Overview

The future of AI is agentic—autonomous systems that reason, plan, and coordinate across multiple agents to solve complex problems. Fiddler Observability is built for this future, providing comprehensive monitoring across traditional ML models, LLM applications, and emerging multi-agent systems.

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The Challenge: Exponential Complexity

As AI evolves from static models to autonomous agents, observability complexity grows exponentially:

• Multi-agent systems require 26x more monitoring resources than single-agent applications
• Non-deterministic behavior breaks traditional APM frameworks designed for predictable code
• Cascading failures across agent hierarchies create unprecedented debugging challenges
• 90% of enterprises cite security, trust, and compliance as top concerns for agentic AI

Fiddler provides the unified observability platform that scales from simple models to complex agentic workflows—all powered by the same family of Fiddler Centor Models.

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Agentic Observability

Fiddler’s agentic observability provides hierarchical visibility into multi-agent systems, tracking the complete lifecycle of autonomous reasoning and coordination.

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The Five Observable Stages

Every agent operates through five distinct stages that require specialized monitoring: Stage-by-Stage Observability:

1. Thought: Monitor how agents ingest data, retrieve context, and interpret information
2. Action: Track planning processes, tool selection, and decision-making logic
3. Execution: Observe task performance, API calls, and external integrations
4. Reflection: Capture self-evaluation, learning signals, and adaptation decisions
5. Alignment: Verify trust, safety, and policy enforcement at every step

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Hierarchical Monitoring Architecture

Agentic systems operate across multiple levels of abstraction. Fiddler provides observability at each layer: Hierarchical Root Cause Analysis:

• Trace issues from user-facing symptoms down to individual tool calls
• Understand cross-agent dependencies and coordination failures
• Analyze patterns across sessions to identify systemic issues
• Full context preservation for debugging non-deterministic behavior

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Framework & Integration Support

Supported Frameworks:

• LangGraph - Full SDK integration with native tracing
• Strands Agents - Strands agent application monitoring
• OpenTelemetry - Standard instrumentation for custom agents
• Custom Agents - Fiddler Client SDK for any framework

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Unified Observability Platform

All Fiddler observability capabilities—from traditional ML to agentic systems—are powered by a unified architecture built on Fiddler Centor Models: Centor Models Advantages:

• 10-100x faster than general-purpose LLMs for evaluation tasks
• Purpose-built models optimized for safety, quality, and accuracy assessment
• Consistent, deterministic evaluation at scale
• Air-gapped deployment options for data sovereignty
• GDPR, HIPAA, CCPA compliant monitoring

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Core Capabilities

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LLM Monitoring

Comprehensive observability for generative AI applications with trust and safety at the core. Key Features:

• 14+ Enrichment Metrics: Auto-generated trust, safety, and quality scores
• RAG Monitoring: Retrieval quality, source relevance, groundedness
• Embedding Analysis: UMAP visualization, drift detection, clustering
• Prompt & Response Tracking: Full conversation history and context

Trust & Safety Metrics:

• Safety (toxicity, jailbreaking, harmful content)
• Privacy (PII/PHI detection across 35+ entity types)
• Quality (faithfulness, coherence, conciseness, relevance)
• Sentiment and tone analysis
• llm

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ML Model Observability

Battle-tested monitoring for traditional machine learning models in production. Key Features:

• Drift Detection: JSD and PSI metrics for distribution shifts
• Performance Tracking: Accuracy, precision, recall, F1 across all deployments
• Data Integrity: Missing values, type mismatches, range violations
• Traffic Monitoring: Volume patterns and anomaly detection
• Vector Monitoring: Specialized tools for embedding-based applications

Advanced Capabilities:

• Model segmentation and cohort analysis
• Class imbalance handling
• Statistical analysis (mean, std, distributions)
• Model version comparison
• Custom formula-based metrics
• platform

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Analytics & Root Cause Analysis

Deep-dive investigation tools for understanding performance issues and data quality problems. Four-Part Analysis Experience:

1. Events: Browse sample of 1,000 recent events for pattern recognition
2. Data Drift: Feature-by-feature drift breakdown with prediction impact
3. Data Integrity: Violation summaries (range, type, missing value issues)
4. Analyze: Interactive charts for performance and feature analytics

Chart Types:

• Performance Analytics (confusion matrices, prediction scatterplots)
• Feature Analytics (distributions, correlations, feature matrices)
• Metric Cards (single KPI visualization)
• analytics

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Dashboards & Visualization

Customizable dashboards for monitoring your entire AI portfolio. Features:

• Auto-Generated Insights: Every model gets an out-of-the-box dashboard
• Custom Dashboards: Build your own views with flexible layouts
• Model Comparison: Side-by-side performance tracking
• Multi-Column Plots: Drift and integrity across all features
• Interactive Controls: Date ranges, timezones, bin sizes, zoom
• Collaboration: Save and share dashboards across teams
• dashboards

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Alerting & Response

Proactive monitoring with intelligent alerting across all AI systems. Alert Types:

• Drift Alerts: Detect distribution shifts in production data
• Data Integrity Alerts: Flag missing values, type mismatches, range violations
• Performance Alerts: Monitor accuracy degradation over time
• Custom Metric Alerts: Formula-based alerts for business KPIs
• Traffic Alerts: Volume and pattern anomaly detection

Alert Features:

• Warning and critical threshold configuration
• Multiple notification channels (email, Slack, PagerDuty, webhooks)
• Triggered revisions with real-time updates
• Template-based alert creation
• Alert history and audit logs

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Getting Started

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Choose Your Path

For LLM Applications:

• LLM Monitoring Quick Start - Set up enrichments and quality tracking
• LLM-Based Metrics Guide - Configure trust and safety metrics

For Traditional ML Models:

• ML Observability Quick Start - Deploy drift detection and performance monitoring
• Monitoring Platform Guide - Configure alerts and data integrity checks

For Agentic Systems:

• Agentic Monitoring Quick Start - Set up hierarchical tracing with LangGraph
• Agentic Observability Concepts - Understand the agent lifecycle and monitoring approach

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Additional Resources

Platform Guides:

• Analytics Deep Dive - Root cause analysis and investigation
• Custom Dashboards - Build monitoring views for your team

Integration Documentation:

• Python Client SDK Reference - Programmatic access to all features