Your agents are running. But can you actually see what they're doing?
Traditional observability — logs, metrics, traces — was built for deterministic software. A request comes in, hits the same code path, and returns a predictable response. AI agents break every one of those assumptions.
Agents make decisions. They call tools in unpredictable sequences. They generate different outputs from identical inputs. And when they fail, they don't throw clean stack traces — they quietly produce garbage that looks plausible.
This is why ai agent observability requires a fundamentally different approach. Not just more logging, but a new observability stack designed for non-deterministic, multi-step, tool-using systems.
Why Traditional Observability Falls Short for AI Agents
APM tools like Datadog, New Relic, and Grafana are excellent at what they do: tracking request latency, error rates, throughput, and infrastructure health. But they were designed for a world where code paths are predictable.
AI agents introduce three problems that break traditional observability:
1. Non-Deterministic Execution Paths
The same input can produce different tool calls, different reasoning chains, and different outputs. You can't write alerts for "expected behavior" when the behavior changes every time.
2. Quality Is Subjective and Contextual
A 200 OK response means nothing for an agent. The output could be perfectly formatted but factually wrong, subtly hallucinated, or answering the wrong question entirely. Traditional health checks can't catch this.
3. Multi-Step Compound Errors
Agents chain actions together. A small error in step 2 — an incorrect API parameter, a misinterpreted user intent — compounds through steps 3, 4, and 5 until the final output is completely wrong. By the time you see the failure, the root cause is buried several decisions back.
This doesn't mean you should abandon traditional observability. You still need infrastructure metrics, uptime monitoring, and error tracking. But you need to layer agent-specific observability on top.
The Agent Observability Stack
Effective ai agent observability requires four layers, each capturing a different dimension of agent behavior:
Layer 1: Traces — Following the Decision Chain
Agent traces are fundamentally different from distributed system traces. Instead of tracking a request across microservices, you're tracking a reasoning process across decisions.
An agent trace should capture:
- The trigger: What initiated this agent run? A user message, a scheduled task, an event?
- Each LLM call: The prompt sent, the completion received, the model used, tokens consumed, and latency
- Tool invocations: Which tools were called, with what parameters, what they returned, and how long they took
- Decision points: Where did the agent choose between options? What alternatives existed?
- The outcome: What was the final result? Did the agent complete its goal?
Nested spans work well here: a parent span for the full agent run, child spans for each LLM call, and grandchild spans for tool executions. This gives you both the big picture and drill-down capability.
Key metric: Trace depth — how many steps does the agent take to complete a task? Increasing depth over time often signals prompt degradation or context pollution.
Layer 2: Evals — Measuring Output Quality
Traces tell you what the agent did. Evals tell you how well it did it.
There are three eval approaches for production agents:
Automated rubric scoring: Define criteria (accuracy, completeness, formatting, tone) and use a judge LLM to score outputs. Fast, scalable, but only as good as your rubric.
Human-in-the-loop sampling: Route a percentage of outputs to human reviewers. Slower but catches things automated evals miss — subtle hallucinations, off-brand tone, technically correct but unhelpful answers.
Regression testing: Maintain a golden dataset of input-output pairs. Run new agent versions against it and compare. Essential before deploying prompt changes or model upgrades.
The key insight: evals should run continuously in production, not just during development. Agent quality drifts over time as models update, data changes, and edge cases accumulate.
Layer 3: Replays — Reproducing Agent Sessions
When something goes wrong, you need to understand exactly what happened. Replays let you step through an agent's session from the beginning.
A good replay system captures:
- The complete input context (user message, conversation history, system prompt)
- Every LLM request and response (including the full prompt, not just the completion)
- All tool calls with parameters and return values
- Timing data for each step
- The agent's internal state at each decision point
This is the ai agent debugging equivalent of a flight recorder. When a customer reports a bad output, you pull up the replay, step through the decisions, and identify exactly where things went wrong.
Pro tip: Store replay data in append-only logs with structured metadata. You'll want to search by user, time range, outcome quality, error type, and cost — often simultaneously.
Layer 4: Cost Tracking — The Forgotten Dimension
Observability without cost tracking is like monitoring server health without checking the bill. AI agents consume tokens, and tokens cost money.
Track cost at three levels:
- Per-call: How many tokens did this specific LLM invocation consume? What did it cost?
- Per-task: What was the total cost to complete this agent task? Include all LLM calls, tool invocations, and retries.
- Per-agent: What's the aggregate cost of this agent over time? Is it trending up or down?
Cost anomalies are often the first signal that something is wrong. An agent stuck in a retry loop, an overly verbose prompt, a hallucinated tool call that triggers expensive downstream operations — all of these show up in cost data before they show up in quality metrics.
Debugging Non-Deterministic Agent Behavior
The hardest part of ai agent debugging isn't finding errors — it's finding the cause of errors in a system where behavior changes between runs.
Here's a systematic approach:
Step 1: Reproduce the Context, Not the Bug
You can't reliably reproduce a non-deterministic bug by replaying the same input. Instead, reproduce the context: the conversation history, the system state, the tool availability, and the model version. Often, the bug is in the context, not the input.
Step 2: Diff Against Successful Runs
Find a similar task that succeeded. Compare the traces side by side. Where did the paths diverge? This narrows the search space from "everything" to "this specific decision."
Step 3: Trace Backward from the Failure
Don't start at the beginning. Start at the failure point and walk backward through the trace. At each step, ask: was this step's input correct? If yes, the error originated here. If no, keep walking backward.
Step 4: Check the Usual Suspects
- Context window overflow: Was the prompt truncated? Did the agent lose important context?
- Tool response changes: Did an external API change its response format?
- Prompt injection: Did user input manipulate the agent's instructions?
- Model degradation: Is this correlated with a model version update?
Setting Up End-to-End Agent Observability
Here's a practical setup guide for teams deploying agents in production:
Instrument Your Agent Framework
Most agent frameworks (LangChain, CrewAI, AutoGen) support callbacks or middleware. Hook into these to capture traces automatically:
- On LLM call: Log prompt, completion, model, tokens, latency
- On tool call: Log tool name, parameters, response, latency
- On agent start/end: Log the full session boundary with metadata
Centralize Your Telemetry
Don't scatter observability across multiple systems. Route all agent telemetry to a central store that supports:
- Structured search across traces, evals, and costs
- Time-range queries ("show me all failures in the last 24 hours")
- Filtering by agent, task type, user, and quality score
- Aggregation for trend analysis
Build Alert Rules for Agents
Agent alerts look different from traditional alerts:
| Alert | Trigger | Why It Matters |
|---|---|---|
| Quality drop | Eval score below threshold for 1 hour | Agent output is degrading |
| Cost spike | Per-task cost exceeds 2× rolling average | Retry loop or prompt bloat |
| Completion rate drop | Task success rate below 90% for 30 min | Agent is failing silently |
| Latency increase | P95 task duration exceeds 3× baseline | Context overflow or API slowdowns |
| Tool failure rate | Any tool above 10% error rate | External dependency issue |
Don't Alert on Everything
Agent behavior is inherently variable. Alert on sustained trends, not individual outliers. A single expensive run isn't an incident. An hour of increasingly expensive runs is.
Dashboard Design for Agent Ops Teams
The best agent observability dashboards answer three questions at a glance:
1. Are My Agents Healthy Right Now?
- Agent status indicators (active, idle, errored, sleeping)
- Current task assignments and progress
- Real-time heartbeat signals showing agents are alive and working
Platforms like AgentCenter provide this out of the box — real-time status monitoring with heartbeat tracking, task lifecycle visibility, and activity feeds that show exactly what each agent is doing at any moment.
2. How Did My Agents Perform Today?
- Tasks completed vs. failed
- Average quality scores from evals
- Total cost by agent and task type
- Deliverable approval rates
3. What Needs My Attention?
- Failed tasks with replay links
- Quality score outliers (both low and surprisingly high — the latter might indicate gaming)
- Cost anomalies
- Agents that have been idle or stuck for too long
The key design principle: surface anomalies, not averages. An average quality score of 8.5 is meaningless if it's hiding a bimodal distribution where half the outputs score 10 and half score 7.
The Observability Maturity Model
Teams typically progress through four stages:
Stage 1 — Logging: Basic structured logs of agent actions. You can grep for errors but can't see the full picture.
Stage 2 — Tracing: Full execution traces with nested spans. You can replay any agent session and follow the decision chain.
Stage 3 — Evaluation: Continuous quality scoring in production. You catch degradation before users report it.
Stage 4 — Predictive: ML models on your observability data that predict failures, identify improvement opportunities, and auto-tune agent configurations.
Most teams are at Stage 1 or 2. Getting to Stage 3 is where the real ROI lives.
FAQ
What is AI agent observability?
AI agent observability is the practice of monitoring, tracing, and evaluating autonomous AI agents in production. Unlike traditional application monitoring, it accounts for non-deterministic behavior, multi-step reasoning chains, and subjective output quality. It includes traces, evals, session replays, and cost tracking.
How is agent observability different from LLM observability?
LLM observability focuses on individual model calls — latency, token usage, and response quality. Agent observability tracks the entire agent workflow: the sequence of decisions, tool calls, and actions an agent takes to complete a task. An agent might make dozens of LLM calls in a single task, and the quality of the overall outcome depends on how they chain together.
What tools are used for AI agent tracing?
OpenTelemetry is the emerging standard for agent tracing, with agent-specific extensions. LangSmith (for LangChain), Arize Phoenix, Helicone, and Braintrust provide specialized agent tracing. For agent-level management and monitoring (task tracking, heartbeats, status), platforms like AgentCenter complement tracing tools by providing the operational layer.
How do you debug a non-deterministic AI agent?
Start by reproducing the full context (conversation history, system state, tool availability) rather than just the input. Compare traces from failed runs against successful ones to identify where paths diverged. Trace backward from the failure to the root cause. Check common culprits: context window overflow, tool response changes, prompt injection, and model version updates.
What metrics should I track for AI agents?
The essential five: task completion rate, cost per task, output quality score (from evals), error rate by type, and agent utilization. Secondary metrics include trace depth (steps per task), tool failure rates, and latency percentiles. Track trends over time rather than absolute values.
How often should agent evals run in production?
Continuously. Run automated evals on every output for fast feedback. Route 5-10% of outputs to human reviewers for calibration. Run regression tests against a golden dataset before any prompt or model change. Quality drift is gradual — weekly snapshots miss slow degradation.
Can I use existing APM tools for agent observability?
Partially. Existing APM tools handle infrastructure monitoring, error tracking, and basic metrics well. But they lack agent-specific capabilities: execution replays, quality evaluations, multi-step trace visualization, and cost attribution. The best approach is to layer agent observability tools on top of your existing APM stack rather than replacing it.