> ## Documentation Index
> Fetch the complete documentation index at: https://docs.zespan.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Errors

> Track, group, and triage errors across your agents — model errors, tool failures, timeouts, and rate limits.

The Errors view aggregates failures from all your agent traces into a structured error log. Instead of hunting through individual traces, you see errors grouped by type and agent so you can triage at scale.

## Error types

Zespan classifies errors into four categories:

| Type            | Description                                                                         |
| --------------- | ----------------------------------------------------------------------------------- |
| **Model error** | The LLM returned an error response (e.g. content policy violation, invalid request) |
| **Tool error**  | A tool call threw an exception or returned an error status                          |
| **Timeout**     | A span exceeded its deadline — either an LLM call or a tool invocation              |
| **Rate limit**  | A 429 response from a model provider                                                |

## Error list

The error list shows all errors in the selected time range, with:

* Error message (truncated to 200 characters)
* Error type
* Agent that produced the error
* Model (for model errors)
* Tool name (for tool errors)
* Timestamp and count (how many times this exact error occurred)

Errors with identical messages and sources are grouped automatically. A group showing 47 occurrences means the same error happened 47 times — not 47 unique bugs.

## Filtering

Filter by error type, agent, model, environment, or date range. The **Only new** toggle shows only errors that first appeared in the selected period — useful for spotting regressions after a deployment.

## Opening the source trace

Click any error row to open the trace that produced it. The flame graph highlights the failed span so you can see the full context — what the agent was doing when the error occurred, what inputs led to it, and what happened immediately before.

## Error rate metrics

The error rate chart at the top of the page shows errors per hour over the selected period. A flat baseline with sudden spikes indicates an external event (provider outage, a bad deployment). A gradually increasing baseline indicates a growing problem.

## Next steps

* [Incidents](/dashboard/incidents) — AI-detected anomalies, including error surges
* [Alerts](/dashboard/alerts) — set up notifications when error rates exceed a threshold
* [Traces](/dashboard/traces) — investigate individual failed runs