Traces, monitoring and AI-found issues
In short
Observability records every run as a trace: a tree of every node's input, output, status, timing and cost, replayable exactly as it happened. The Monitoring tab charts requests, latency, tokens and cost over time for a deployment, and the same trace explorer covers SDK workflows that ship their runs in with a callback handler. An AI agent reviews production traces on top of that record and surfaces issues, regressions and failures for you to check.
What Observability does
- Every run traced
- Each run records its full execution tree: every node's input, output, status and timing, replayable exactly as it happened.
- Cost, latency and tokens
- See prompt and completion tokens, dollar cost and duration for every node, not just a total for the run.
- Monitoring over time
- Chart requests, latency, tokens and cost by hour, day, week or month, and drill from a spike straight into the traces behind it.
- Traces from SDK workflows
- Attach a tracing callback handler to a workflow running in your own infrastructure and its runs show up next to deployed App traces.
- An AI agent reviews traces
- An AI agent works through production traces and surfaces issues, regressions and failures for a person to check.
- Downloadable for offline analysis
- Export a single trace or every trace in a date range as JSON, with node-level runs included when you need them.
A run tree with cost, latency and tokens on every node
Open a trace and the execution structure renders as a graph, a tree or a timeline, with every node's resolved input, output, status and timing one click away. LLM and Agent nodes additionally show the rendered prompt after templating, so a wrong answer is traceable to the exact text the model saw, not just the node that produced it. The same view works whether the run came from a deployed App, a workflow test in the editor, or an open-source SDK workflow that shipped its trace in with a tracing callback handler.
- Filter by status and start time, and download a single trace or a whole date range as JSON
- A failed run or a failed node shows an error banner with the exact message
- A voice call's transcript, recording and per-turn latency waterfall are the same idea, applied to a call
Monitoring over time
The Monitoring tab of an App charts four series over a date range you pick, cost split into prompt and completion tokens, total tokens, request counts split into successes and failures, and average latency, bucketed by hour, day, week or month. The same numbers back a metrics API you can pull into your own dashboards. A rising failure count or a latency spike is a starting point, not an answer; every chart is one click from the traces that explain it.
- Traces from SDK workflows need a project-scoped Access Key; an org-scoped key is rejected
- Delivery failures in a tracing handler are logged, never raised, so tracing cannot break a production run
Cost
$412.80
Tokens
18.4M
Requests
96,210
Latency
1.4 s
An AI agent that reviews your traces
Beyond the charts and the trace tree, an AI agent works through your production traces on its own and surfaces issues, regressions and failures for a person to check, rather than waiting for someone to notice a chart moved. It gives a team a starting point for what changed and where, on top of the same trace record every run already produces, so nothing about how you trace or monitor a deployment changes to get it.
- Built on the same traces the Traces and Monitoring tabs already record
- A finding points at the run and the node it came from, not just a metric
Where teams use it
- AI agent governanceScreen what reaches the model, approve what the agent does, and keep a replayable record of every run, with the same controls on every agent.
- Compliance and financial crimeTriage KYC and AML alerts, monitor regulatory change, and draft reports, with every decision routed to an analyst for sign-off.
- Back-office and finance operationsReconcile accounts, clear payment exceptions, prepare dispute cases and draft reports across the systems you already run, with approval before anything commits.
Works with
- EvalsScore an agent on a dataset before you ship, then keep scoring a sample of its live traffic after you deploy.
- Apps and deploymentsDeploy a saved workflow version as an App with its own hostname. Call it over HTTP, embed a chat widget, or trigger it on a schedule or an event.
- Guardrails and approvalsDetect PII and prompt injection before a model sees them, enforce content policies, and pause tool steps for a person to approve.
Questions and answers
What does a trace actually record?
Every node a run touched, with its resolved input, output, status, timing and, for LLM and Agent nodes, the rendered prompt. A run's overall input, output, total duration and token usage sit alongside it.
Can I see traces from workflows that are not deployed on Dynamiq?
Yes. Attach a tracing callback handler to a workflow running in your own infrastructure with the open-source SDK, and its runs report into the same trace explorer as deployed Apps.
What is on the Monitoring tab?
Cost, token usage, request counts and latency for a deployment, charted over a date range you choose and bucketed by hour, day, week or month, with every chart one click from the traces behind it.
What does the AI agent that reviews traces actually do?
It works through your production traces and surfaces issues, regressions and failures for a person to check, on top of the same trace record every run already produces.
Is every run replayable later?
Yes. Every run is traced and replayable, so you can open a run from last week exactly as it executed, node by node, to see what a model saw and what each step returned.
See an agent on your own workflow.
Bring a process and its documents. Our engineers will show you how Dynamiq runs it, in your environment or ours.