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Artifacts are the dashboards, reports, comparisons, scorecards, diagrams, and explicitly saved generated files CloudThinker agents create from your connected cloud data. Generated files appear in the conversation first. Click Save to artifacts to add one to the Files gallery. Describe the insight you need in plain language, and the agent builds a data-backed visual from your connections. Charts are components of dashboards and reports, not standalone Artifact types.

How it works

  1. Ask — send a prompt using the CloudThinker Language syntax: @agent #tool instruction.
  2. Gather — the agent queries live data across your connections: Cost Explorer, CloudWatch, databases, and more.
  3. Generate — the agent assembles an Artifact with charts, tables, and a written summary.
  4. Save, download, or rerun — save generated files to Files, download supported Artifacts as PDFs from their PDF URLs, or schedule the prompt to run again.
A scheduled task reruns its prompt in a new scheduled conversation. It may create a fresh Artifact; it does not refresh an existing Artifact in place.
AWS cost dashboard with spending trends and cost drivers

AWS cost dashboard with spending trends and cost drivers

What you can do

Artifact types

Charts are components of dashboards and reports. The #chart tag is not a standalone Artifact type.

Example prompts

Start with a one-line request — agents pick sensible defaults for scope and time range:

Cost analysis dashboard

Add structure to the instruction when you need specific breakdowns:

Cross-domain dashboard

Ask Anna to correlate data that lives in different systems:
Database and infrastructure correlation dashboard showing performance and cost metrics

Database and infrastructure correlation dashboard

Charts in dashboards and reports

Ask for a chart as part of a dashboard or report:
Aurora query performance time-series chart with p50, p95, p99 latency metrics

Aurora query performance time-series chart

Reusable templates

Save parameterized prompts as templates for recurring investigations, then fill in the {variables} on each run:
For example, run database_performance_review with cluster_id=production-aurora-cluster, time_period="past 7 days", comparison_period="previous 30 days", and latency_threshold=200.
Performance review dashboard template for Aurora cluster analysis

Performance review dashboard template

Spend and forecast

Dive deeper into spend trends, forecasts, and cost attribution analysis

Infrastructure Analytics

Correlate performance, cost, and reliability signals across connected clouds

CloudThinker Language

Master the full @agent #tool syntax for building effective prompts

Tasks

Rerun prompts in new scheduled conversations