Analytics

Usage

Use Usage to understand overall activity in the platform. This tab is the first place to check when you want to know whether users are actively working with agents and conversations.

The Usage screenshot shows headline KPIs for conversations, messages, connected data sources, and active agents. Below the KPIs, the monthly conversations chart makes adoption trends visible across the calendar year. The side panel lists recent negative feedback so admins can jump from an analytics signal into the conversations or agents that need follow-up.

Typical questions:

• Are conversations and messages increasing or decreasing?
• Did activity change after a rollout, workflow update, or agent configuration change?
• Are users engaging with the expected assistants?

Cost

Use Cost to track token and model spend. This tab helps admins understand how AI usage translates into consumption and where limits or optimization may be needed.

The Cost tab is connected to the same cost and token accounting that powers organization usage controls. The date range selector applies to all cards and charts on the tab, so change it first before comparing totals.

The headline cards separate token usage into:

• Input token count: tokens sent into model requests, including user messages, instructions, retrieved context, tool context, and other prompt material.
• Output token count: tokens generated by the model in the final answer or intermediate assistant output.
• Reasoning token count: internal reasoning tokens reported by models that expose reasoning usage.
• Total token count: combined input, output, and reasoning tokens for the selected range.

The Token Consumption chart shows daily token volume split by input, output, and reasoning tokens. Use it to find the exact day where usage increased before opening conversations, workflows, or agent analytics for deeper investigation.

The lower Cost charts explain where the token total came from:

• Tokens by Model shows the share of selected-range tokens by model. This is the fastest way to spot whether traffic is concentrated on a more expensive or more capable model than expected.
• Tokens by Agent shows the share of selected-range tokens by agent. Use it to find the agents that are driving organization spend and decide where prompt, retrieval, workflow, or model tuning should happen first.
• Token Consumption by Model shows model usage over time. The stacked bars make model migrations, fallback behavior, and one-day spikes visible; the tooltip exposes the exact token count per model for a selected date.

If an Azure AI Foundry model router deployment is used, Siesta AI analytics should be read together with Azure Monitor. Siesta AI shows which agent, team, user, or model connection is consuming tokens; Azure Monitor is the source of truth for the router's underlying model distribution.

Typical questions:

• Which period has the highest AI cost?
• Did a newly deployed agent or workflow increase usage?
• Should model connections receive stricter token limits?
• Which model is responsible for most token consumption?
• Which agent is responsible for a usage spike?

Limits

Use Limits to monitor and configure preventive token guardrails for LLM/model connections. Cost analytics shows historical usage; Limits shows how current daily and weekly usage compares with configured budgets.

The provider selector at the top of the tab chooses the model provider or model connection whose limits you are reviewing, such as OpenAI. The top cards summarize the current limit state:

• Daily org usage: organization token usage today compared with the configured organization daily limit.
• Weekly org usage: organization token usage this week compared with the configured organization weekly limit.
• Closest daily limit: the nearest daily limit to being exhausted across organization, team, or user scopes.
• Closest weekly limit: the nearest weekly limit to being exhausted across organization, team, or user scopes.

Each card shows the percentage used, a progress bar, the consumed tokens, and the configured limit. Status labels such as Safe indicate whether the current usage is still within the expected budget.

The Configured limits table shows the active default budgets for organization, team, and user scopes. Use Manage limits to open the editable connection-level limit settings.

The Limit utilization table compares actual usage with configured daily and weekly limits for individual users or teams. Switch between Users and Teams to find who is closest to a budget threshold. Each row shows the consumed token count, configured limit, percentage used, and a progress bar for both daily and weekly windows.

Use this view before changing limits: if usage is concentrated in one team, adjust the team override; if the whole organization is approaching the same threshold, change the organization default.

The LLM token limit view is scoped to a specific model connection, for example Siesta AI LLM - Default. Limits are configured in millions of tokens (M) and can be defined at multiple levels:

• User defaults: the default daily and weekly budget for users on the selected connection.
• Team defaults: the default daily and weekly budget for teams on the selected connection.
• Organization defaults: the workspace-level daily and weekly budget for the selected connection.
• User overrides: per-user rows where admins can set a custom daily or weekly budget and disable a specific override.
• Team overrides: per-team rows for team-specific budgets.

Save changes after editing defaults or override rows. When a configured budget is exceeded, Siesta AI should stop the model request with a controlled token-limit error instead of allowing unlimited spend.

Limits are most useful after Cost analytics identifies a high-volume model, agent, or team. Set the broad organization default first, then add user or team overrides only where the real usage pattern justifies a different budget.

Data

Use Data to monitor data-source and collection activity. This is useful when agents rely on uploaded files, synced sources, or knowledge collections.

The Data screenshot summarizes the current data inventory: total storage, number of data collections, source types, and files. The donut charts break storage down by collection and source connection, and show document counts by file type. This helps admins verify whether the expected data sources are present and whether one collection or connection dominates storage usage.

Use this tab together with Data Collections when a collection exists but answer quality is low. Analytics tells you whether the data footprint looks healthy; the collection detail tells you whether the expected documents, chunks, and sync runs are actually present.

Typical questions:

• Are data collections being used and updated?
• Are new sources being added as expected?
• Does low agent quality correlate with missing or stale data?

Workflows

Use Workflows to monitor workflow usage and outcomes. This view is useful after publishing a workflow to a pilot team or after making a workflow available more broadly.

The Workflows tab summarizes automation activity for the selected date range. The KPI cards show:

• Workflow Executions: total workflow runs in the range.
• Operations: total executed workflow operations, which can be higher than executions because one workflow run may contain multiple steps.
• Operation Categories: number of operation categories represented in the range.
• Active Periods: number of time buckets where workflow activity occurred.

Use Executions Over Time to see workflow runs grouped by the selected time range. This chart is the fastest way to spot rollout effects, schedule spikes, quiet periods, or a sudden drop in automation activity.

Use Operations by Workflow to understand which workflow or operation type is responsible for most automation volume. The donut chart shows share, count, and percentage for categories such as assistant-triggered runs, scheduled workflows, webhook workflows, recording transcription flows, and tool functions.

This tab is especially useful after releasing a new workflow to production or after changing triggers, conditions, or external connections. Compare expected operational volume with the real run pattern before assuming a workflow is healthy.

Typical questions:

• Which workflows are being run most often?
• Are workflow runs completing successfully?
• Did a workflow change affect usage or failure patterns?

Recordings

Use Recordings to review recording activity and processing trends. This helps teams understand whether recordings are being captured, processed, and reused as expected.

The Recordings screenshot shows the total number of recordings, peak recording volume, and active recording periods. The time-series chart groups created recordings by week for the selected range, making it easier to spot adoption spikes, quiet periods, or irregular recording behavior.

If recordings are used as a source for follow-up chat, analytics, or operational review, use this tab to confirm that uploads and processing continue at the expected pace after a rollout.

Typical questions:

• Are recordings being created regularly?
• Are there periods with unusual recording activity?
• Does recording usage support the intended team workflow?

Chart Interpretation

Below the KPIs, analytics views use charts to show trends over time. For quick diagnostics:

• Sharp drop = check the availability of agents, connected channels, or recent changes in prompts or workflows.
• Growth = verify whether capacity (rate limits, resources) is keeping up.

Feedback and Follow-up

When analytics points to quality or adoption problems, follow the signal into the related operational page:

• Open Conversations to inspect the conversation that created the signal.
• Open Agents to adjust instructions, tools, prompts, or model settings.
• Open Data to check whether the agent has the right data collection.
• Open Workflows when the signal may come from automation volume or failed orchestration behavior.
• Open Recordings when the issue is tied to transcript availability or uneven recording intake.
• Open Logs to inspect audit events or tool executions.

Tips for Working with Data

• Monitor daily changes in KPIs to quickly identify fluctuations.
• If the number of messages is increasing without a rise in conversations, check the quality of responses (feedback) and possibly adjust the instructions.
• With zero data sources, verify that agents have the correct datasets and access assigned.