Summary

The Enrichment Agent dashboard, found under AI Agents in Analytics, gives administrators and content teams visibility into how Enrichment Agents are performing across the portal: how often agents run, how many suggestions get accepted or discarded, and who is generating that activity. Use it to track adoption, monitor consumption, spot agents that are ready to run with less manual review, and calculate the ROI of your enrichment workflows.

Who?

• Administrators: monitor overall agent adoption and consumption, and set usage alerts.
• Content managers and team leads: track how individual agents and users are performing, identify review backlogs, and build the business case for AI Agents.

Why?

Key questions this dashboard helps answer:

• How much enrichment activity is happening, and is it trending up or down?
• Which agents generate suggestions that get accepted, and which ones are mostly getting discarded?
• Which agents are performing consistently well enough to run with less manual review?
• Who is running agents, and how much of their output still needs review?
• How many assets were actually enriched, and what is that worth to the business?

How?

Accessing the dashboard

1. Navigate to Analytics.
2. Open AI Agents > Enrichment Agents.
3. Use the filters at the top to scope the view: Event Date, Agent Name, User Name, Group Name, and Profile Name.

1. Track your Consumption

This is usually the first thing to check: how much are we actually using this, and is it trending up or down. The top metric cards give you the headline numbers for the selected date range, and the Enrichments over Time chart shows the trend.

Key metrics

[Screenshot: Agent Runs, Enrichments, Discards, In Review metric cards]

Enrichments over Time: shows how enrichment activity evolves across the agent funnel: agent runs, accepted enrichments, and discards, by month.

2. Spot Agents Ready for Automation

Once you know your overall volume, use the Acceptance Rate over Time and Enrichments by Agent widgets to find agents that are consistently performing well and could run with less manual review.

Acceptance Rate over Time: the Reviewed Acceptance Rate plotted by period, so you can see whether suggestion quality is improving or declining.

Enrichments by Agent: breakdown of performance per agent, including runs, suggestions, accepted enrichments, discards, and the acceptance rate for each specific agent.

Sort this table by Reviewed Acceptance Rate to see which agents are the strongest candidates for automation. An agent that has run on meaningful volume and consistently shows a high acceptance rate with few discards is a good candidate to:

• Enable Auto-apply on the agent, so its suggestions write straight to the asset's metadata without waiting in the review queue. See Agent Library: Advanced features.
• Trigger it via an Automation Rule (for example, on upload), so it runs without a person having to select assets and launch it manually. See How To Use Bynder's AI Automation Workflows.

AI can make mistakes. We recommend that you only apply automations after careful consideration and in consultation with the respected teams in your organization.

Conversely, agents low on this list with a high discard rate are good candidates for prompt tuning before you consider automating them further.

3. Track Usage by Team and User

Enrichments by User: breakdown of activity by the users who initiated agent runs. Attributes all generated suggestions and outcomes (accepted, edited, discarded) to the user who triggered the agent. Use this to track adoption and usage within the organization.

4. Drill into Detail

User Detailed Activity: a detailed log of individual enrichment interactions, including agent runs, suggestions, accepted enrichments, and discards, per user and date.

Asset Detailed Activity: a detailed view of enrichment activity at the asset level, including suggestions, accepted enrichments, and discards.

5. Calculate your ROI

Consumption and quality data are also the raw inputs for ROI. The Enrichments metric is your best proxy for "assets actually enriched" in a given period, since it only counts suggestions that were reviewed and accepted, not just attempted.

Use it to estimate the value delivered:

• Estimated hours saved = Enrichments × the average time a person previously spent manually enriching one asset for that workflow (tagging, writing alt text, translating).
• Estimated cost saved = Estimated hours saved × your team's average hourly cost, plus any agency or translation spend now avoided per asset.

For example, if the Enrichments metric shows 1,200 accepted suggestions for alt text and descriptions last quarter, and manual tagging previously took roughly 4 minutes per asset, that is close to 80 hours saved, before adding any agency costs no longer needed for that work.

Filter by Agent Name or Group Name to isolate the Enrichments count for a specific workflow (for example, one product line or campaign), so you can compare it against the "before" baseline for that same workflow.

For the full methodology, including resource savings, productivity gains, cost reduction, asset reuse, and risk mitigation, see Tips for Measuring the Value of Your AI Agents.

Setting usage alerts

From the dashboard, administrators can click the bell icon on a metric card (for example, Agent Runs or Discards) to configure a notification threshold. This alerts you when agent run volume approaches or reaches a set number, giving you an early warning if consumption is trending higher than expected. This is one of several controls Bynder provides for governing AI Agent usage, alongside permission profile access (deciding who can manage or run agents) and the choice between manual and automated agent triggers.

Related Articles

Enrichment Agent: Automate Asset Metadata

Agent Library: Creating, Running, and Updating Agents

Workspace: Agent Activity - Reviewing, Editing, and Approving Agent Suggestions

Updated July 27, 2026 11:10