How Block’s custom AI agent supercharges every team, from sales to data to engineering

VP of engineering Jackie Brosamer and principal engineer Brad Axen join me to demo Goose, Block’s open-source AI agent that runs locally, plugs into your existing tools through model context protocol (MCP) servers, and peels away the rote parts of work so people can focus on insight and impact. This episode is packed with in-depth demos: starting with a messy farm-stand sales CSV, Goose analyzes the data, builds visualizations, and generates a shareable HTML report. We then spin up an MCP that lets Goose talk to Square’s dashboard for inventory management, vibe code an email MCP that can send payment links automatically, and unpack how environment setup, debugging, and tool orchestration get handled behind the scenes. *What you’ll learn:* 1. A practical, repeatable workflow for turning any working script or function into a custom MCP—and exposing it to natural-language control 2. How to transform messy CSVs into visualizations, HTML reports, and actionable business insights without needing a data science background 3. Ways to hook Goose into live business systems (e.g. Square inventory, payments) so analysis flows directly into operational action 4. The thinking behind Block’s decision to open-source Goose 5. Lessons from Block’s bottom-up meets top-down adoption model 6. Why organizational transformation, not just picking the right LLM, will separate AI winners from laggards over the next few years 7. How to scale an internal MCP catalog 8. The organizational transformation required to fully leverage AI capabilities *Brought to you by:* CodeRabbit—Cut code review time and bugs in half. Instantly: https://coderabbit.link/howiai Lenny’s List—Hands-on AI education curated by Lenny and Claire: https://maven.com/lenny *Where to find Jackie Brosamer:* LinkedIn: https://www.linkedin.com/in/jbrosamer/ *Where to find Brad Axen:* LinkedIn: https://www.linkedin.com/in/bradleyaxen/ *Where to find Claire Vo:* ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo *In this episode, we cover:* (00:00) Introduction to Goose and its data analysis capabilities (02:27) How Block embraced AI across the organization (04:48) What Goose is and why Block open-sourced it (07:45) Demo: Analyzing farm-stand sales data with Goose (12:18) Creating shareable HTML reports from data analysis (14:15) Model context protocols (MCPs) that Goose uses (18:56) Demo: Using Square MCP to create a product catalog (23:35) Creating payment links from analyzed data (26:30) Demo: Building a custom email MCP (31:18) Testing the new email MCP with Goose (36:09) Debugging and fixing MCP code errors (38:44) Connecting workflows: sending payment links via email (41:30) Lightning round and final thoughts *Tools referenced:* • Goose: https://block.github.io/goose/ • Pandas: https://pandas.pydata.org/ • Plotly: https://plotly.com/ • Python: https://www.python.org/ • ChatGPT: https://chat.openai.com/ • Claude: https://claude.ai/ • Cursor: https://www.cursor.com/ • Mailgun: https://www.mailgun.com/ *Other references:* • Block: https://block.com/ • Model context protocol (MCP): https://www.anthropic.com/news/model-context-protocol • GitHub: https://github.com/ _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email jordan@penname.co._

Brad AxenguestClaire VohostJackie Brosamerguest

Jul 28, 202546mWatch on YouTube ↗

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 ideas

AI 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 quotes

You 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

Bottoms-up + top-down AI adoption at BlockGoose as an open-source, model-agnostic agentMCP as the tool/integration protocol (“arms and legs”)Local-first execution and environment/debug automationVibe data analysis with Pandas + automated recommendationsFrom messy inputs to product catalog + payment links (Square)Vibe-coding new MCPs (Mailgun) + security/permission gates

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