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Beyond Safety & Security
AI Observability
Built Into Every Interaction
Running your AI through Aiceberg doesn't just protect it — it illuminates it. Understand what your users want, how your agents perform, and where your AI spend is going.
Safety Gives You the Data.
Observability Gives You the Insight.
Every AI interaction that passes through Aiceberg Guardian is already being analyzed for safety and security. That same analysis produces a rich stream of metadata — user intent, topic classification, token consumption, latency, and workflow structure — that most organizations have no way to access today.
Aiceberg turns this into a live operational lens across every AI-powered application, agent, and workflow in your organization.
User Intent
What are users actually trying to accomplish with your AI?
Topic Analysis
Which subjects dominate your AI conversations?
Token Economics
Where is your AI spend going — by agent, workflow, and team?
Workflow Health
Which component is causing latency or degradation?
What Observability Unlocks
Six dimensions of insight that emerge naturally from securing your AI interactions.
User Intent Analytics
Understand why users engage with your AI. Are they seeking information, creating content, troubleshooting, or doing something you never anticipated?
Real-time intent classification across all interactions
Trend analysis to track shifts in user behavior
Gap detection — what users want but your AI can't deliver
Topic Intelligence
See which subjects dominate your AI conversations. Discover emerging themes before they surface in support tickets or product requests.
Automatic topic clustering and trending
Cross-application topic comparison
Anomaly alerts for unexpected topic spikes
Token Economics
Know exactly where your AI budget is going. Track consumption per agent, per workflow, per team — and identify runaway costs before they escalate.
Per-agent and per-workflow token tracking
Cost attribution by business unit
Usage anomaly and spike detection
Autonomy Scoring
Measure how independently your agentic workflows operate. A high autonomy score means fewer human escalations and more reliable end-to-end processing.
Per-workflow autonomy metrics
Human-in-the-loop frequency tracking
Autonomy trend over time as workflows mature
Degradation Root Cause
When an agentic workflow slows down or fails, pinpoint exactly which component is responsible — the orchestrator, a specific agent, an external tool call, or the model itself.
Component-level latency tracing
Failure chain analysis for multi-step workflows
SLA tracking and alerting per component
Agent & Workflow Inventory
Maintain a live catalog of every AI agent and agentic workflow deployed or in development across your organization — no more spreadsheets or guesswork.
Auto-discovered agent registry
Development vs. production status tracking
Ownership, dependencies, and model mapping
Agentic Workflow Deep Dive
Drill into any agentic workflow to see how each component performs. Identify bottlenecks, trace failures, and understand exactly where human intervention is required.
Trigger
Inbound Request
12ms avg latency
→
Orchestrator
Task Router
45ms avg latency
→
Agent
Data Retriever
2.8s avg latency ⚠
→
Agent
Response Generator
340ms avg latency
→
Guardian
Safety Check
85ms avg latency
Bottleneck Detected
Data Retriever is responsible for 82% of total workflow latency. Vector DB query times have increased 3x since Tuesday.
Autonomy Alert
Human escalation rate in this workflow increased from 8% to 19% this week. Response Generator is requesting clarification more frequently.
Cost Insight
This workflow consumed 340K tokens today. Response Generator accounts for 71% of token spend — consider prompt optimization.