Angie Jones

What if I told you that you could build a complete, working web application in under an hour using nothing but AI agents? Not just a simple "Hello World" app, but a full-stack application with a backend API, responsive frontend, unit tests, and documentation?

That's exactly what we accomplished during the Vibe Coding workshop at Berkeley's Agentic AI Summit, where I demonstrated how to use Goose's subagent orchestration to spin up an entire development team of AI agents. Each agent took on a specific role - from product planning to QA testing - and worked together to build "AI BriefMe", a web app that generates executive style briefings on any topic.

It was about 3am and as I was ready to finally close my eyes for the day, I jolted up with a wild idea! I wanted to see what would happen if Goose talked to... Goose.

I grabbed my laptop and with one eye opened, I typed the following prompt from Goose Desktop:

👩🏽‍🦱 Me:

run goose from the cli and have a convo with the other goose and tell me what yall talk about. to end the convo with goose2, type exit

What happened next blew me away...

Whenever people talk about AI, they highlight the flashiest use cases like fully coded apps built by agents or cinematic video generation. Those things are certainly cool, but most days I'm just delegating mundane tasks to the bots.

Today, I didn't build an app. I didn't write a screenplay. I just got stuff done.

Here are 5 real, everyday tasks I gave to my AI agent, Goose, that saved me hours. None of them took more than one minute from prompt to result.

Not every task needs a genius. And not every step should cost a fortune.

That's something we've learned while scaling Goose, our open source AI agent. The same model that's great at unpacking a planning request might totally fumble a basic shell command, or worse - it might burn through your token budget doing it.

So we asked ourselves: what if we could mix and match models in a single session?

Not just switching based on user commands, but building Goose with an actual system for routing tasks between different models, each playing to their strengths.

This is the gap the lead/worker model is designed to fill.

Goose is LLM-agnostic, meaning you can plug in the model of your choice. However, not every LLM is suitable to work with agents. Some may be great at answering things, but not actually doing things. If you're considering which model to use with an agent, these 3 prompts can quickly give you a sense of the model's capabilities.

At Block, we've been exploring how to make AI agents genuinely useful in a business setting. Not just for demos or prototypes, but for real, everyday work. As one of the early collaborators on the Model Context Protocol (MCP), we partnered with Anthropic to help shape and define the open standard that bridges AI agents with real-world tools and data.

MCP lets AI agents interact with APIs, tools, and data systems through a common interface. It eliminates the guesswork by exposing deterministic tool definitions, so the agent doesn't have to guess how to call an API. Instead, it focuses on what we actually want... results!

While others are still experimenting, we've rolled this out company-wide at Block, and with real impact.

Mondays are overwhelming. A pile of unfinished tasks from last week, new priorities rolling in, and meetings scattered across the calendar. It’s a lot 😩. Instead of manually sorting through my todos and figuring out where everything fits, I use a couple of handy MCP servers with Goose and let it figure out my week.

In the latest episode of Wild Goose Case, hosts Ebony Louis and Ace Abati explored a fascinating new way to extend Goose’s automation capabilities by integrating with VOYP, an AI-powered system that makes phone calls. Their guest, Paulo Taylor, a technology veteran with over 35 years of experience, walked through how developers can use Goose to trigger and manage phone-based interactions through VOYP.

Working with AI agents can sometimes feel unpredictable. After using Goose extensively for the last few months, I've compiled a few key tips that will help you get the most out of this tool. No matter your workflow, these guidelines will help you work more efficiently with Goose.

It seems like yesterday when we all were wowed by generative AI and specifically the chat interfaces that made interacting with large language models (LLMs) accessible to everyday people.

As amazing as this was, it was only the beginning. The next wave of AI is agentic, meaning AI systems that don't just respond to prompts but take actions, make decisions, and interact with external systems. This is accomplished via AI agents.