The interesting part: the Gauntlet Loop

I’m also experimenting with a repo-level evaluation loop:

Builder → deterministic evidence → blind critic → one gap → Builder

The critic doesn't inspect the implementation directly. It only receives the generated evidence.

That makes the evaluation much closer to:

“Can the system prove that it works?”

rather than:

“Does the implementation look correct?”

The goal is to make agent-assisted development more measurable and less dependent on subjective code review.

The architecture looks roughly like this:

Ground Truth → Rust Workspace → CLI → CES REST API

with the Gauntlet Loop continuously testing the implementation against the contract.

I'm particularly interested in feedback from people working with Rust CLIs, agent tooling, Google Cloud/CX Agent Studio, API compatibility testing, or deterministic evaluation systems.

What would you add to a machine-first CLI like this?

I spent the last few weeks building ExplorePune — a curated city guide where every place gets its own hand-built, interactive 3D scene.

No GLBs. No downloaded 3D models.

Everything is procedurally generated with Three.js at runtime, including interactive hotspots mapped to the editorial feature list.

🏙️ 15 Places · 5 Categories

Forts & Palaces

Shaniwar Wada

Lal Mahal

Sinhagad Fort

Aga Khan Palace

Temples & Spiritual

Dagdusheth Halwai Ganapati

Pataleshwar Cave

Parvati Hill

Osho Meditation Resort

Nature & Gardens

Pune-Okayama Friendship Garden

Saras Baug

Empress Garden

Museums & Culture

Raja Dinkar Kelkar Museum

National War Memorial, Southern Command

Lakes & Hills

Vetal Tekdi

Khadakwasla Dam

🛠️ Tech Stack

Next.js 16 — App Router

React 19

Three.js

Tailwind CSS 4

Base UI

Google Maps with @vis.gl/react-google-maps

Vitest for layout and anchor-helper testing

⚙️ Some Engineering Decisions

WebGL stays completely out of the server render using next/dynamic with ssr: false.

Camera anchors, palettes, and feature ordering are implemented as pure, testable helpers.

Every location includes practical information: timings, entry fees, best seasons, audience tags, estimated duration, and ratings.

Full directory with filtering by category, season, audience, and fee.

Multiple views: Grid · List · Map

Community layer with local reviews and a suggest-a-place workflow.

Admin dashboard for approving and moderating submissions.

The goal was to make a city guide that feels less like a static directory and more like an interactive digital exploration of Pune.

I spent the last few weeks building ExplorePune — a curated city guide where every place gets its own hand-built, interactive 3D scene.

No GLBs. No downloaded 3D models.

Everything is procedurally generated with Three.js at runtime, including interactive hotspots mapped to the editorial feature list.

🏙️ 15 Places · 5 Categories

Forts & Palaces

• Shaniwar Wada
• Lal Mahal
• Sinhagad Fort
• Aga Khan Palace

Temples & Spiritual

• Dagdusheth Halwai Ganapati
• Pataleshwar Cave
• Parvati Hill
• Osho Meditation Resort

Nature & Gardens

• Pune-Okayama Friendship Garden
• Saras Baug
• Empress Garden

Museums & Culture

• Raja Dinkar Kelkar Museum
• National War Memorial, Southern Command

Lakes & Hills

• Vetal Tekdi
• Khadakwasla Dam

🛠️ Tech Stack

• Next.js 16 — App Router
• React 19
• Three.js
• Tailwind CSS 4
• Base UI
• Google Maps with @vis.gl/react-google-maps
• Vitest for layout and anchor-helper testing

⚙️ Some Engineering Decisions

• WebGL stays completely out of the server render using next/dynamic with ssr: false.
• Camera anchors, palettes, and feature ordering are implemented as pure, testable helpers.
• Every location includes practical information: timings, entry fees, best seasons, audience tags, estimated duration, and ratings.
• Full directory with filtering by category, season, audience, and fee.
• Multiple views: Grid · List · Map
• Community layer with local reviews and a suggest-a-place workflow.
• Admin dashboard for approving and moderating submissions.

The goal was to make a city guide that feels less like a static directory and more like an interactive digital exploration of Pune.

I came across this really interesting interactive experience for Shaniwar Wada, Pune.

Instead of simply looking at photos or reading historical information, the experience lets you explore the fort in a more visual and interactive way — including locations such as:

🏰 Delhi Darwaza 🧱 Ramparts & Bastions 💧 Hazari Karanje 🏛️ Palace Foundations 🥁 Nagarkhana 🌙 Lawns & Light & Sound Show

What I particularly like is the combination of historical storytelling + 3D visualization + interactive exploration.

This kind of approach could make historical sites much more accessible to people who can't visit them physically.

What do you think — could interactive 3D/AI experiences become a better way to explore India's historical places?

#Pune #ShaniwarWada #Maharashtra #History #3D #Technology

Sharing reel 58 from my free Dialogflow CX / CX Agent Studio interview prep series.

Question: What is CX Agent Studio?

Summary: CX Agent Studio, also known as CXAS, is Google Cloud's enterprise platform for building, deploying and managing conversational AI agents at scale using a low-code visual environment.

Key concepts covered:

• CX Agent Studio
• Key Capabilities
• Enterprise Governance

Full video in comments. Hope this helps anyone interviewing for CCAI / Conversational AI roles!

Hey everyone,

Breaking down the Native CXAS VS Code Extension feature by feature for anyone building on CX Agent Studio.

This wraps the entire cxas CLI in a native editor experience (VS Code + Antigravity IDE). Here is what each feature actually does:

CXAS Sidebar Tree
Lists all your resources — agents, tools, callbacks, variables, evals. Right-click context menus let you scaffold new resources without opening a terminal.

Command Palette Integration
Every core cxas operation (Push, Pull, Lint, Run Evals) is available under the CXAS: prefix in the command palette. Full keyboard workflow.

Inline Diagnostics
The linter runs as you type. Squiggles appear in real time and feed into the Problems panel. You catch schema and config issues before you ever try to push.

Cmd+Click Navigation
If your instruction files reference {@TOOL: my_tool} or a variable, Cmd+Click jumps you directly to the source definition. Huge time saver when working across large agent configs.

Embedded Live Chat
After pushing your agent, you can test it in a native webview inside the editor itself. No switching to a browser, no separate terminal session.

Interactive Eval Runner
Run individual test cases or your full eval suite from inside the editor. Results appear in a dedicated report panel.

This eliminates the constant terminal-editor-browser loop that makes CXAS development slow.

Happy to answer questions about any of these features or how SCRAPI fits into the larger CXAS development workflow

Most teams I talk to spend a ton of time on instructions and sub-agent structure in CX Agent Studio, but treat tools as an afterthought.

The result? Agents that sound smart but keep failing on actual execution — wrong tool selection, hallucinated parameters, bloated responses, or brittle multi-step flows.

I just published a detailed post on the tool design patterns that actually move the needle in production:

Key things that made the biggest difference:

• Docstrings are not optional — In Python tools, the docstring becomes the model’s primary understanding of when and how to use the tool. Vague descriptions = unreliable routing.
• Stop chaining granular tools in prompts — Instructing the agent to call check_availability → book_slot → send_confirmation in one turn is fragile. The official best practices even call this out as a bad pattern.
• Use the Tool Wrapper pattern — Create higher-level Python tools that internally handle sequencing, data transformation, and only return the minimal fields the agent actually needs. This has been one of the highest-ROI changes we’ve made.
• Kill return value bloat — Returning full API responses with 30-50 fields is one of the fastest ways to increase hallucinations and token waste. Wrap and curate what the agent sees.
• Voice mode makes bad tool design even more obvious. Inputs need to support natural language (“tomorrow afternoon”) rather than forcing rigid formats.

I shared a before/after example + a short production story where refactoring to wrapper tools significantly improved reliability on complex flows.

Would love to hear from others building in CX Agent Studio:

• Have you run into tool explosion or ambiguous naming issues?
• Are you using Python wrapper tools yet, or still mostly relying on OpenAPI connectors?
• What’s been your biggest pain point with tool reliability?

Happy to discuss patterns that worked (or didn’t) in production.

Master the core architecture of CX Agent Studio. Understand root agents, hierarchical sub-agents, tools, orchestration, and how it differs fundamentally from Dialogflow CX flows and pages. As a Developer who has spent years building production conversational systems on Dialogflow CX and now CX Agent Studio, I can tell you this: the biggest unlock isn't just new features - it's a complete paradigm shift in how we think about building agents. If you've ever wrestled with sprawling flows, page transitions, and intent graphs that become unmaintainable at scale, CX Agent Studio feels like finally getting the right abstraction. It's LLM-native, agentic, and built for the messy, dynamic reality of real customer conversations. In this deep dive (Article 3 in the series), we'll break down the architecture that makes it all work: root agents, sub-agents, tools, instructions, and the visual orchestration layer. By the end, you'll understand why things are structured this way and how to design modular, observable, and maintainable agent systems.

I put together a practical, no-fluff guide on getting started with CX Agent Studio (ces.cloud.google.com). It covers everything from initial project setup and enabling the right APIs, to creating your first agent application, using the “Start with AI” feature, configuring instructions, and testing in the multimodal simulator. What’s inside:

Prerequisites & console access Creating your first root agent (and the 1-2 min provisioning note) How to effectively use Gemini to bootstrap the agent Key parts of the Agent Builder interface Testing tips in the simulator Common first-agent pitfalls (especially vague instructions) Best practices I’ve seen work well

I used a simple Weather Information Assistant as the running example because it’s concrete and easy to extend with tools. Full guide here: [Link to your Medium article] Quick question for the community:

What was the first agent you built in CX Agent Studio? Any pro tips on model selection or structuring instructions that made a big difference? How are you handling migration thoughts from legacy Dialogflow CX?

CX Agent Studio removes the old tradeoff between simple disappointing chatbots and months-long custom projects. Teams can now go from idea to deployed, evaluated, multimodal agents in days — with visual orchestration, strong observability, and production-grade infrastructure. If you’re in CX, conversational design, Google Cloud, or building customer experiences, these should save you time. Have you tried CX Agent Studio yet? Migrating from Dialogflow CX? Any early tips or questions? Drop them in the comments — happy to discuss!

https://medium.com/google-cloud/what-is-google-cx-agent-studio-2a689c99c650

Been working with Google's CX Agent Studio and kept hitting the same friction points — no easy way to script evaluations, measure latency, or lint agents offline.

CXAS SCRAPI (CX Agent Studio Scripting API):

🐍 Python SDK — programmatically manage agents, apps, conversations ⌨️ CLI — run evaluations and automations from the terminal 🤖 Agent Skills — plug-and-play tools for Gemini CLI & Claude Code

Key features:

• Deep conversation evaluations
• Real-time latency metrics
• Offline linting (no API calls needed)
• Full conversation history access
• Works in Colab, Cloud Run, Cloud Functions, local env

pip install cxas-scrapi

It's open source under Apache 2.0 and hosted on Google Cloud Platform's GitHub.

🔗 https://github.com/GoogleCloudPlatform/cxas-scrapi

Happy to answer questions or take feature requests. What workflows are you trying to automate with CXAS?