration

Fix Recipes + PR/Jira hooks turn insights into shipped fixes.

Actionable orchestration

Fix Recipes + PR/Jira hooks turn insights into shipped fixes.

Intergrations & Installation

Works with your stack in minutes

Research paper

AgentCompass: Towards Reliable Evaluation of Agentic Workflows in Production

NVJK Kartik, Garvit Sapra, Rishav Hada, Nikhil Pareek

With the growing adoption of Large Language Models (LLMs) in automating complex, multi-agent workflows, organizations face mounting risks from errors, emergent behaviors, and systemic failures that current evaluation methods fail to capture. We present AgentCompass, the first evaluation framework designed specifically for post-deployment monitoring and debugging of agentic workflows. AgentCompass models the reasoning process of expert debuggers through a structured, multi-stage analytical pipeline: error identification and categorization, thematic clustering, quantitative scoring, and strategic summarization.