Build High Performing AI Agents with Fiddler Agentic Observability
Fiddler provides comprehensive visibility across the entire agentic hierarchy, from applications and sessions down to individual spans and tool calls, so you can see what happens in any interaction.
Fiddler Agentic Observability helps enterprises:
- Run tests and experiments with different prompts and responses to develop better agents
- Obtain aggregate and granular insights to understand agent performance and behavior
- Perform root cause analysis and diagnostics to pinpoint failure points
- Improve agents with a feedback loop from production back to development
[00:00:01] Fiddler delivers end-to-end agentic observability, giving you visibility across the agentic hierarchy and through every stage of the agent lifecycle — from evaluation in development through monitoring in production.
[00:00:13] Under the hood, the Fiddler Trust Service runs purpose-built Trust Models to score agent and LLM inputs and outputs in your environment. Because out-of-the-box and custom evaluators stay within your environment, there’s no risk of data exposure and no hidden costs from external API calls. Fiddler is native, not additive. Think batteries included.
[00:00:36] Agentic systems comprise several layers: applications contain sessions; sessions involve multiple agents; agents produce traces; traces break into spans. Along these layers are decision paths, tool calls, and feedback loops.
[00:00:51] Now let's see Fiddler Agentic Observability in practice with a travel app powered by a supervisor agent, a travel agent, and a hotel agent.
[00:00:59] Before going live, you can run evaluations to understand how the agent performs.
[00:01:04] Start with a golden dataset to validate correct behavior, then use a challenger dataset to stress-test unpredictable scenarios, and compare side-by-side to reach better outcomes.
[00:01:14] Run experiments with different prompts and responses, highlighting weaknesses to bolster before launch.
[00:01:19] Once in production, Fiddler gives you a single pane of glass to track health, traffic, cost, latency, and safety trends of your travel application.
[00:01:28] Analyze spikes and dips that signal coordination issues between the supervisor and agents that you need to drill-down on.
[00:01:35] Perform root cause analysis to understand each agent’s decision path.
[00:01:39] You can see sessions, agents, and spans in one place. Use filters, sorting, and attributes to inspect spans and surface anomalies.
[00:01:47] Then trace decision paths and reasoning across agents and sessions.
[00:01:51] You can diagnose the agentic hierarchy in two views.
[00:01:54] The hierarchy view traces dependencies and reasoning chains across application, session, agent, trace, span.
[00:02:01] The timeline view shows the sequence of actions, so you can pinpoint what happened, when, and why.
[00:02:07] This is forensic-level root cause analysis — tracing the exact execution path from user query to supervisor decisions to agent actions to tool interactions. Unlike sifting endlessly through raw logs, Fiddler gives you both aggregate metrics and granular details. You get system-wide insights to span-level metrics.
[00:02:25] What makes this even more powerful is the feedback loop between evaluation and monitoring. Fiddler delivers visibility, context, and control along your agent’s journey.
[00:02:36] Whether in evaluation or in production, every agent needs a high-performing end-to-end observability.
[00:02:42] Fiddler enables enterprises to deliver high performance AI, protect against AI risks, and maximize ROI.
[00:02:49] Visit fiddler.ai to start your agentic journey.
