AI Workflow Observability

An agent's behavior emerges from a chain of model decisions and tool calls. When the output is wrong, the cause could be any link in that chain — a bad prompt, a tool that returned junk, a retry that changed the state, a rate limit that reordered things. Flat text logs rarely reconstruct that story. Useful observability for AI workflows means being able to see:

• The execution timeline of a run, node by node.
• Each node's execution: when it started, finished, and its status.
• Every tool call and LLM request, with their inputs and outputs.
• Latency per step and across the run.
• Failures and retries — what failed, how many attempts, and why.
• Complete workflow history, so nothing is lost between steps.
• Cost visibility: tokens and dollars, attributable to nodes.

Every step is an event

A durable run isn't just checkpointed for recovery — the same event log is what you read to understand it. Each tool call, model response, retry and approval is an immutable event with its inputs and outputs attached.

Replay instead of guess

Because the history is complete and immutable, you can replay a run to reproduce exactly what happened. Debugging becomes deterministic: you're not reconstructing state from scattered logs, you're reading the record of the run itself.

• Immutable event history. Every tool call, LLM response, retry and approval is recorded and inspectable.
• Replay any run. Reproduce a run from its history to debug a failure deterministically.
• Live tokens and cost. Usage is tracked as the run executes, so spend is visible per run — not a monthly surprise.
• Retry visibility. See where a node backed off and retried, and how it eventually resolved.

What is AI workflow observability?

The ability to see exactly what an agent workflow did: each node's execution, the tool calls and LLM requests it made, their inputs and outputs, latency, retries, failures, and token/cost usage — as a timeline you can inspect and replay.

How do you debug an agent workflow that failed?

Start from the run's execution timeline, find the node that failed, and read its inputs, outputs and error. Because a durable run records every step as an immutable event, you can replay the run to reproduce the failure deterministically instead of guessing from logs.

Why are tokens and cost part of observability?

LLM calls are the dominant cost and a common source of surprises. Tracking tokens and cost per node and per run turns spend into something you can see, attribute and control — not a monthly invoice shock.