How do you deploy AI agents to 12,000 employees in just 8 weeks? How do you do it safely? Angie Jones, VP of Engineering for AI Tools and Enablement at Block, joins the show to share exactly how her team pulled it off.

Block (the company behind Square and Cash App) became an early adopter of Model Context Protocol (MCP) and built Goose, their open-source AI agent that's now a reference implementation for the Agentic AI Foundation. Angie shares the challenges they faced, the security guardrails they built, and why letting employees choose their own models was critical to adoption.

We also dive into vibe coding (including Angie's experience watching Jack Dorsey vibe code a feature in 2 hours), how non-engineers are building their own tools, and what MCP unlocks when you connect multiple systems together.

Chapters:

00:00 Introduction

02:02 How Block deployed AI agents to 12,000 employees

05:04 Challenges with MCP adoption and security at scale

07:10 Why Block supports multiple AI models (Claude, GPT, Gemini)

08:40 Open source models and local LLM usage

09:58 Measuring velocity gains across the organization

10:49 Vibe coding: Benefits, risks & Jack Dorsey's 2-hour feature build

13:46 Block's contributions to the MCP protocol

14:38 MCP in action: Incident management + GitHub workflow demo

15:52 Addressing MCP criticism and security concerns

18:41 The Agentic AI Foundation announcement (Block, Anthropic, OpenAI, Google, Microsoft)

21:46 AI democratization: Non-engineers building MCP servers

24:11 How to get started with MCP and prompting tips

25:42 Security guardrails for enterprise AI deployment

29:25 Tool annotations and human-in-the-loop controls

30:22 OAuth and authentication in Goose

32:11 Use cases: Engineering, data analysis, fraud detection

35:22 Goose in Slack: Bug detection and PR creation in 5 minutes

38:05 Goose vs Claude Code: Open source, model-agnostic philosophy

38:17 Live Demo: Council of Minds MCP server (9-persona debate)

45:52 What's next for Goose: IDE support, ACP, and the $100K contributor grant

47:57 Where to get started with Goose

Connect with Angie on LinkedIn: https://www.linkedin.com/in/angiejones/

Angie's Website: https://angiejones.tech/

Follow Angie on X: https://x.com/techgirl1908

Goose GitHub: https://github.com/block/goose

Connect with Conor on LinkedIn: https://www.linkedin.com/in/conorbronsdon/

Follow Conor on X: https://x.com/conorbronsdon

Modular: https://www.modular.com/

Presented By: Galileo AI

Download Galileo's Mastering Multi-Agent Systems for free here: https://galileo.ai/mastering-multi-agent-systems

Topics Covered:

- How Block deployed Goose to all 12,000 employees

- Building enterprise security guardrails for AI agents

- Model Context Protocol (MCP) deep dive

- Vibe coding benefits and risks

- The Agentic AI Foundation (Block, Anthropic, OpenAI, Google, Microsoft, AWS)

- MCP sampling and the Council of Minds demo

- OAuth authentication for MCP servers

- Goose vs Claude Code and other AI coding tools

- Non-engineers building AI tools

- Fraud detection with AI agents

- Goose in Slack for real-time bug fixing

Google DeepMind is reshaping the AI landscape with an unprecedented wave of releases—from Gemini 3 to robotics and even data centers in space.

Paige Bailey, AI Developer Relations Lead at Google DeepMind, joins us to break down the full Google AI ecosystem. From her unique journey as a geophysicist-turned-AI-leader who helped ship GitHub Copilot, to now running developer experience for DeepMind's entire platform, Paige offers an insider's view of how Google is thinking about the future of AI.

The conversation covers the practical differences between Gemini 3 Pro and Flash, when to use the open-source Gemma models, and how tools like Anti-Gravity IDE, Jules, and Gemini CLI fit into developer workflows. Paige also demonstrates Space Math Academy—a gamified NASA curriculum she built using AI Studio, Colab, and Anti-Gravity—showing how modern AI tools enable rapid prototyping.

The discussion then ventures into AI's physical frontier: robotics powered by Gemini on Raspberry Pi, Google's robotics trusted tester program, and the ambitious Project Suncatcher exploring data centers in space.

00:00 Introduction

01:30 Paige's Background & Connection to Modular

02:29 Gemini Integration Across Google Products

03:04 Jules, Gemini CLI & Anti-Gravity IDE Overview

03:48 Gemini 3 Flash vs Pro: Live Demo & Pricing

06:10 Choosing the Right Gemini Model

09:42 Google's Hardware Advantage: TPUs & JAX

10:16 TensorFlow History & Evolution to JAX

11:45 NeurIPS 2025 & Google's Research Culture

14:40 Google Brain to DeepMind: The Merger Story

15:24 Palm II to Gemini: Scaling from 40 People

18:42 Gemma Open Source Models

20:46 Anti-Gravity IDE Deep Dive

23:53 MCP Protocol & Chrome DevTools Integration

26:57 Gemini CLI in Google Colab

28:00 Image Generation & AI Studio Traffic Spikes

28:46 Space Math Academy: Gamified NASA Curriculum

31:31 Vibe Coding: Building with AI Studio & Anti-Gravity

36:02 AI From Bits to Atoms: The Robotics Frontier

36:40 Stanford Puppers: Gemini on Raspberry Pi Robots

38:35 Google's Robotics Trusted Tester Program

40:59 AI in Scientific Research & Automation

42:25 Project Suncatcher: Data Centers in Space

45:00 Sustainable AI Infrastructure

47:14 Non-Dystopian Sci-Fi Futures

47:48 Closing Thoughts & Resources

- Connect with Paige on LinkedIn: https://www.linkedin.com/in/dynamicwebpaige/

- Follow Paige on X: https://x.com/DynamicWebPaige

- Paige's Website: https://webpaige.dev/

- Google DeepMind: https://deepmind.google/

- AI Studio: https://ai.google.dev

Connect with our host Conor Bronsdon:

- Substack – https://conorbronsdon.substack.com/

- LinkedIn https://www.linkedin.com/in/conorbronsdon/

Presented By: Galileo.ai

Download Galileo's Mastering Multi-Agent Systems for free here!: https://galileo.ai/mastering-multi-agent-systems

Topics Covered:

- Gemini 3 Pro vs Flash comparison (pricing, speed, capabilities)

- When to use Gemma open-source models

- Anti-Gravity IDE, Jules, and Gemini CLI workflows

- Google's TPU hardware advantage

- History of TensorFlow, JAX, and Google Brain

- Space Math Academy demo (gamified education)

- AI-powered robotics (Stanford Puppers on Raspberry Pi)

- Project Suncatcher (orbital data centers)

You've heard of evaluations—but eval engineering is the difference between AI that ships and AI that's stuck in prototype.

Most teams still treat evals like unit tests: write them once, check a box, move on. But when you're deploying agents that make real decisions, touch real customers, and cost real money, those one-time tests don't cut it. The companies actually shipping production AI at scale have figured out something different—they've turned evaluations into infrastructure, into IP, into the layer where domain expertise becomes executable governance.

Vikram Chatterji, CEO and Co-founder of Galileo, returns to Chain of Thought to break down eval engineering: what it is, why it's becoming a dedicated discipline, and what it takes to actually make it work. Vikram shares why generic evals are plateauing, how continuous learning loops drive accuracy, and why he predicts "eval engineer" will become as common a role as "prompt engineer" once was.

In this conversation, Conor and Vikram explore:

• Why treating evals as infrastructure—not checkboxes—separates production AI from prototypes
• The plateau problem: why generic LLM-as-a-judge metrics can't break 90% accuracy
• How continuous human feedback loops improve eval precision over time
• The emerging "eval engineer" role and what the job actually looks like
• Why 60-70% of AI engineers' time is already spent on evals
• What multi-agent systems mean for the future of evaluation
• Vikram's framework for baking trust AND control into agentic applications

Plus: Conor shares news about his move to Modular and what it means for Chain of Thought going forward.

Chapters:00:00 – Introduction: Why Evals Are Becoming IP01:37 – What Is Eval Engineering?04:24 – The Eval Engineering Course for Developers05:24 – Generic Evals Are Plateauing08:21 – Continuous Learning and Human Feedback11:01 – Human Feedback Loops and Eval Calibration13:37 – The Emerging Eval Engineer Role16:15 – What Production AI Teams Actually Spend Time On18:52 – Customer Impact and Lessons Learned24:28 – Multi-Agent Systems and the Future of Evals30:27 – MCP, A2A Protocols, and Agent Authentication33:23 – The Eval Engineer Role: Product-Minded + Technical34:53 – Final Thoughts: Trust, Control, and What's Next

Connect with Conor Bronsdon:Substack – https://conorbronsdon.substack.com/LinkedIn – https://www.linkedin.com/in/conorbronsdon/X (Twitter) – https://x.com/ConorBronsdon

Learn more about Eval Engineering:⁠https://galileo.ai/evalengineering⁠

Connect with Vikram Chatterji:LinkedIn – ⁠https://www.linkedin.com/in/vikram-chatterji/⁠