Notes: female AI voice, conversational pace, pauses marked [PAUSE] Target: 90 seconds narration, 3 minutes total with demo running
This is EdgePilot.
Its two AI agents using the StackQL MCP server interacting with Cloudflare and Confluent from a single SQL interface.
[PAUSE - show repo in editor or terminal]
Before we run the agents, we provisioned the Kafka topic using stackql-deploy. One command. No state file. If the topic already exists, it does nothing.
[PAUSE - show terminal with stackql-deploy output, or just skip to demo.py]
Now let's run the demo.
[RUN python demo.py - PAUSE 3-4 seconds while MCP server starts and tools load]
The MCP server is live. Those are StackQL tools - Cloudflare, Confluent, loaded from a single local server.
[PAUSE - recon agent's first tool call appears]
The recon agent just ran a SELECT against live Cloudflare analytics. Not a cached state file. The actual API, right now.
[PAUSE - second tool call, rate limit query]
Now it's reading the current rate limit config.
[PAUSE - recon result prints]
Traffic is elevated. Twenty-three threats in the last thirty minutes. The recon agent hands that off.
[PAUSE - action agent starts, first SELECT visible]
The action agent reads the same rate limit config to confirm what it's changing.
[PAUSE - UPDATE visible]
Tightens the threshold. One SQL UPDATE against Cloudflare.
[PAUSE - INSERT into Confluent visible]
And now that INSERT is going to a completely different cloud - Confluent Kafka. Same MCP server. Same SQL interface.
[PAUSE - "done" prints]
That's it. Two agents. Four SQL statements. Cloudflare and Confluent from one query layer.
Infrastructure as data.
[END]
Claude Desktop version (optional follow-on, 30 seconds):
The same MCP server is wired into Claude Desktop. So you can run this interactively - ask it to check your Cloudflare zone, tighten a rule, log the decision - and it uses the exact same StackQL tools, the exact same SQL, no code required.
[SHOW Claude Desktop with a natural language prompt executing]