Fiddler Enterprise Agentic Observability
Agentic Observability for Autonomous and Reflective Multi-Agent Systems
When one request triggers five agents and a dozen tool calls, your team spends hours digging through traces to find which decision broke it, and it’s hard to tell which agents are delivering value and which are just burning budget.
Fiddler shows you why agents make decisions and how they depend on each other, so you can pinpoint the failing step fast and know which agents are worth the investment.
That visibility is what lets you give agents more autonomy and scale them.
Questions you can finally answer:
Build. Test. Monitor. Improve.
Fiddler delivers end-to-end Agentic Observability, giving enterprises complete visibility from the full application down to every tool call. It observes agents holistically and makes them interpretable, so that you can understand tool-use chains, agent handoffs, and outcomes.
By combining evaluations in development with monitoring in production, Fiddler provides a continuous feedback loop, making agentic systems more reliable, cost-effective, and scalable.
Powered By Fiddler Centor Models
Fiddler Agentic Observability is powered by Centor, Fiddler’s proprietary evaluation engine. At the core of Centor, purpose-built Fiddler Centor Models score agent and LLM inputs and outputs in under 80ms, inside your environment, at up to 98% lower cost than using external LLMs as a judge. Because Centor Models run on infrastructure you provision, cost scales with GPU capacity rather than trace volume. You get full trace coverage with a predictable cost.
End-to-End Observability for the Agentic Lifecycle
Launch Reliable Agents
- Evaluate agents with curated golden and challenger datasets to improve performance and behavior.
- Run low-cost experiments with prompts, models, and configurations to reach better outcomes.
- Stress-test edge cases to find weaknesses early, reducing risks and harmful incidents after launch.
See Every Agent Action in Production
- Capture every agent run, tool call, and handoff with OpenTelemetry GenAI semantic conventions.
- Get fleet-wide visibility into coding agents, including cost by model, cost per PR and commit, model usage, and active developers against seats.
- Track planning steps, tool calls, MCP server usage, and multi-agent handoffs across sessions.
- Monitor and get alerts on hallucination, toxicity, PII/PHI, jailbreaks, and custom KPIs, with full trace coverage.
- View aggregate and granular insights in customizable dashboards and reports.
Understand the ‘Why’ to Optimize Agent Performance
- Trace issues from the application down to the failing span with full context, reducing MTTI and MTTR.
- Catch tool-loop errors, wrong tool selection, and bottlenecks across your agent fleet.
- Connect coding agent sessions, plans, and files touched to the requests, tokens, and spend behind them, so you can attribute cost to a team, repo, and model.
- Surface critical issues with targeted filtering, sorting, and span attributes to drive improvements.
- Create a feedback loop to improve agent performance, accuracy and safety.
Support for Any Agentic Framework
- OpenTelemetry (OTEL) integration fits into your existing AI pipeline and work across systems
- Coding agent telemetry from Claude Code, Gemini CLI, and other OpenTelemetry-compatible agentsNative support for LangGraph, Amazon Bedrock, AWS Strands Agents, Google ADK, CrewAI, and other frameworks and tools in your agentic tech stack
Featured Resources
Frequently Asked Questions About Agentic Observability
What is agentic observability?
Agentic observability is the ability to evaluate, monitor, analyze, and improve the behavior of autonomous agents across their lifecycle. It gives visibility from the full application down to individual sessions, agents, traces, and spans. This enables enterprises to understand decision-making, coordination, and performance within AI agentic workflows from pre-production to production. It covers first-party agents, third-party agents, and coding agents such as Claude Code and Gemini CLI.
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 write and review code, like coding agents in the IDE and CLI.
How is agentic observability different from LLM observability?
LLM observability focuses on individual prompts and responses. Agentic observability covers multi-step workflows: reasoning chains, tool calls, handoffs between agents, and code changes made by coding agents, with span-level tracing across sessions. It shows why an agentic system produced an outcome, not just what a single model returned.