Analytics & Reporting

The Unbound dashboard provides analytics at several levels — from organization-wide summaries down to a single task trace, plus a full inventory of the AI tools running across your fleet. This page covers the main views and what each one is for. Analytics answers what your agents did. AI Tools Discovery answers what your developers have installed. Most teams use both.

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Analytics

Dashboard: gateway.getunbound.ai/analytics

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Overview

Organization-wide spend and activity at a glance — cost and token consumption over time, how spend splits across models, and response latency. Start here to see the shape of your AI usage before drilling into any one team or developer.

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Tasks

Each agent task that has finished processing, classified by the kind of work it was doing, with cost and token consumption attached. Shows spend by work type, so you can answer “what are we actually buying with our AI budget” — refactoring, tests, debugging, new features. Tasks are placed on the timeline by when they started, so one still running or still being classified won’t be listed yet.

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Agentic Use

What your agents did, split into three views:

• Terminal Run — the shell commands agents executed, with a risk level on each.
• MCP Actions — the MCP servers and tools agents called, with risk categorization and per-user and per-server breakdowns. Each server row carries a Raw logs action. It opens the full call history for that one server: every MCP tool call with its tool name, the developer who made it, and when. Expand a call to see the exact arguments sent and the output returned. Narrow it with the Tool filter. Large values are handled in two places, and the page tells you which applies. A call whose arguments or output were too big to store in full shows a note with the original size and the stored preview. Anything over 4,000 characters is trimmed on screen with a Show full link — nothing is hidden, it just stops a multi-megabyte response freezing the page. Raw logs may not be enabled for your organization; where it is, it’s visible to the same roles that can see Agentic Use.
• Skills — which Agent Skills were actually run, by whom, and through which agent. See Skills below.

Each action is attributed to a user and session, and carries how it was initiated — user-intentional, agent-initiated, or unknown when there isn’t enough signal to tell. Attribution is applied shortly after the action lands, so the newest rows can show as unclassified for a short while. This is the fastest way to see where a Tool Policy would have the most impact — watch the traffic here first, then write the rule. Terminal Run, MCP Actions, unsanctioned MCP usage and the MCP server list can each be exported.

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Code Stats

How much of your codebase your agents are actually writing, broken down by tool and by model. Coverage is wider than it used to be — Claude Code, Claude Cowork, Cursor, Codex, GitHub Copilot and Augment Code all report the file edits their agents make. Shows your top code generators and the files and functionality most affected.

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Data

Every time a Security Policy guardrail fires — sensitive data like PII or a secret detected in a prompt, and whether it was audited or blocked — it lands here. Two views:

• Summary — Top Tools with Guardrail Actions: which AI tools are triggering the most guardrail activity, as a table of tool, guardrail, action, and hit count. Start here to see where sensitive data is concentrated.
• Logs — the individual events behind those counts, each with its timestamp, tool, user, the guardrails that fired, and the action taken. Filter by tool type, guardrail, or action, and open any event for the full detail.

Click a row in Summary to drop straight into Logs pre-filtered to that tool, guardrail, and action — the fast path from which tool is leaking to show me exactly what it sent. Go back to Summary and that drill-down clears itself, while any filters you set by hand stay put. This is the observability side of Security Policies: the policies decide what to catch, and this tab shows you what they caught.

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Usage

Two views of how your organization is using AI, at Analytics → Usage. Adoption reports take-up: weekly and monthly active users, a Claude Code Users · WAU figure, an Adoption Funnel from seats through to weekly habit, Weekly Active Users by agent, and Retention by Team. Choose a window of 4, 8 or 12 weeks; every chart compares against a 50% adoption target. The team breakdown is built from the Okta directory sync. Policy Enforcement reports Account Access refusals — which sign-in attempts your policies turned away, and why. Adoption is licensed per organization. Policy Enforcement is available to every Admin and Analytics Viewer.

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Users

Per-developer activity, oriented around how agentic each developer’s workflow is: how long their agents run unattended, median run length, clean-completion rate, how many tasks they worked on, tokens, cost, cache-hit rate, and how many distinct models they used. Open a developer to see which models they actually leaned on and what share of their spend each one took. Use it to see which developers are getting real leverage from agents, and which are still babysitting every step. Clean-completion rate and median run length stay blank until a developer has enough agent runs in the window for the number to mean something — a quiet week reads as blank, not as zero. The same view ranks developers by consumption. Power Users and Light Users are the same list sorted highest-first and lowest-first, so you can read either end of the distribution; Top Experimenters is a separate list of the five developers who reached for the widest range of models.

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Task Logs

Dashboard: gateway.getunbound.ai/logs Every agent session, logged with full prompt-level detail — messages sent and received, models used, token counts, tool calls made, and policy decisions. Filter by user, model, tool type, or date range, then click any task to trace the whole conversation end to end. This is where you go when you need to answer “what exactly happened here.”

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AI Tools Discovery

Dashboard: gateway.getunbound.ai/ai-tools-discovery Analytics shows you the agents routed through Unbound. Discovery shows you everything installed across your fleet — including the tools nobody told you about. It’s powered by unbound discover.

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Summary

The fleet at a glance: how many AI tools are in use, how they’re distributed across your org, and which users carry the most risk. From here you can sanction the agents you approve of — everything else is surfaced as unsanctioned, which is how you find Shadow AI.

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Inventory

The full catalogue of what Discovery found, in four views:

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Users

Discovery from the people side — what each developer has installed, rather than what’s deployed across the fleet. Open any user for a per-user risk view: their tools, their risk factors, and what’s driving their score.

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Permissions

What your agents are permitted to do on each machine, with recommendations on the permissions worth denying.

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Skills

Dashboard: gateway.getunbound.ai/analytics/agentic-use?tab=skills — a sub-tab of Analytics → Agentic Use, not a page of its own. Which Agent Skills your developers are actually running — how often, by whom, and through which agent. AI Tools Discovery tells you which skills are installed across your fleet; this tells you which ones get used. Start here to see which skills have caught on, which sit unused, and who’s leaning on them. Headline numbers up top — total invocations, distinct skills used, and how many developers used them over the period — then a daily invocation trend, your most-invoked skills, and a breakdown of which agent (Claude Code, Cursor, and the rest) each skill was called from. Filter the whole view by skill, tool, user, or time range. The All Skills table lists every skill with its invocation count, unique users, the tools that ran it, a risk level, and when it was last used. Open any skill to see its usage over time, its top users, and the rendered SKILL.md content itself — including each distinct version when several copies of a skill share one name across your fleet.

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Insights

Dashboard: gateway.getunbound.ai/insights How well your developers are actually prompting their agents — and where they’re spending tokens on prompts that could be tighter. Where Analytics measures usage and cost, Insights looks at prompt quality: it scores each developer’s prompting efficiency, flags recurring weak spots, and suggests concrete ways to improve. The main view ranks your users by an efficiency score, alongside their task count, API calls, tool types, cost, and the categories of weak prompting showing up in their work. A Top Recommendations panel pulls out the highest-impact fixes across the org. Open any user to drill into their individual tasks — each with its own efficiency score, cost, and summary — to see which prompts are dragging the score down.

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Custom Reports

Dashboard: gateway.getunbound.ai/custom-reports Query your Unbound data in natural language, or with a structured builder. Example queries:

• “Which users ran the most delete commands this week?”
• “What models did my team use most in April?”
• “Show me all policy matches grouped by command family”

Queries can be saved, shared with your team, and assembled into Dashboards — reusable boards of the reports your team checks regularly.

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Billing & Usage Export

For cost-focused reporting, see Cost Policies. Admins can also export a full usage report with daily granularity — broken down by user and model — from Billing → Usage → Export in the dashboard.