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How I AIHow Block’s custom AI agent supercharges every team, from sales to data to engineering
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At a glance
WHAT IT’S REALLY ABOUT
Block’s open-source Goose agent connects tools via MCP to automate workflows
- Block’s Jackie Brosamer and Brad Axen walk through Goose, their open-source, model-agnostic AI agent that gains “arms and legs” by connecting to tools via MCP servers.
- They demo “vibe data analysis” on a messy CSV using Python/Pandas, then automatically generate a shareable HTML dashboard with charts for non-technical stakeholders.
- Next, they connect Goose to a Square MCP to convert the same CSV into a real product catalog, generate a live payment link, and then extend the workflow by vibe-coding a new Mailgun MCP to email that link.
- Along the way, they emphasize organizational change (not just tech), self-serve data access for every team, local-first control and security guardrails, and pragmatic tactics for debugging and iteration with agents.
IDEAS WORTH REMEMBERING
5 ideasAI adoption is primarily an organizational transformation problem.
Jackie argues the winners won’t just adopt AI tools; they’ll change operating norms so teams can reliably use them. Technology scales fast, but human process and culture are the real bottleneck.
Non-engineers can be the strongest drivers of AI value.
Block saw sales pushing hardest early, and Jackie notes non-developers are unusually creative at stitching tools together. Putting agents closer to domain experts unlocks unexpected workflows.
Goose’s power comes from tool connectivity, not just chat.
Block’s definition of an “agent” is an LLM plus a collection of tools it can call to complete tasks. MCP standardizes those connections so the agent can act on real systems (Drive, shell, browsers, internal tools).
Local-first agents can reduce friction and increase control.
Goose runs workflows locally, matching developer realities (files, CLIs, environments) and appealing to users who want end-to-end control. It can also handle long-running tasks you “hand off and come back to.”
Agents turn messy data into usable outputs and shareable artifacts fast.
Jackie drops an “ugly” CSV into Goose, which finds the file, handles Python environment issues, runs Pandas analysis, and generates insights plus recommendations. Goose then creates a simple HTML/Plotly report to share (or publish internally via an MCP).
WORDS WORTH SAVING
5 quotesYou tell it what you need it to do by connecting it to different capabilities, and it can just solve any problem.
— Brad Axen
The winners… [are] who not just leans into technology, but even more than that, leans into the organizational transformation… humans don't go exponentially so well.
— Jackie Brosamer
One of the really underappreciated things about LLMs is how much they function as data duct tape.
— Jackie Brosamer
[MCP is] the arms and legs for the model. This is how the model goes and interacts with the real world.
— Brad Axen
Find the thing that you don't like doing, and automate that.
— Brad Axen
High quality AI-generated summary created from speaker-labeled transcript.
