> ## Documentation Index
> Fetch the complete documentation index at: https://docs.akua.dev/llms.txt
> Use this file to discover all available pages before exploring further.
# Dashboard Generation
> Use AI agents to generate and iterate on custom dashboards and snippets
[Custom dashboards](/dashboards/custom-dashboards) are powered by JavaScript snippets that query the Akua API (the same `platform.request()` sandbox used by [Code Mode](/ai/platform-mcp#why-code-mode)). This makes them a natural fit for AI-driven generation: your agent already knows how to write this code.
## Why use an agent
Snippet code is an **implementation detail, not something you hand-craft**. The JavaScript is simple, repetitive, and easy to regenerate; treat it as disposable. Instead of learning API endpoints and debugging code yourself, describe what you want and let your agent handle it:
| You say | The agent does |
| ------------------------------------------- | --------------------------------------------------------------------------------------------------------------- |
| "Create a dashboard showing cluster health" | Writes snippets for cluster status, KaaS version, and error counts, then creates the dashboard with all widgets |
| "Add a stat widget for total installs" | Creates a snippet that counts installs, adds it to the dashboard |
| "Make the cluster table show regions too" | Updates the snippet code to join cluster and region data |
| "The logs widget is too wide, make it half" | Adjusts the widget's column span from 4 to 2 |
The agent uses the same [Platform MCP](/ai/platform-mcp) that powers all Akua operations. It can create snippets, create dashboards, add widgets, and update layouts, all through the API.
## Workflow
Tell your agent what data matters to you. Be specific about the use case, not the implementation:
*"I want a dashboard that shows how many clusters I have, which ones are healthy, and the last 50 lines of logs from my production install."*
The agent writes the JavaScript for each widget and can test it immediately. Since snippets use the same `platform.request()` sandbox as [Code Mode](/ai/platform-mcp#why-code-mode), the agent test-runs the code via `execute`, sees errors or unexpected results, fixes them, and only saves the working version as a snippet. You never see the debugging, just the finished widget.
Review the result and refine. The agent can update snippet code, change display types, resize widgets, reorder the layout, or add new widgets, all from natural language.
*"Split the cluster table into two widgets: one for KaaS clusters and one for imported."*
The dashboard is saved to your workspace. Anyone on your team can open it, and widgets auto-run on load with no setup required.
## What agents can generate
Agents can create any snippet that the Akua API supports. Common patterns:
* **Resource tables**: list clusters, installs, products, or regions with selected columns
* **KPI stats**: count resources, calculate ratios, show status values
* **Health reports**: multi-step queries that check pod status, fetch logs, and summarize issues
* **Audit views**: cross-reference installs with clusters and products for compliance or inventory
* **Log viewers**: fetch and format container logs from running pods
Since snippets can make up to 50 API calls per execution, agents can build complex multi-step widgets. For example: fetch all installs, query pods for each one, then aggregate the results into a summary table.
## Beyond platform data: application-level dashboards
Here's where it gets powerful. Snippets can use the [exec endpoint](/ai/kubernetes-access#command-execution) to run commands *inside your running containers*. Dashboards aren't limited to Akua platform data; they can reach into your actual applications.
Ask your agent to build widgets like these:
| Widget idea | What the snippet does |
| ---------------------------- | --------------------------------------------------------------------------------------------------------- |
| **Postgres table sizes** | Runs `psql` via exec to query `pg_stat_user_tables`; shows table names, row counts, and disk usage |
| **Redis memory breakdown** | Runs `redis-cli INFO memory` inside the Redis pod; displays used memory, peak memory, fragmentation ratio |
| **MongoDB collection stats** | Runs `mongosh --eval` to list collections with document counts and storage sizes |
| **Application health check** | Hits an internal `/healthz` or `/metrics` endpoint via `curl` inside the pod |
| **Queue depth** | Queries RabbitMQ or Celery via CLI to show pending/processing/failed job counts |
| **Nginx access stats** | Parses recent access logs with `awk` to show top endpoints, status code distribution, and request rates |
| **SSL certificate expiry** | Runs `openssl` to check certificate dates across services; flags anything expiring soon |
| **Disk usage per PVC** | Runs `df -h` inside pods to show persistent volume usage before you run out of space |
These are **live widgets that auto-refresh** when you open the dashboard. A single dashboard can mix platform-level data (cluster health, install status) with application-level data (database sizes, queue depths), giving you a unified view that would normally require stitching together multiple monitoring tools.
You don't need to know `psql` flags, `redis-cli` subcommands, or `mongosh` syntax. Your agent does. Just describe the data you want: *"Show me which Postgres tables are largest and whether any need vacuuming."*
## Tips
* **Let the agent debug for you**: if a widget shows an error, paste it into the conversation. The agent can test-run the snippet code via Code Mode, see exactly what failed, fix it, and update the snippet, all without you touching JavaScript.
* **Start broad, then refine**: ask for a general dashboard first, then tweak individual widgets.
* **Don't optimize the JavaScript**: if a snippet works, it's good enough. Regenerate rather than debug.
* **Use descriptive names**: tell the agent to name snippets clearly (e.g. "Cluster Health by Region" not "query1") so they're easy to reuse across dashboards.
* **Combine with Code Mode**: use [Code Mode](/ai/code-mode-in-action) for one-off investigations, then save the useful queries as snippets for ongoing monitoring.
## Related topics
Manual dashboard and snippet creation.
How the same sandbox powers AI agent operations.
Connect AI agents to Akua.