Getting Ahead of the Fragmentation in Agentic AI
Agentic AI represents a monumental leap forward, promising to automate complex processes, revolutionize customer support, and unlock unprecedented levels of productivity.
But for the enterprises building these systems, the reality has been a story of fragmentation. Development teams are forced to stitch together a patchwork of open-source libraries for pre-production testing and separate, often inadequate, tools for production monitoring.
This disconnected workflow is inefficient and, more importantly, risky. Insights from production rarely inform development, and the tests run before deployment often fail to capture the chaotic reality of real-world use. How can you be confident in your agent's performance when the tools you use to build it are blind to what happens after you ship?
Today, Fiddler is closing that gap.
Introducing End-to-End Agentic Observability
We are proud to introduce enterprise-grade, end-to-end observability for agentic AI. We are completing the story, providing a single, unified workflow that spans the entire AI lifecycle from the first prompt in development to managing millions of interactions in production. By integrating our best-in-class monitoring and analytics with powerful pre-production evaluation, Fiddler gives you the visibility, context, and control needed at every stage of your agent’s journey.
The Fiddler Trust Service Advantage
At the core of our platform is the Fiddler Trust Service, the foundation that powers evaluation, guardrails, and monitoring in your environment. Its purpose-built, fine-tuned Trust Models deliver low-latency, task-specific scoring of agent and LLM inputs and outputs within your environment.
By leveraging this high-speed, scalable, and secure engine, you establish a consistent and cost-effective foundation, giving you complete control over your development and deployment process.
Four Stages in the Complete Lifecycle for Reliable Agents
1. Build: Establish the Foundation with Rapid Iteration
The journey to effective agentic observability begins with the very first line of code, creating a blueprint that ensures your agents are reliable, cost-effective, and aligned with business goals.
- Instrument your agent for end-to-end tracing and cost telemetry from day one.
- Define your data schemas and establish the business KPIs you'll track throughout the lifecycle.
- Align technical performance with the business outcomes that matter most.
2. Test: Ensure Reliability with Pre-Production Evaluations
Once the agent is built, it must be rigorously evaluated to stress-test its logic against real-world chaos before it ever interacts with a customer.
- Experiment with different prompts and responses, evaluating them on both golden and challenger datasets.
- Investigate results with powerful sorting and filtering to pinpoint successes and failures.
Deploy with Confidence
With rigorous testing complete, deployment transforms from a moment of anxiety into a confident, controlled rollout. The "go/no-go" decision is no longer a guess but a data-driven conclusion based on comprehensive evaluation scores. You are deploying an agent that has been thoroughly vetted against a wide range of scenarios, ensuring it meets your organization's standards for performance and reliability. The system is primed for immediate and comprehensive monitoring the moment it goes live.
3. Monitor: Deliver Maximum Performance
Once deployed, Fiddler provides a centralized command center, giving you a single pane of glass for real-time performance at scale.
- Track key metrics like latency, token spend, task success, and response quality.
- Proactively monitor for any degradation in performance, safety, or quality.
- Understand when the patterns in your live data deviate from your training data or when the agent's behavior changes over time
4. Analyze & Improve: Close the Loop
Monitoring tells you what is happening; analysis tells you why. When an issue occurs, Fiddler allows you to go from a high-level indicator directly into deep analysis to understand the root cause.
- Drill down through the entire agentic hierarchy - from the app → sessions → agents → traces → spans - to pinpoint the exact point of failure in seconds.
- Conduct root cause analysis to understand the "why" behind any incident.
- Create a feedback loop by feeding production logs back into your evaluation workflow as new challenger datasets, turning every incident into a new test case.
Mastering the Agentic AI Lifecycle
To truly unlock the promise of this technology, enterprises need a single source of truth — a platform that provides complete visibility, deep context, and granular control across the entire AI lifecycle.
Fiddler provides that platform.
By bringing together pre-production evaluation and post-production monitoring, we’re empowering enterprises to build the agents they want, with the confidence they need. Request a demo today.
