Run & observe
Logs & traces
Every run on the platform — playground chats, swarm nodes, notebook calls — is recorded as a trace. Reading traces is the core debugging skill in agentic systems, and the one course environments almost never let you practice.
The traces table
/traces lists every run with the agent name, provider and model, latency, tokens in and out, dollar cost, status, and timestamp. Sort or scan for the rows that look wrong — the red statuses, the latency outliers, the runs that cost ten times their neighbours.
What a trace contains
Selecting a run opens the full record:
- Metrics
- Latency, tokens in, tokens out, and cost computed from model pricing.
- Prompt
- The prompt for the run, with the agent and model that handled it.
- Tool calls
- Each tool the model called, with the arguments it chose and the result it got back. If a tool you expected is never called, check the request payload to confirm it was offered at all.
- Request / response payloads
- The raw provider request and response. This is the ground truth: the exact message array, parameters, and tool definitions the model actually received, and exactly what it returned.
- Error
- For failed runs, the error message the runtime captured.
The playground inspector
While chatting in the Playground, the inspector panel shows the same information live, in three tabs: the latest request/response exchange, the stream of tool events as they happen (with a running call count), and the trace for the current conversation. For swarm runs, the observability view adds the per-node timeline.
A debugging method that works
- Reproduce, then read. Re-run the failing input, open the trace, and read the request payload before forming a theory. Most "the model is broken" reports turn out to be "the model was sent something other than what I assumed".
- Change one thing. Adjust a single line of prompt, one parameter, or the model — then run the same input and compare the two traces. Keeping the old trace open in a second tab is the closest thing prompt engineering has to a scientific method.
- Watch cost as a signal. A run whose cost jumps an order of magnitude usually means a loop, a context blow-up, or a tool feeding the model far more text than intended — the trace shows which.