Monitoring a web application is straightforward: track request latency, error rates, and throughput. Monitoring an agentic system is fundamentally different—because the system makes decisions that you didn’t program, follows paths you didn’t anticipate, and consumes resources in ways that vary with every execution.
Traditional Application Performance Monitoring wasn’t built for this. Here’s what observability looks like when your software has agency.
What makes agent observability different
Three properties distinguish agent observability from traditional monitoring:
Non-deterministic execution paths. The same input can produce different tool call sequences, different reasoning chains, and different outputs. You can’t pre-define the “happy path” because there isn’t one.
Compound error propagation. When an agent makes a mistake at step 3 of a 12-step task, that error doesn’t always surface immediately. It propagates through subsequent reasoning, potentially corrupting the final output in ways that are invisible to simple success/failure metrics.
Cost variability. A single agent invocation might consume 500 tokens or 50,000 tokens depending on the complexity of its reasoning and how many tool calls it makes. Without visibility into token consumption at each step, cost management is guesswork.
The four layers of agent observability
A complete observability strategy addresses four distinct layers:
Layer 1: Execution traces
The foundational layer. Every agent invocation should produce a structured trace that captures:
- The reasoning chain: What did the agent decide to do and why?
- Tool call sequence: Which tools were invoked, in what order, with what parameters?
- Intermediate results: What did each tool return, and how did the agent interpret it?
- Decision points: Where did the agent choose between alternatives?
This is where distributed tracing standards like OpenTelemetry become relevant—but you’ll need agent-specific span attributes beyond what standard HTTP tracing provides.
Layer 2: Token economics
Every LLM call has a cost. Agent observability must track:
- Input tokens per step: How much context is the agent sending?
- Output tokens per step: How verbose are the agent’s responses?
- Total tokens per task: What’s the full cost of completing a unit of work?
- Token efficiency: How does token consumption correlate with output quality?
Without this layer, you’ll discover cost problems only when the invoice arrives.
Layer 3: Behavioral telemetry
Beyond what the agent did, you need to understand how well it performed:
- Tool selection accuracy: Did the agent choose the right tool for each step?
- Reasoning quality: Did the agent’s chain of thought lead to correct conclusions?
- Escalation patterns: When did the agent ask for help, and was that appropriate?
- Error recovery: When something failed, did the agent recover gracefully?
This layer requires evaluators—often LLM-based themselves—that assess the quality of agent decisions against defined criteria.
Layer 4: Operational health
The traditional monitoring layer still matters:
- Latency distribution: How long do agent tasks take, and what’s the variance?
- Failure rates: What percentage of tasks fail, and at which step?
- Resource utilization: How are your MCP servers, A2A endpoints, and model APIs performing?
- Queue depth and backpressure: For asynchronous agents, how is work accumulating?
What existing tools don’t provide
Current APM platforms can handle Layer 4 and parts of Layer 1. But agent-specific observability requires:
Reasoning chain visualization. Standard trace viewers show HTTP spans. Agent traces need to show reasoning steps, branching decisions, and tool-call dependencies in a format that’s actually debuggable.
Cost attribution. There’s no standard way to attribute token costs across organizational boundaries—by team, by use case, by customer. You’ll need to build this.
Quality scoring. Automated quality assessment of agent outputs is an active research area. Current solutions are fragile and domain-specific.
Anomaly detection for non-deterministic systems. What counts as “anomalous” when every execution is different? Statistical baselines for agent behavior are harder to establish than for deterministic services.
Implementation considerations
Instrument at the agent framework level. Don’t try to add observability after the fact. If your agent framework doesn’t support structured tracing, that’s a problem you need to solve early.
Define your quality criteria before you build. What does “good” look like for each agent task? Without defined criteria, behavioral telemetry has nothing to measure against.
Budget for storage. Agent traces are verbose—reasoning chains, full tool call payloads, intermediate results. Plan for significantly more telemetry data than traditional applications produce.
Start with cost visibility. Of the four layers, token economics has the clearest ROI. Most organizations discover they’re spending 3-5x more than expected once they have per-step cost visibility.
The bigger picture
Observability for agentic systems isn’t an extension of traditional monitoring—it’s a new discipline. The non-deterministic, multi-step, reasoning-intensive nature of agent execution demands purpose-built tooling and new mental models for what “healthy” looks like.
The organizations that invest in agent observability early will have a decisive advantage: they’ll be able to debug, optimize, and trust their agents in ways that organizations without visibility simply cannot.