Fiddler Agentic Observability in the Enterprise
Agentic Observability for Autonomous and Reflective Multi-Agent Systems
Enterprises are rapidly moving toward sophisticated multi-agent systems for advanced reasoning, which can introduce exponential complexity. Agentic applications bring autonomy, reasoning, and coordination that break the traditional APM (Application Performance Management) framework apart, leaving enterprises without the traceability and interpretability needed to understand why agents make specific decisions and their dependencies.
Enterprise Visibility, Context, and Control with Fiddler Agentic Observability
Fiddler observes agents at a holistic level and makes them interpretable so that you can understand the behavior of the agentic system. It is a rich, contextually-aware performance experience that reveals system-wide insights. It is not a one-trace-at-a-time debuggability experience.
Build, Monitor, and Improve Agentic AI Deployments
Cross-Agent Ecosystem Visibility
- Track complex multi-agent interactions, reasoning, and decision-paths across sessions down to spans, enabling full visibility into your AI agentic workflows.
- Uncover cross-agent dependencies and coordination bottlenecks that impact system performance.
- Identify cascading failures across the entire agent ecosystem.
Hierarchical Root Cause Analysis
- Navigate through the agentic hierarchy from system health down to spans and back up without sifting through logs.
- Pinpoint exact failure points with intelligent filtering and context-aware diagnostics.
Unified Metrics Aggregation
- Consolidate system and agent-level metrics from multiple data sources into single-pane visibility.
- Prioritize intelligently with actionable insights for agent quality, transparency, and optimization.
Support For Any Agentic Framework
- OpenTelemetry (OTEL) integration for comprehensive agentic data collection for aggregate and granular visibility.
- Native support for LangGraph, Amazon Bedrock, and other frameworks of choice, along with the tools in your agentic tech stack.
Featured Resources
Frequently Asked Questions
What is agentic observability?
Agentic observability is the ability to monitor, interpret, and analyze the behavior of autonomous agents within complex multi-agent systems. It provides hierarchical visibility across applications, sessions, agents, traces, and spans. This enables enterprises to understand decision-making, coordination, and performance within AI agentic workflows.
What is the difference between deterministic and agentic?
Deterministic systems follow fixed rules and produce predictable outcomes. In contrast, agentic systems are autonomous and capable of making decisions based on reasoning, context, or learned behavior. As a result, their outputs are dynamic and less predictable.
What is the difference between autonomous and agentic?
Autonomous systems can operate independently without human input. Agentic systems build on this capability by reasoning, planning, adapting to new situations, and collaborating with other agents. While all agentic systems are autonomous, not all autonomous systems exhibit agentic behavior.
What are examples of agentic behavior?
Examples of agentic behavior include reasoning through complex problems, adjusting plans based on context, delegating tasks to other agents, and reflecting on previous actions to improve future decisions. These behaviors are often found in large language model agents that manage workflows, book travel, or solve software issues.
What is the meaning of agentic retrieval?
Agentic retrieval refers to an AI agent’s ability to independently find and use relevant information from external tools, documents, or databases. This capability supports the agent’s goals by allowing it to reason more effectively within its current context. It is essential in systems using retrieval-augmented generation.
