View agent activity

Use W&B Weave’s Agents view to understand what your agent did, how much it cost, and exactly where things went right or wrong.

The Agents view gives you a turn-by-turn record of every conversation your agent had, along with token usage, tool invocations, and execution spans.

Agent applications are hard to debug because the interesting behavior happens between the user’s request and the final response. The Agents view in W&B Weave makes that middle layer visible. Every conversation your agent had is captured here, with the full message history, span-level execution detail, and token costs attached. You can see at a glance whether an agent completed its task, how many tool calls it made, and where time or budget was spent. For teams building and iterating on agents, this is the starting point for understanding behavior in production.

Get started

To open the Agents view for your project:

1. Navigate to https://wandb.ai and select your project.
2. In the sidebar menu, select Agents to view all agent conversations saved for your project.

The Agents view is organized into a row of tabs across the top: Dashboard, Agents, Conversations, Spans, and Signals. The Dashboard tab gives a project-wide overview of agent activity, while the other tabs let you drill into individual agents, conversations, spans, and signals.

Dashboard tab

The Dashboard tab is a project-wide overview of all agent activity. It’s designed to be read at a glance, so teams use it as the starting point for a daily health check before drilling into a specific agent or conversation.

At the top, summary cards report totals for the selected time window: Spans, Conversations, Tokens, Cost, Error rate, and Agents. Below the cards, time-series charts plot span, conversation, token, and cost volume over time, alongside error counts and signal activity. The bottom row breaks down the top tools, agents, models, and providers by usage. A list of recent conversations or spans appears on the left, toggleable between Conversations and Spans, and the time window selector controls the period that every panel reflects.

Agents tab

The Agents tab gives you a high-level view of all agents that have logged traces to this project. Use it to spot which agents are active and to compare latency and error rates across agents. It also helps you identify agents that need attention before drilling into individual conversations.

It’s useful for scenarios such as:

• Monitoring a fleet of agents. The card grid lets you compare latency and error rate across all agents at once without opening individual conversations. A latency spike or a newly red error rate on one card signals a regression worth investigating.
• Identifying stale agents. Sorting by Last seen highlights agents that haven’t recorded activity recently. This is useful for confirming a deployment is live or spotting agents that may have stopped logging traces unexpectedly.
• Comparing versions. The version count on each card shows how many distinct versions of that agent have been deployed. A high version count alongside a rising error rate may indicate a regression introduced in a recent deployment.
• Drilling into an agent. Click any card to open the detail panel for that agent, from which you can navigate to its conversations or spans:

Agent cards

Each agent is represented as a card showing:

Field	Description
Agent name	The name logged with the agent’s traces.
Last seen	How long ago the agent last recorded activity.
Version	The number of distinct agent_version values recorded across the agent’s spans.
Activity histogram	A bar chart of recent conversation volume, giving a quick sense of usage trends.
Conversations	Total number of conversations recorded.
Spans	Total number of spans recorded across all conversations.
Cost	Total cost incurred across the agent’s conversations. Shows a dash (-) when cost data isn’t available.
Latency (avg.)	Average end-to-end duration per invocation.
Error rate	Percentage of invocations that returned an error. Displays in red when greater than 0%.

Find and sort agents

Use the Search and filter agents field to find agents by name.

Use the sort dropdown (default: Last seen) to reorder the grid. The available sort options are:

• Last seen: Most recently active agents first.
• Most invocations: Highest conversation volume first.
• Most input tokens: Highest token consumption first.
• Most errors: Highest error count first.

Sorting by Most errors is useful for a quick daily health check: agents with non-zero error rates surface immediately, and the red error rate on the card confirms at a glance which need investigation.

Conversations tab

The Conversations tab on the Agents page lets you browse, filter, and inspect individual agent runs. Use it to investigate failures, measure token costs, and understand the sequence of LLM calls and tool executions that made up a run.

For high-level questions about what an agent said and did across a conversation, start with the Conversations tab.

Conversations table

The conversation table shows one row per conversation. The following columns appear by default:

Column	Description
Conversation	The conversation ID and a preview of the first message.
Last message	A preview of the most recent message, with a role indicator.
Spans	Total number of spans recorded, shown alongside a color-coded strip (described below). Higher span counts indicate more branching or tool use.
Tags	Signal tags and ratings applied to the conversation.
Agent	The name of the agent or agents involved.
Invocations	How many times the agent was invoked during the conversation.
In tokens	Input tokens consumed.
Out tokens	Output tokens generated.
Cost	Total cost of the conversation.
Started	When the conversation began.
Last activity	How long ago the last message was recorded.

The Spans column also renders a color-coded strip that previews the sequence of events in the conversation, using the same event colors as the Events timeline. This lets you tell at a glance whether a conversation was tool-heavy, LLM-heavy, or involved sub-agent delegation without opening it.

To show or hide additional columns, click Columns in the toolbar.

Filters and time window

Use the Filter bar to narrow results by agent, model, error status, or other attributes.

Custom attributes that you stamp on agent spans with the SDK are also filterable here. You can filter the conversation list to a specific value for a specific attribute.

To show a custom attribute as its own column, click Columns in the toolbar. For how to set these attributes, see Set attributes and events on agent spans.

Use the time window selector (1m, 1h, 6h, 24h, 7d, or 30d) to restrict the list to conversations that were active within that period. The conversation volume histogram above the list updates to reflect the selected window.

Hover over any column header in the conversation list to filter that column to a specific value or range.

Agent conversation detail

Click a conversation row to open a detail panel with two sub-panels: Turns and Events. The panel header shows the agent name and conversation ID, along with Summarize (to generate a summary of the conversation) and Add to dataset actions.

Turns

The conversation detail turns panel shows each turn in chronological order, numbered from 1.

Each turn displays the number of intermediate responses and tool calls, and the total wall-clock duration. Expand a turn to see the full message thread.

Messages

Within a turn, messages are grouped by role.

User messages show the message text and any attached media or content references.

Assistant messages show the following:

• The agent name and the model used (for example, gpt-5.5-2026-04-23).
• Timestamp and duration.
• Input and output token counts and cost (for example, 18823 in · 96 out · $0.0717).
• An expandable Reasoning section when the model used extended thinking.
• The response text, which collapses automatically for long responses.

Tool calls show the tool name, timestamp, and duration. If argument or result data is available, the tool call is expandable and shows Args and Result in a key-value table. If the call failed, an ERROR badge appears.

Error states

When a tool call returns an error status, a red ERROR badge appears inline next to it. In the Events timeline, that event also displays in red regardless of its type.

Events

The Events panel on the right shows a color-coded strip that represents the sequence of events within the selected turn.

In the Events timeline, each segment’s color indicates the event type.

Color	Event type
Purple	User message
Green	Assistant message
Blue	Tool call
Sienna	Sub-agent invocation
Magenta	Agent handoff
Gray	Context compaction
Red	Any event that returned an error

Use the Events timeline to get a quick sense of how a turn was structured. For example, you can see whether it was LLM-heavy, tool-heavy, or involved sub-agent delegation before reading the full message thread.

Scores

If any signals are active for the project, a Scores section provides metrics for the conversation. It shows the signal scorer name, an overall numeric rating from 0 to 1, a confidence percentage, and the individual rubric points that contributed to the score. Each rubric point also shows its own confidence. Use this to understand not just whether a turn scored well, but which specific rubric criteria passed or failed.

Meta summary

The Meta summary section shows aggregate statistics for the selected conversation.

Field	Description
Tokens	Total input and output tokens.
Cost	Total cost of the conversation.
Tool calls	Number of tool calls across all turns.
Messages	Total message count.
Conversation time	Wall-clock duration from first to last message.
Turn page	Which turns are currently displayed, and the total turn count.

Token breakdown

The Token breakdown section shows cache and reasoning details for the selected conversation.

Field	Description
Cache read	Tokens served from the prompt cache.
Cache written	Tokens written to the prompt cache.
Cache hit rate	Percentage of input tokens served from cache. A higher rate reduces cost and latency.
Reasoning	Tokens spent on extended thinking.
Reasoning ratio	Percentage of output tokens spent on extended thinking.

Participants

The Participants section lists the agents and models involved in the conversation. In multi-agent conversations, different turns may show different model names here.

Add agent messages to a dataset

To add agent messages to a Dataset:

1. In the conversation details panel header, click Add to dataset to open the Add example to dataset drawer.
2. In Choose a dataset, use the dropdown to choose the dataset you want to add traces to.
3. In Select context, select which messages to add to the dataset. Click Next.
4. Review your selections and click Add to dataset.

Spans tab

The Spans tab shows every individual span recorded across all agent activity in the project. The Conversations tab aggregates activity into dialogue-level rows. The Spans tab exposes the raw operations underneath: each LLM call, tool execution, and agent invocation as its own row. Use it to trace exactly which call was slow, which model consumed unexpected tokens, or which tool invocation failed.

Spans table

The span table shares most columns with the Conversations table (agent, model, tool, token counts, status). Some columns unique to this view are:

Column	Description
Span	The span name and ID, with its trace ID below.
Kind	The OpenTelemetry span kind for this operation (such as INTERNAL, SERVER, or CLIENT).
Operation	The operation type (such as chat, execute_tool, or invoke_agent).
Ratings & Tags	Signal ratings and tags applied to the span.
Finish	The finish reason returned by the model (such as stop or max_tokens). Populated only for chat spans where the model reports a finish reason.
Error type	The type of error returned by the span, when one occurred.

Additional columns for cache token breakdowns, reasoning tokens, LLM parameters, and W&B run metadata are available through the Columns button.

The Spans tab is most useful when you need operation-level precision that the Conversations tab doesn’t provide:

• Identifying expensive calls. Sort by In or Out tokens to find which individual LLM calls are driving cost, rather than seeing totals at the conversation level.
• Debugging a specific operation type. Filter by Operation to isolate all execute_tool spans and check error rates, or all chat spans for a specific model.
• Investigating truncation. Filter Finish by max_tokens to find spans where the model hit its token limit rather than completing normally.
• Correlating with a W&B run. Hidden-by-default columns expose W&B run IDs and run steps, letting you link a specific span back to a training or evaluation run in W&B.

Trace groups

Click any row to select its trace and highlight all other spans that share the same trace ID. This shows you the full set of operations that ran as part of one agent invocation. Grouping here is by trace, not by conversation. This means a single conversation may contain multiple traces if it involved sub-agent delegation.

Agent invocation detail

Click a row in the Spans table to open a detail panel populated with data from the complete agent invocation.

At the top of the detail panel, a bar chart shows the wall-clock position and relative duration of every span in the selected trace, arranged as a waterfall. The parent invocation spans the full width, and each child span appears below it as a colored bar scaled to its actual duration, positioned at its start time in milliseconds from the beginning of the trace. Use the timeline to:

• Spot the longest operation at a glance. Wide bars indicate spans that dominated total latency.
• See parallelism. Overlapping bars indicate spans that executed concurrently rather than sequentially.
• Inspect any span inline. Click a bar in the timeline to load that span’s details into view, including its input messages and output messages, token counts, and other metadata.

You can alternatively view the child spans as a hierarchical trace tree by selecting the Show trace tree icon in the detail panel header.

Signals tab

The Signals tab shows tags and ratings for your agent’s conversations. Signals surface quality and safety issues to flag problems, find patterns, and highlight the traces that need your attention. Use signals to automatically score the quality of your agent’s responses, notice when a user is frustrated, or flag NSFW content.

For setup and detailed usage, see Monitor your agents with signals.