Agentic Monitoring
Modern GenAI applications built with agentic frameworks create complex, multi-step workflows that can be difficult to understand and debug. Fiddler, the pioneer in AI Observability and Security, provides comprehensive monitoring solutions for AI agents regardless of your framework. Whether you're using LangGraph, Strands, or custom implementations, you get complete visibility into agent behaviors with enterprise-grade safeguards and real-time monitoring—the insights you need to confidently deploy reliable, high-performance GenAI applications in production.
What Is Agentic Monitoring?
Agentic monitoring observes and analyzes AI agent behavior in real-time. Unlike traditional application monitoring that focuses on system metrics, agentic monitoring captures the unique characteristics of AI workflows:
Agent decision-making processes: How agents choose between different tools and actions
Multi-step reasoning chains: Complex workflows from initial prompt to final response
LLM interactions: Model inputs, outputs, and performance across different calls
Tool usage patterns: How agents utilize external functions and APIs
Error propagation: How failures cascade through agent workflows
Why Agentic Monitoring Matters
GenAI applications present unique observability challenges that traditional monitoring approaches can't address:
Complexity and Opacity
AI agents make autonomous decisions that are difficult to predict or understand. Without proper monitoring, you can't debug agent behavior in production or understand why an agent made specific choices.
Dynamic Workflows
Unlike traditional applications with fixed execution paths, AI agents create dynamic workflows based on context and available tools. You need to trace the actual execution path for each interaction.
Performance Variability
LLM response times and quality vary significantly based on model load, prompt complexity, and external factors. Monitoring helps you identify performance patterns and optimize accordingly.
Cost Management
GenAI applications consume tokens and compute resources with each LLM call. Understanding usage patterns helps you optimize costs and prevent unexpected billing spikes.
Quality Assurance
AI outputs vary in quality and accuracy. Monitoring helps you identify when agents produce suboptimal results and understand the conditions that lead to better performance.
Integration Options
Fiddler provides flexible integration options to instrument your agentic applications, regardless of the framework you're using. Choose the approach that best fits your technology stack:
LangGraph & LangChain Applications
Best for: Applications built with LangGraph or LangChain
The Fiddler LangGraph SDK provides automatic instrumentation with minimal code changes. Simply initialize the client and your agent workflows are automatically monitored.
from fiddler_langgraph import FiddlerClient
from fiddler_langgraph.tracing.instrumentation import LangGraphInstrumentor
fdl_client = FiddlerClient(
api_key=os.getenv("FIDDLER_API_KEY"),
application_id=os.getenv("FIDDLER_APPLICATION_ID"),
url=os.getenv("FIDDLER_URL")
)Setup time: ~15 minutes | Quick Start Guide →
Strands Agent Framework
Best for: Applications built with Strands Agents
The Fiddler Strands SDK integrates natively with the Strands framework for seamless monitoring without additional instrumentation code.
from fiddler_strands import FiddlerClient
fdl_client = FiddlerClient(
api_key=os.getenv("FIDDLER_API_KEY"),
application_id=os.getenv("FIDDLER_APPLICATION_ID"),
url=os.getenv("FIDDLER_URL")
)Setup time: ~15 minutes | Quick Start Guide →
Custom Instrumentation (OpenTelemetry)
Best for: Custom agent frameworks, non-Python applications, or when you need maximum control
Use OpenTelemetry directly to instrument any agentic application. This approach provides maximum flexibility and works with any programming language.
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
# Configure OpenTelemetry to send spans to Fiddler
provider = TracerProvider()
processor = BatchSpanProcessor(OTLPSpanExporter(
endpoint="https://your-instance.fiddler.ai/v1/traces"
))
provider.add_span_processor(processor)Setup time: ~20 minutes | Quick Start Guide →
Integration Comparison
Automatic Instrumentation
✅ Zero code changes
✅ Native integration
❌ Manual setup
Framework Support
LangGraph, LangChain
Strands Agents
Any framework
Language Support
Python
Python
Python, Java, Go, JS, etc.
Setup Time
~15 minutes
~15 minutes
~20 minutes
Customization
Medium
Medium
High
How Agentic Monitoring Works
Regardless of which integration method you choose, Fiddler's agentic monitoring provides the same comprehensive observability capabilities:
Built on OpenTelemetry Standards
All Fiddler agentic monitoring solutions leverage OpenTelemetry (OTel), the industry standard for observability. This ensures compatibility with existing monitoring infrastructure, vendor neutrality, and future-proofing of your investment.
Automatic or Manual Instrumentation
Depending on your integration choice, you can collect telemetry data automatically or with fine-grained control:
Automatic Instrumentation (LangGraph & Strands SDKs):
Zero-code or minimal-code setup
Automatically captures all agent interactions
Best for rapid deployment and standard use cases
Manual Instrumentation (OpenTelemetry):
Full control over what data is captured
Custom span attributes and metadata
Best for specialized requirements or custom frameworks
All approaches collect the same essential telemetry:
Distributed traces: Complete execution flow
Span attributes: Inputs, outputs, and metadata
Performance metrics: Timing and resource usage
Error tracking: Detailed context and stack traces
Near Real-Time Streaming
Telemetry data streams in near real-time to your Fiddler instance, enabling immediate visibility into agent behavior and rapid response to issues.
Architecture Overview
Fiddler's agentic monitoring integrates seamlessly into your application architecture, regardless of which integration method you choose:
Key Components
Instrumentation Layer: Captures agent execution events (automatic with SDKs, manual with OpenTelemetry)
Trace Exporter: Sends telemetry data to Fiddler using OTLP protocol
Fiddler Client: Manages configuration, authentication, and connection to your Fiddler instance
OpenTelemetry Integration: Provides industry-standard distributed tracing across all frameworks
What You Can Monitor
Once your agent application is instrumented, Fiddler provides comprehensive observability through dashboards, metrics, and automated evaluations:
Dashboards & Visualization
Access specialized dashboards designed for agentic workflows:
Pre-Built Agentic Dashboards:
Agent Performance Overview - Monitor success rates, latency, and throughput across all agents
Workflow Execution Traces - Visualize complete multi-step reasoning chains hierarchically
Tool Usage Analytics - Track which external tools and APIs your agents are calling
Error & Exception Tracking - Identify where agent workflows fail and diagnose the root cause
Trace Visualization: Every agent interaction is captured as a hierarchical trace showing:
Agent decision points and reasoning steps
LLM calls with full inputs, outputs, and metadata
Tool invocations and API requests
Timing information for each step
Parent-child relationships in multi-agent systems
Custom Dashboards:
Build custom dashboards combining multiple charts to track KPIs specific to your use case
Filter by agent type, user segments, time periods, or custom attributes
Share dashboards with team members and stakeholders
Configure alerts based on dashboard metrics
Metrics & Analytics
Fiddler automatically generates specialized metrics for agent monitoring:
Agent-Specific Metrics:
Agent Success Rate - Percentage of workflows completing successfully
Tool Call Distribution - Frequency analysis of tool usage
Reasoning Chain Length - Average number of steps per workflow
Agent Handoffs - How often agents delegate to other agents
Retry & Recovery Rate - Agent resilience and error recovery patterns
Performance Metrics:
End-to-End Latency - Total time from user request to final response
Per-Step Latency - Duration of individual reasoning steps, LLM calls, and tool invocations
Token Usage - LLM consumption tracking across all agent interactions
API Call Volume - Monitor external tool and API usage patterns
Quality Metrics:
Response Accuracy - Validate agent outputs (requires ground truth labels)
Hallucination Detection - Identify when agents generate unsupported claims
Safety & Guardrails - Track safety violations and guardrail activations
User Satisfaction - Capture feedback signals from end users
Evaluator Rules
Automated Quality Assessment
Evaluator Rules enable automated, continuous evaluation of your agent's performance directly from production spans and traces. Define once, and Fiddler automatically assesses quality, safety, and performance in real-time.
How Evaluator Rules Work:
Configure an Evaluator - Choose from Fiddler-provided evaluators or create custom LLM-as-a-judge evaluators
Map Inputs - Connect evaluator variables to span attributes from your agent telemetry
Set Application Rules - Define which spans qualify for evaluation (e.g., only "llm" span types)
Activate - Evaluations run automatically on all matching spans
Available Evaluators:
Fiddler-Provided: Topic Classification, Answer Relevance, Coherence, Conciseness, RAG Faithfulness, PII Detection, Sentiment Analysis, Prompt Safety, Response Faithfulness
Custom LLM-as-a-Judge: Create domain-specific evaluators with custom prompts and scoring logic
Key Benefits:
Automated Quality Monitoring - Continuous evaluation without manual labeling
Real-Time Insights - Identify quality issues as they happen in production
Backfill Capability - Apply evaluations retroactively to historical data
Flexible Filtering - Evaluate specific span types, regions, or custom conditions
→ Learn more about Evaluator Rules
What Data Is Captured
All instrumentation methods capture comprehensive data about your agent workflows:
Agent Execution Traces
Workflow structure: Complete hierarchy of agent steps and decisions
Timing information: Duration of each step and overall execution time
Agent identification: Unique identifiers for different agents in your system
LLM Interactions
Model configuration: Model name, temperature, and other parameters
Input prompts: System messages, user input, and conversation history
Model outputs: Generated responses, token usage, and completion metadata
Performance metrics: Response time, tokens consumed, and success rates
Tool and Function Calls
Tool identification: Names and types of tools used by agents
Input parameters: Arguments passed to functions and tools
Output results: Return values and success/failure status
Execution context: When and why tools were invoked
Error and Exception Handling
Exception details: Full error messages and stack traces
Context information: State of the agent when errors occurred
Recovery attempts: How agents handled and recovered from failures
Key Benefits
Agentic monitoring with Fiddler provides immediate value across your development lifecycle, helping you move beyond experimentation to deploy production AI confidently:
Development and Learning
Quick setup: Start monitoring with just a few lines of code
Immediate insights: See agent behavior without complex configuration
Deep visibility: Understand decision-making processes beyond just inputs and outputs
Performance and Optimization
Hierarchical root cause analysis: Drill down from application-level issues to specific agent spans to reduce MTTI (Mean Time to Identify) and MTTR (Mean Time to Resolve)
Application-critical metrics: Monitor performance, costs, and safety through a unified dashboard
Quality improvement: Understand which patterns lead to better results
Production and Troubleshooting
Enterprise-grade monitoring: Track agent performance and success rates at Fortune 500 scale
End-to-end visibility: Complete observability into multi-agent interactions and coordination patterns
Actionable alerts: Get early warnings on performance issues and cross-agent problems
Data-driven decisions: Make informed optimizations based on comprehensive telemetry data
Security and Privacy Considerations
Fiddler's agentic monitoring is designed with enterprise-grade security and privacy:
Enterprise compliance: SOC 2 Type 2 security and HIPAA compliance standards
Data encryption: All telemetry data is encrypted in transit using HTTPS/TLS
Access control: Role-based access control (RBAC) and SSO for enterprise user management
Personal access token: Access token-based authentication ensures only authorized access
Data control: You control what data is captured and sent to Fiddler
Deployment flexibility: Deploy in Fiddler cloud or your own cloud
Compliance: Built on industry-standard OpenTelemetry for compliance requirements
Ready to Get Started?
You're now ready to add enterprise-grade observability to your agentic and LLM applications. Simply choose your integration library to get started in under 10 minutes:
Strands Agent Quick Start Guide ⏱️ 15 min
OpenTelemetry Quick Start Guide ⏱️ 20 min
Learn More:
Agentic Monitoring in Observability - Dashboards, metrics, and analytics
Frequently Asked Questions
Q: How is this different from traditional APM tools?
A: Traditional APM focuses on system metrics. Agentic monitoring captures AI-specific behaviors, such as reasoning chains, tool selection, and LLM interactions.
Q: Which integration method should I choose?
A: Choose based on your framework: LangGraph SDK for LangGraph/LangChain apps, Strands SDK for Strands agents, or OpenTelemetry for custom frameworks or non-Python applications. All provide the same monitoring capabilities.
Q: What's the performance overhead?
A: With default settings, expect less than 5% overhead. This can be reduced further with sampling.
Q: Is my data secure when using Fiddler's monitoring?
A: Yes, all data is encrypted in transit using HTTPS/TLS, and you retain full control over what data is captured and sent to Fiddler. Fiddler supports deployment in your own cloud environment for maximum security.
Q: How quickly will I see data in my Fiddler dashboard?
A: Telemetry data streams in near real-time, typically appearing in your dashboard within 1-2 minutes of agent execution.
Q: What happens if my agents fail? Will I still get monitoring data?
A: Yes, all instrumentation methods capture comprehensive error information, including exception details, agent state at failure, and recovery attempts, helping you debug and improve agent reliability.
Q: Can I use Evaluator Rules with any integration method?
A: Yes, Evaluator Rules work with spans and traces from all integration methods—LangGraph SDK, Strands SDK, and OpenTelemetry.
Limitations and Considerations
Fiddler Agentic Observability has some current limitations:
Protocol support: Currently uses HTTP-based OTLP; gRPC support planned for future releases
Attribute limits: Default limits prevent oversized spans; configurable for high-volume use cases
Language support: LangGraph and Strands SDKs are Python-only; use OpenTelemetry for other languages
These limitations don't affect the core monitoring capabilities but are essential to consider for production planning.
Next Steps
Quick Start Guides: Choose the quick start applicable to your use case
SDK References: Complete technical documentation for all SDK components