AI Engineering and Building Systems: Reflections on a Month of AI Engineering with goose by Block

Source: Dev.to

Reflections on a Month of AI Engineering with goose

A month‑long engineering journey powered by goose (by Block), MCP, Anthropic’s Sonnet 4.5, MediaPipe (Google), accessibility‑driven UI design, and a comprehensive stack of modern AI tooling.
Seventeen full‑stack applications later, here’s what I learned and why this workflow fundamentally transformed the way I build.

What goose Is and Why It Transformed My Workflow

• goose Desktop – A visual IDE that lets you prototype, debug, and iterate on AI‑augmented applications without leaving the UI.
• goose CLI – Command‑line tools for scripting, automation, and CI/CD integration.
• YAML Recipes – Declarative, version‑controlled configurations that describe data pipelines, model calls, and UI components.
• MCP Auto Visualizers – Automatic generation of interactive visualizations from model outputs, speeding up insight discovery.
• Anthropic’s Sonnet 4.5 + Goose API Integration – Seamless, low‑latency calls to a state‑of‑the‑art LLM, with built‑in prompt‑management and response handling.

Why Goose Is a Game‑Changer for AI Engineering

1. Goose Eliminates Fragmentation Entirely

“Goose eliminates that fragmentation entirely.”

The Power of Unified Context

When you can:

• See changes reflected instantly
• Inspect tool outputs without switching windows
• Debug state without adding console logs or restarting processes

you stay in flow—the place where the best engineering happens.

2. MCP: The Protocol That Changes Everything

MCP servers let me:

• Define complex tool chains
• Manage stateful workflows
• Deliver dynamic UIs without the usual overhead

Because Goose is built around MCP, everything just works together seamlessly:

• Tools talk to each other
• State persists correctly
• Visualizations appear automatically

The protocol eliminates an entire category of integration problems that usually consume hours of debugging time.

3. Speed Without Sacrificing Quality

• YAML recipes automate multi‑step workflows, managing complexity intelligently.
• Auto‑visualizers render data structures instantly, giving clearer insight without skipping understanding.
• Sonnet 4.5 helps refactor code or generate documentation, augmenting—not replacing—engineering judgment.

4. A Development Experience That Feels Alive

I’ve used many development environments over the years. Some are powerful but clunky; others are elegant but limited. Goose is the first tool that is both powerful and delightful:

• Handles complexity gracefully while staying out of your way
• Gives you control without overwhelming configuration
• Is opinionated about the right things and flexible about everything else

5. The Future of Development Is Here

This month proved we’re at an inflection point. The tools, protocols, and AI models exist; Goose brings them together in a way that feels inevitable in hindsight but revolutionary in practice.

The Agentic AI Foundation (AAIF)

The new agentic group created by major tech companies—including OpenAI, Block, and Anthropic—with support from other platinum members such as Google, Microsoft, Amazon Web Services (AWS), Bloomberg, and Cloudflare is called the Agentic AI Foundation (AAIF).
The foundation launched under the umbrella of the Linux Foundation in December 2025.

If you’re serious about AI engineering, want to build faster without sacrificing quality, and crave a development experience that feels empowering rather than exhausting, goose is a wonderful addition. It’s not just a tool; it’s a glimpse into how we’ll all be building software in the very near future with these amazing technologies.

Linux Foundation press release on AAIF

Anthropic’s Sonnet 4.5 and Goose API Integration

• Used Sonnet 4.5 as an AI engineering partner for:
  • Code generation
  • Architecture decisions
  • Technical documentation

MediaPipe: Building Spatial Intelligence into Applications

Why MediaPipe Matters

• Accessibility: No extensive ML expertise or server‑side infrastructure required.
• Real‑time processing: Handles complexity, letting developers focus on meaningful interactions.

Real‑World Applications

• Gesture‑based navigation
• Hands‑free controls for accessibility
• Spatial UI interactions
• Pose‑based fitness‑tracking interfaces

These prototypes demonstrate how spatial intelligence can make interfaces more inclusive and intuitive.

How This Work Applies to Real‑World Engineering

“goose” expanded my workflow, giving me a faster, more expressive way to build the same caliber of systems that real engineering teams depend on daily.

A Month of Technologies, Patterns, and Systems

Across seventeen projects I worked extensively with:

• MCP servers – custom tool layers, state engines, and rendering pipelines
• Semantic HTML, WCAG, and ARIA accessibility patterns for inclusive design
• MediaPipe for spatial intelligence
• Anthropic Sonnet 4.5 for AI‑augmented engineering
• goose Desktop & CLI for a unified development experience
• YAML recipes for automated, reproducible workflows

Final Thought

The convergence of goose, MCP, Sonnet 4.5, MediaPipe, and modern accessibility practices shows that AI‑augmented engineering is no longer a distant vision—it’s happening right now. Embrace these tools, and the future of software development will feel both powerful and human‑centered.

Overview

• Glassmorphism, gradients, and motion‑aware UI – modern, polished interfaces
• YAML automation recipes – workflow orchestration made simple
• MediaPipe & spatial intelligence – multimodal interaction capabilities
• JavaScript & TypeScript full‑stack patterns – robust application architecture
• Organizational systems – architecture diagrams, planning documents, and reusable pattern libraries

Each project builds on the foundation of the last. The workflow becomes progressively more structured, expressive, and enjoyable with every iteration.

Accessibility: Building for Everyone

Why Accessibility Matters to Me

The beautiful thing about accessible design is that it makes applications better for everyone.

• Clear semantic HTML improves SEO and code maintainability.
• Proper ARIA labels enhance usability across all devices.
• Keyboard navigation benefits power users.
• High‑contrast ratios help people in bright sunlight or low‑light environments.

“When we design for accessibility, we design for flexibility and resilience.”

WCAG and ARIA in Practice

• Semantic HTML – use proper heading hierarchies, landmark regions, and meaningful element choices.
• ARIA labels & roles – provide context for screen readers and assistive technologies.
• Keyboard navigation patterns – ensure every interactive element is reachable and operable without a mouse.
• Color contrast – meet WCAG AA standards (4.5:1 for normal text, 3:1 for large text).
• Focus indicators – keep them clearly visible and never remove them without an alternative.
• Alt text – write meaningful descriptions; use empty alt="" only for decorative images.
• Error identification & suggestions – help users understand and correct mistakes.
• Responsive layouts – work across screen sizes and zoom levels without breaking functionality.

The Real‑World Impact

“Every time I write semantic HTML, add an ARIA label, or test keyboard navigation, I’m choosing inclusion over exclusion. That matters deeply to me.”

Moving Forward

Closing Reflection

What’s Next for This AI Engineer

This month of intensive engineering was a foundation, not a finish line. Seventeen full‑stack systems later, I’m stepping into the next phase of my AI‑engineering journey with unprecedented clarity and structural understanding.

The projects I built during this challenge were deliberately diverse:

• Full‑stack applications deployed
• MCP servers
• UI engines
• Automation workflows
• Spatial‑intelligence prototypes
• Accessibility‑driven interfaces
• Organizational systems that now fundamentally shape how I approach software development

The next step is taking this work beyond the challenge and into the broader AI‑engineering community. I’ll be attending the Microsoft AI Tour conference, continuing to refine and evolve my workflow, and exploring how these patterns scale into larger, production‑grade systems.

My goals

• Push the boundaries of AI engineering and AI‑assisted development
• Deepen MCP integrations
• Enrich UI architectures
• Advance spatial and multimodal experiments
• Build tools that feel both cohesive and genuinely human‑centered

This challenge may be complete, but my AI‑engineering journey will continue. I couldn’t be more excited about what comes next.

This post is part of my AI engineering journey. Follow along for more AI Engineering Building with Eri!