How to Build AI Agent Dashboards

Create tables for: agent_runs (one row per task execution, with agent_id, start_time, end_time, status, token_count, cost, error_message), agent_alerts (anomalies and threshold violations), and agent_config (current configuration, model, prompt version).

Every agent writes to agent_runs after completing a task. Include: input summary (truncated), output summary, tools called, duration in milliseconds, total tokens used, estimated cost, and final status (success, failure, escalated). This table is the foundation for every dashboard view.

The overview shows: total tasks processed today, success rate (percentage), average response time, total cost today, active alerts count, and a mini chart showing task volume over the last 7 days. Each indicator should be color-coded — green for healthy, yellow for attention needed, red for critical.

Design this panel to answer one question in under 5 seconds: 'Is everything working?' If someone glances at the overview and sees all green, they move on. If they see yellow or red, they drill into the detail view.

Each agent gets its own detail page showing: task completion rate over time, average latency trend, error breakdown by type, cost per task trend, and recent task history with expand-to-view-details. Include the agent's current configuration — model, prompt version, tools enabled — so you can correlate performance changes with config changes.

Add comparison capabilities: this agent's performance this week versus last week, or Agent A versus Agent B on the same task type. These comparisons surface optimization opportunities that raw numbers alone don't reveal.

Build a search interface over the agent_runs table. Filter by: agent, date range, status (success/failure/escalated), task type, and cost range. Each task row expands to show: full input, full output, tool calls made, reasoning steps (if logged), and any errors encountered.

This view is essential for debugging. When a customer reports a wrong response, you search by the customer identifier, find the exact task, and trace through the agent's reasoning to identify where it went wrong. Without searchable logs, debugging becomes guessing.

Display active alerts in a dedicated panel, sorted by severity and recency. Each alert shows: what triggered it, which agent, when it occurred, and a direct link to the relevant task log entry. Include acknowledge and resolve buttons so the team can manage alerts without switching to another tool.

Connect the alert feed to the same notification channels (Slack, Telegram) so the team gets notified whether they're looking at the dashboard or not. The dashboard provides context; the notification provides urgency.