Usage Intelligence (dw-usage-intelligence)

The Usage Intelligence agent answers the question every platform owner eventually asks: is anyone actually using this, and how? It measures practitioner interactions with the Data Workers platform itself — which tools and agents are called, by whom, when, in what sequences — and turns that into adoption dashboards, session analytics, usage heatmaps, and anomaly detection.

It is deliberately zero-LLM: every number comes from deterministic computation over recorded activity, not from a model’s summary. That makes it cheap to run continuously and makes its outputs reproducible — the same activity log always yields the same dashboard. It’s how you find out which agents earned adoption, which are shelfware, and where a sudden usage drop is telling you something broke.

Key capabilities

• Tool and agent usage metrics. get_tool_usage_metrics reports usage volume, unique users, trend direction, and response times, grouped by tool, agent, or user.
• Adoption dashboards. get_adoption_dashboard classifies agents and tools as adopted, growing, underused, or shelfware, against a configurable adoption threshold.
• Session analytics. get_session_analytics measures session duration, depth (tools per session), agents per session, and classifies users as power, regular, or occasional.
• Workflow patterns. get_workflow_patterns finds the multi-tool and multi-agent sequences practitioners actually chain together, and how much usage is standalone versus part of a workflow.
• Usage heatmaps. get_usage_heatmap shows when and where people interact — hourly, daily, or agent-by-user.
• Usage anomaly detection. detect_usage_anomalies flags sudden drops (friction), unusual spikes (automation loops or incidents), and behavior shifts, at configurable sensitivity.
• Hash-chained activity log. get_usage_activity_log retrieves the SHA-256 hash-chained record of who called which tool, when, with what outcome — and verifies chain integrity.
• Agent health. list_active_agents and check_agent_health report which agents are running and how they’re doing.

Example prompts

“Which agents got adopted last month and which are shelfware?”

“Show me the usage heatmap for the last 30 days — when do people actually use this?”

“What tool sequences do our power users chain together?”

“Usage of the quality agent dropped 60% this week — is that friction or an outage?”

“Pull the activity log for last week and verify the hash chain.”

Connect it to your stack

Usage Intelligence measures the swarm itself, so it lights up as a side effect of using the platform rather than through its own warehouse credentials. The more agents and connectors your team runs, the more its dashboards have to say.

Works before you connect anything

The agent starts in 🟡 Evaluation on built-in sample activity — a simulated 30 days of usage — so you can explore every dashboard, pattern, and heatmap before your own history exists. See Verify your setup.

Limits, honestly

• Scope is the Data Workers platform: this is analytics on practitioner interaction with the swarm, not a general product-analytics tool for your applications.
• In 🟡 Evaluation the numbers describe the sample activity, not your team. Real dashboards need real usage history, which accumulates only after your team starts working through the platform.
• Zero-LLM means deterministic, but also literal: it reports what happened, not why. Pair a surprising pattern with the relevant specialist agent for interpretation.
• Anomaly detection is statistical; at high sensitivity expect some false positives, and tune the sensitivity down if the noise outweighs the catches.