As AI evolves from deterministic prediction to probabilistic decision-making, the focus is shifting from outputs to behavior. Traditional APM tools were built to track metrics like latency and errors, but they fall short in the world of autonomous, reasoning agents. Today’s AI agents think, act, execute, reflect, and align, all within a single loop. To truly understand and improve agentic systems, developers need visibility not just into what happened, but why. That is where Agentic Observability comes in.
In our previous blog post, we explored why Agentic Observability is essential for debugging and monitoring multi-agent systems in the enterprise setting. In this blog, we go deeper into the anatomy of observed agents. We unpack the five critical stages that must be made visible to build reliable agentic AI.
Anatomy of an Observed Agent
Agentic Observability is not just infrastructure or model telemetry. It is about understanding the full cognitive and operational loop of AI agents in action so that teams can monitor, control, and protect agent performance and behavior. To truly observe an agent, we must capture each phase of its lifecycle, from thought to alignment. At Fiddler, we break the anatomy of the observed agent into five stages:
- Thought: Ingest, Retrieve, Interpret: The agent begins by ingesting prompts, retrieving memory, and forming an internal belief state. From this context, they interpret goals and formulate an execution process to decide what to do and how. Observability at this stage captures prompt inputs, memory quality, goal interpretation, and plan generation, offering insight into agent intent before any action is taken.
- Action: Plan and Select Tools: The agent selects tools or APIs to invoke based on its plan. Observing this stage reveals how decisions are operationalized, including tool choices, reasoning paths, and sequencing of steps.
- Execution: Perform Tasks and Capture Outputs: The agent acts by invoking tools, calling APIs, or communicating with external systems. Observability at this stage captures input/output traces, errors, latency, tool effectiveness, and success or failure signals. These are critical data points for diagnosing runtime issues.
- Reflection: Evaluate Success/Failure and Adapt: After execution, the agent reflects on what happened. Did it meet the goal? Was the plan effective? This self-critique step can include trajectory scoring, error analysis, and adaptive learning. Reflection could also happen due to human escalations or leveraging Trust model evaluations.
- Alignment: Enforce Trust, Compliance, and Safety: Finally, guardrails come into play. This phase enforces safety, compliance, and fallback logic. It’s where trust models or human-in-the-loop mechanisms can intervene.
Together, these stages form a closed feedback loop that’s essential for building intelligent, reliable systems. By observing each stage of the agent lifecycle, teams gain actionable insights, not just into failures, but into why decisions were made, where coordination broke down, or how to improve performance over time.
How Fiddler Delivers Enterprise Agentic Observability
We’ve helped enterprises monitor and govern ML and LLM applications for years. With the rise of agentic AI, we’re extending that foundation to support multi-agent systems. We support integrations with leading agentic frameworks such as LangGraph, Amazon Bedrock, and custom-built agents, making it easy for engineering teams to weave Fiddler Agentic Observability directly into their existing pipelines without rearchitecting workflows.
We focus on three core areas to help enterprises monitor, control and protect multi-agent systems:
1. Complete Visibility Across the Agentic Hierarchy
Fiddler provides end-to-end visibility, from high-level application health down to individual agent actions and tool calls. Teams can trace interactions and decisions across sessions, spot coordination breakdowns, and surface dependencies that could lead to cascading failures.
2. Faster Diagnosis with Hierarchical Root Cause Analysis
Our interactive hierarchical root cause analysis enables teams to isolate failures quickly without sifting through logs. You can drill down from application metrics to the exact span or tool call where things went wrong.
3. Unified, Actionable System Metrics
Fiddler rolls up metrics from every layer of the system into a single, unified view. This makes it easier to monitor overall performance, track trends, and prioritize actions based on agent transparency, quality, and reliability.
With this depth of visibility, teams no longer have to sift through overwhelming data exhaust generated by many agents. Instead, they gain intelligent oversight, faster resolution, and the confidence to scale Agentic AI with transparency and control.
The Future of Enterprise Agentic Observability
As AI evolves beyond static inference into dynamic, goal-driven agents, the next frontier is clear: observability must shift from reactive logging to real-time understanding of agent behavior. Multi-agent systems demand visibility not just into outputs, but into the internal reasoning, coordination, and adaptations that drive those outputs.
At Fiddler, we’re pioneering this shift with Agentic Observability that blends traditional infrastructure monitoring, LLM introspection, and semantic traceability across distributed agents.
We believe in the following principles:
- Reflection as a First-Class Signal: Capture agents' self-critiques and internal scoring to surface the “why” behind actions, not just the “what.”
- Runtime Semantic Tracing: Go beyond surface telemetry, trace agent plans, belief states, and tool chains as they evolve in real time.
- Behavior-Centric Debugging: Focus on detecting off-policy behavior, failed coordination, and missed goals as most agentic failures are misalignments and not necessarily bugs.
- Integrated Guardrails and Trust Models: Escalate, reroute, or recover tasks live when agents drift from acceptable behavior.
Missed our first post? Read it here to understand why Agentic Observability is the missing piece in building transparent and reliable AI agents.
Agentic Observability is emerging but moving at a blazing speed. We are excited about what enterprises are building and how we can help. Each use case is unique. We welcome your thoughts on how you are building your agentic system, which priorities matter, and additional functionality you’d like to see from Fiddler. Get in touch — we would love to hear from you!
