DEV Community: Vikas Sahani

I Analyzed 3 Months of Google Antigravity IDE Failures - Here's What's Actually Breaking

Vikas Sahani — Sat, 11 Apr 2026 12:22:05 +0000

A personal story, data-backed research, and a product proposal for workflow continuity in agentic AI IDEs.

I want to start with what impressed me — because I was genuinely impressed.

When I first started using Google Antigravity IDE, I kept stopping mid-session just to watch it work.

The agent would read my codebase, build a mental model of the architecture, open the browser, verify UI behavior, and iterate on failures — all without me typing a single line of code. Gemini 3.1 Pro handling a cross-file dependency refactor that would have taken me the better part of an afternoon. In fifteen minutes. Autonomously.

The underlying models are legitimately exceptional. The benchmark numbers aren't marketing — the reasoning quality in actual use reflects them.

I was hooked. I started using Antigravity for real work.

That's when I started hitting walls.

The First Wall

About 40 minutes into a complex debugging session, mid-agent-execution:

Agent terminated due to error.

No warning. No checkpoint. Files left in a partially edited, non-compiling state.

Then the popup: 167-hour lockout. Seven days.

I dug into what happened. The agent had entered an error-correction loop — retrying a failing verification step repeatedly — and burned through the Weekly Baseline Cap before I'd realized anything was wrong.

I started reading community forums to understand if this was just me.

It wasn't.

What the Community Data Shows

Across Google AI Developer Forum threads, r/GoogleAntigravityIDE, r/google_antigravity, and developer blogs, a clear pattern emerged.

I aggregated these into proxy metrics (these are directional estimates from community discourse, not official telemetry):

Metric	Estimated Range	What It Means
Task Completion Rate (TCR)	45–52%	Most agentic workflows fail before completion
Quota Interruption Rate (QIR)	68–75%	Majority of deep sessions end in forced termination
User Intervention Rate (UIR)	82–88%	True autonomy is rarely sustained
Workaround Adoption Rate (WAR)	35–42%	Users built their own continuity systems

The benchmark capability of the models: ~80% on SWE-bench Verified.
The real-world Task Completion Rate in the IDE: ~48%.

That ~32% gap is the product problem.

The 7 System Failures Behind the Gap

After mapping the failure patterns, I identified seven root causes — and they compound:

1. Mid-Workflow Termination

The quota enforcement is a binary hard cutoff. No warning threshold. No graceful wind-down. No checkpoint. When the limit is reached, execution stops immediately, leaving files in broken states.

2. Cross-Model Contagion

All models share a single quota pool. Exhausting Claude Opus (via a planning loop) instantly removes access to Gemini Flash — even for trivial, low-cost operations. A localized problem becomes a total platform outage.

3. No Predictive Awareness

The system doesn't estimate task cost before execution. The first signal of a problem is the hard stop — after the damage is done.

4. No Session State Continuity

When interrupted, all accumulated agent context is lost. Recovery requires full codebase re-ingestion, which itself costs quota. You burn resources recovering from the problem that burned your resources.

5. Thinking Token Opacity

Advanced models generate thousands of internal "thinking" tokens as part of their reasoning process. These consume quota at full rate but are invisible in the UI. Opus can burn quota ~4x faster than Gemini models. Users discover this only after the lockout.

6. UI / Backend State Desync

The interface shows quota available. The backend is already blocking execution. The trust contract between the UI and the system is broken.

7. Infinite Agent Loops

The Reason-Act-Verify loop has no failure threshold. An unresolvable error triggers infinite retries that can burn the entire weekly baseline cap in minutes. This is the most common trigger for the 167-hour lockout.

The Most Telling Signal: What Users Are Doing to Survive

The behavioral adaptations I found in the community are the most valuable product insight in this entire analysis.

Manual model routing: Users independently discovered the Opus-for-planning, Flash-for-execution pattern. They're doing this manually, 3–4 times per session.

Quota self-rationing: Power users stop work voluntarily at ~40% usage to preserve enough quota for the rest of the week.

.antigravityignore files: Users manually exclude node_modules, dist, build, .next from indexing because background indexing burns quota on every file save — before any intentional work begins.

Manual context handoff: Using /handoff commands and third-party mcp-shared-memory extensions to dump agent context to plain text files just to survive crashes.

When 35–42% of power users are building their own continuity systems, the product is failing to deliver on its core value proposition.

Every workaround is a user-validated product requirement.

The Proposal: Continuity Engine

I drafted a system-level product proposal to address this. Not a collection of feature patches — a single missing layer that manages the space between hard compute limits and user workflow expectations.

Feature 1: Intelligent Task-Based Model Routing

Auto-assign models based on task complexity. Users are already doing this manually — productize it.

Localized edit / regex / formatting → Gemini Flash
Single-file refactor / unit tests   → Claude Sonnet  
Architecture / cross-file analysis  → Gemini Pro
Complex debugging / root cause      → Claude Opus

Show the routing decision transparently. Allow instant override.

Feature 2: Quota-Aware Predictive Execution (Pre-Flight Checks)

Before the agent touches the filesystem, estimate token cost. Compare against remaining sprint quota and weekly cap.

Cost within budget → proceed silently
Cost at 70–90% of remaining → warn, offer task splitting
Cost exceeds remaining → hard stop with alternatives, not silent failure

Feature 3: Fiduciary Circuit Breakers

If the Verify phase fails 3 consecutive times: hard abort. Save state. Require human intervention.

Configurable: max_verify_failures, thought_token_ceiling, loop_detection_window.

This prevents the single most destructive failure mode — the infinite loop lockout.

Feature 4: Session State Continuity (Handoff)

Continuously serialize agent working memory to a compressed local artifact:

Active architectural decisions
Parsed file context (indexed, not verbatim)
Execution plan progress

On interruption: rehydrate from checkpoint into a new session. Target: 12% of original re-ingestion cost.

Feature 5: Decoupled Quota Pools

Separate quota buckets for High-Reasoning (Opus/Pro) and High-Velocity (Flash) models. Background indexing gets its own isolated pool.

Exhausting Pool A degrades capability — it doesn't trigger total lockout.

The Honest Trade-offs

I'm not going to pretend this is a complete fix.

Compute cost reality: The lockouts exist because inference at 1M tokens is extraordinarily expensive. Even at $200/month, unbounded agentic loops on premium models may be commercially unsustainable. The Continuity Engine works within limits — it doesn't remove them.

Hallucination cascade: Serialized session state compounds errors across sessions. A subtle wrong assumption in session 1 becomes unchallenged ground truth in session 5. Human review gates are mandatory.

Competitive defection: If daily friction of using Antigravity outweighs the benefit, the market migrates to Claude Code or Cursor. The Continuity Engine buys time — but doesn't fix the underlying trust damage from repeated lockouts.

Full Case Study

I've documented all of this in a public GitHub repository:

7 system failure mode analyses
Full user evidence taxonomy (sourced quotes from community)
Complete Continuity Engine specification (5 features, implementation priority, north star metric)
Honest risk and trade-off assessment

🔗 https://github.com/VIKAS9793/antigravity-continuity-engine

Have you hit quota walls in Antigravity? What patterns have you found? I'd especially like to hear from developers who've found effective strategies for sustained agentic work.

⚠️ Disclaimer: This is an independent product case study developed from personal usage of Google Antigravity IDE and analysis of publicly available community data. Not affiliated with, endorsed by, or representative of Google or any related organization. All metrics are proxy estimates derived from community discourse and are not official platform telemetry.

Author: Vikas Sahani | GitHub | LinkedIn | vikassahani17@gmail.com

I Built an MCP Server to Stop AI from Hallucinating Android Code — Here's What Happened

Vikas Sahani — Mon, 02 Mar 2026 15:51:20 +0000

description: "AI tools are fast. But in the Android ecosystem, fast + wrong = wasted hours. I built AndroJack MCP to force AI assistants to verify official docs before writing a single line of code."
tags: android, mcp, ai, kotlin
cover_image: https://androjack-web.netlify.app/og-image.png

canonical_url: https://androjack-web.netlify.app

Confidence is the most dangerous trait in an AI coding assistant.

It Started With a Healthcare App That Kept Breaking

While developing a complex, offline-first healthcare application, I found myself stuck in a relentless correction loop.

My AI assistants — Cursor, Claude, Windsurf — were exceptionally fast at generating code. But they were consistently generating the wrong code.

❌ Suggested Navigation 2 patterns for a Navigation 3 project
❌ Hallucinated Gradle coordinates that did not exist on Maven
❌ Used APIs removed from the Android platform years ago
❌ Generated AsyncTask — removed in API 33
❌ Generated TestCoroutineDispatcher — removed from coroutines-test 1.8+
❌ Generated ContextualFlowRow — deprecated in Compose 1.8
❌ Added android:screenOrientation="portrait" — illegal on ≥600dp under Android 16

Every session started with promise and ended with me on developer.android.com manually verifying what the AI should have checked itself.

This was not a failure of the models. It was a failure of grounding.

The Trust Crisis Is Real

I started researching the scale of this problem and found I wasn't alone.

The 2025 Stack Overflow Developer Survey (49,000 developers) confirmed a worrying trend:

84%  of developers use AI tools
29%  trust AI output accuracy  (down from 40% the year before)
35%  of Stack Overflow visits are now triggered by debugging AI-generated code

The structural flaw is simple: AI models predict tokens based on stale training data.

In the Android ecosystem — where Jetpack Compose, Navigation, and Material 3 evolve faster than model weights are retrained — memory is a liability, not an asset.

Passive prompts like agents.md or .cursorrules are not enough. They control how an AI responds. They cannot control what the AI actually knows.

What I Built: AndroJack MCP

I built AndroJack to bridge this gap.

It is an open-source Model Context Protocol (MCP) server that gives your AI assistant a live, physical connection to official Android and Kotlin documentation.

Instead of predicting from stale training memory, the AI is forced to fetch evidence before writing a single line.

VIKAS9793 / AndroJack-mcp

AndroJack: AI that actually knows Android. Real-time dependency tracking, modern architectures, and zero hallucinations.

AndroJack — The Jack of All Android Trades

An MCP server that equips your AI coding assistant with live, verified Android knowledge — so it builds from official sources, not from memory.

Also works with: Windsurf · VS Code Copilot · Google Antigravity · JetBrains AI — see Manual Config below ↓

VS Code distribution: Also live on the VS Code Marketplace as AndroJack MCP for VS Code.

PM / APM docs: Product strategy, JTBD, personas, roadmap, user stories, competitive analysis, and GTM materials live under product-management/README.md.

🔥 The Crisis That Created This Tool

In 2025, the Stack Overflow Developer Survey asked…

View on GitHub

🌐 Website → androjack-web.netlify.app
📦 npm → npmjs.com/package/androjack-mcp

See It in Action

How It Works: 21 Specialized Grounding Tools

AndroJack equips your AI assistant with a complete verification toolset:

Tool	Purpose
`android_official_search`	Live search across developer.android.com + kotlinlang.org
`gradle_dependency_checker`	Real-time version lookup from Google Maven + Maven Central
`android_component_status`	Is this API stable, deprecated, or removed?
`architecture_reference`	Official MVVM, UDF, modularization patterns
`android_api_level_check`	API availability by minSdk level
`kotlin_best_practices`	Idiomatic coroutines, Flow, Compose patterns
`material3_expressive`	Full M3 Expressive reference (GA on Android 16)
`android_navigation3_guide`	Navigation 3 (stable Nov 2025) — stops Nav2 hallucinations
`android_api36_compliance`	Android 16 / Play Store August 2026 mandate checker
`android_code_validator`	Loop-back gate — validates every code block before delivery
`android_kmp_guide`	Kotlin Multiplatform patterns
`android_wearos_guide`	Wear OS 5 + M3 Expressive for Wear
`android_xr_guide`	Android XR SDK (DP3, Dec 2025)
`android_ondevice_ai`	AICore + ML Kit Gen AI patterns
`android_large_screen_guide`	Adaptive layouts, WindowSizeClass, foldables
`android_testing_guide`	Unit, Compose UI, Hilt testing patterns
`android_build_and_publish`	R8, signing, Baseline Profiles, Play publishing
`android_scalability_guide`	Paging 3, offline-first, modularization
`android_permission_advisor`	Runtime permissions + ActivityResultContracts
`android_play_policy_advisor`	Play Store policy (October 2025 changes)
`android_debugger`	Stacktrace diagnosis grounded in official sources

The Grounding Gate: Two Levels of Enforcement

Level 1 — The Suggestion

Tools are available. The AI can use them when it feels uncertain.
Helpful, but still leaves room for overconfidence.

Level 2 — The Guarantee ⭐

Using the androjack_grounding_gate system prompt, you mandate a Verify-Before-Write loop. Every task type is mapped to a required tool call:

Before ANY Android or Kotlin code:

1. SEARCH      → android_official_search
2. VALIDATE    → android_component_status  (every API class)
3. ARCHITECTURE → architecture_reference
4. DEPENDENCIES → gradle_dependency_checker (every library)
5. API LEVEL   → android_api_level_check
6. PATTERNS    → kotlin_best_practices
7. DEBUG       → android_debugger          (for stacktraces)
8. VALIDATE OUTPUT → android_code_validator (every code block)

No code reaches the user without passing through the gate.

The Loop-Back Validator (Level 3)

android_code_validator is the final safety net — a 24-rule validation engine that inspects every code block before it reaches the user:

Verdict: PASS / WARN / FAIL

Catches:
❌ AsyncTask              → removed API 33
❌ TestCoroutineDispatcher → removed coroutines-test 1.8+
❌ ContextualFlowRow      → deprecated Compose 1.8
❌ GlobalScope.launch     → coroutine leak
❌ NavController in new code → use Navigation 3
❌ screenOrientation lock → illegal Android 16 / ≥600dp
❌ runBlocking on UI thread → ANR risk
❌ kapt in new code       → use ksp

If verdict is FAIL — the AI fixes all violations and re-runs before the user ever sees the output.

Get Started in 60 Seconds

Install into any MCP-capable IDE with a single command:

npx androjack-mcp@1.6.4 install

The interactive installer auto-detects your IDEs and writes the config:

✔ System scan complete.

IDE Detection Results:
◉  Claude Desktop     detected, not configured
◉  Cursor             detected, not configured
○  Windsurf           not found
◉  VS Code (Copilot)  detected, not configured
◉  Google Antigravity detected, not configured

◆  AndroJack MCP  Installer
◆  Select installation mode:
   ▸ Auto-install to all detected IDEs  [Recommended]
     Pick specific IDEs
     Show manual config snippet

Manual Config (if you prefer)

Claude Desktop / Cursor / Windsurf / JetBrains:

{
  "mcpServers": {
    "androjack": {
      "command": "npx",
      "args": ["-y", "androjack-mcp@1.6.4"]
    }
  }
}

VS Code (/.vscode/mcp.json):

{
  "servers": {
    "androjack": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "androjack-mcp@1.6.4"]
    }
  }
}

What's New in v1.6.4

This release addresses three areas discovered during the first real-world adoption wave:

🔴 CLI Routing Fix (Critical)

The install wizard was unreachable across all invocations — every subcommand fell through silently to the stdio server instead.

# All of these incorrectly started the server in v1.6.1:
npx androjack-mcp install
npx androjack-mcp --help
npx androjack-mcp --version

# All fixed in v1.6.4 ✅

Root cause: index.ts entry point never read process.argv before defaulting to server start. The install and serve handlers existed as separate entry points but were never routed to.

🟡 IDE Detection UX Improvement

Kiro and JetBrains/Android Studio now surface a clearer status when installed but MCP is not yet configured — instead of silently showing not found.

Before: AWS Kiro  not found
After:  AWS Kiro  detected (MCP not yet configured — launch IDE once to initialize)

🟢 Security Hardening

Updated @modelcontextprotocol/sdk to latest
Resolved all npm audit findings
Improved exception handling across tool handlers
Added input validation checks on all tool entry points
Improved dependency hygiene

Security score: 10.0 / 10.0 on MCP Marketplace scanner — zero known CVEs, verified package integrity.

Early Traction (First Launch Cycle)

AndroJack was distributed across four developer channels simultaneously:

📦 npm Registry          → 240 weekly downloads
🧩 VS Code Marketplace   → 59 installs
🔍 MCP Registries        → 115K+ discovery exposure
📊 Registry Ranking      → Top 300 MCP tools, upward movement

Conversion signal: The VS Code Marketplace showed a 163% conversion rate — meaning more installs happened directly from IDEs than from Marketplace page visits. This indicates community-driven word-of-mouth distribution, not search-driven discovery.

The Negative Constraints (What It Refuses to Generate)

One of the most useful features is what AndroJack prevents. These are hard prohibitions enforced at the system prompt level — the AI cannot generate these without explicit user override:

Universal (all targets):

AsyncTask — crashes on API 33+
TestCoroutineDispatcher — breaks CI on coroutines-test 1.8+
GlobalScope.launch / GlobalScope.async — coroutine leaks
ContextualFlowRow / ContextualFlowColumn — deprecated Compose 1.8
startActivityForResult() — use registerForActivityResult()
IntentService — deprecated API 30
runBlocking {} on UI/main thread — ANR

New Compose projects:

NavController / NavHost — use Navigation 3
MutableLiveData — use MutableStateFlow
kapt — use ksp

targetSdk ≥ 36 (Android 16 / Play Store August 2026 mandate):

android:screenOrientation="portrait" — illegal on ≥600dp
android:resizeableActivity="false" — illegal on ≥600dp

Why MCP Is the Right Architecture for This

MCP (Model Context Protocol) — open-sourced by Anthropic in late 2024 — gives AI assistants a standardized way to call external tools and retrieve live data during a conversation.

This is precisely what grounding requires:

Without MCP:
User prompt → Model predicts from training memory → Code output

With AndroJack MCP:
User prompt → Model calls android_official_search
           → Model calls gradle_dependency_checker
           → Model calls android_component_status
           → Model calls android_code_validator
           → Verified code output

The AI becomes an evidence-based engineer, not a pattern predictor.

What's Coming Next

v1.7.0 — Expanded IDE detection (process-level detection, not config-directory-only)
Community tool contributions — PR-based tool additions
HTTP transport mode — for team-shared MCP instances (already in serve subcommand)

Try It

npx androjack-mcp@1.6.4 install

🌐 androjack-web.netlify.app
💻 github.com/VIKAS9793/androjack-mcp
📦 npmjs.com/package/androjack-mcp

If you're an Android developer using AI-assisted workflows, I'd genuinely love your feedback — especially on which hallucinations you're hitting most often. Every real-world bug report shapes the next iteration.

"You are an evidence-based Android engineer, not a pattern predictor. Your value is not in knowing Android — it is in verifying Android before writing a line."

— AndroJack Grounding Gate system prompt

Exploring Google's Agentic Interoperability Stack: A Practical Guide

Vikas Sahani — Fri, 16 Jan 2026 15:04:46 +0000

Exploring Google's Agentic Interoperability Stack: A Practical Guide

What you'll learn: How Google's interconnected agent tooling components work together, what problems they solve, and what becomes possible when you combine them.

🎥 Watch the Visual Walkthrough

What Is This Stack?

Google has released several agent-related tools and protocols that, when combined, form an ecosystem-like experience for building AI agents:

ADK (Agent Development Kit) - Framework for building agents
Google MCP Servers - Connections to Google services
A2A (Agent-to-Agent Protocol) - How agents communicate
A2UI (Agent-to-UI) - How agents generate interfaces
UCP (Universal Commerce Protocol) - How agents handle commerce

Important: This isn't a single product called "Google Agentic Ecosystem." These are separate components that work well together.

The Core Problem

Before: Building an AI agent that could search flights, check loyalty points, and complete checkout required:

Custom integrations for each service
Manual UI coding for each interaction
No way to delegate tasks to other agents
Complex payment authorization flows

After: With this stack, agents can:

Use standardized connections (MCP)
Generate UIs declaratively (A2UI)
Delegate to specialized agents (A2A)
Handle commerce flows (UCP)

The Five Components Explained

1. ADK (Agent Development Kit)

What it does: Provides the foundation for building multi-agent systems.

Simple Example:

from google.adk import Agent

# Create a specialized agent
travel_agent = Agent(
    name="TravelPlanner",
    model="gemini-2.5-flash",
    tools=[search_flights, book_hotels],
    instruction="Help users plan trips efficiently."
)

What becomes possible:

Build agents that orchestrate multiple sub-agents
Manage conversation state across interactions
Integrate with external tools and APIs
Handle errors and fallbacks gracefully

📚 Learn more about ADK

2. Google MCP Servers

What it does: Provides standardized connections to Google services using the Model Context Protocol (MCP).

Available Connections:

Google Maps (geocoding, directions, places)
BigQuery (data analysis)
Google Cloud Engine (VM management)
Firebase (real-time database)
Analytics (metrics, reporting)
...and many more

Key Insight: MCP is an open standard created by Anthropic. Google provides official server implementations, so agents can connect to Google services using a standardized protocol.

What becomes possible:

Agents can discover and use Google services automatically
No need to write custom API integrations
Works with any MCP-compatible agent framework

📚 Learn more about Google MCP Servers

3. A2A (Agent-to-Agent Protocol)

What it does: Enables agents from different vendors to communicate and delegate tasks.

Example Flow:

Travel Agent → (A2A) → Loyalty Agent → (A2A) → Payment Agent

What becomes possible:

A Google-built agent can delegate to a Microsoft-built agent
Agents can discover each other's capabilities
Tasks can be delegated with full context
Security is built-in (OAuth, IAM)

Real-World Scenario: Your travel agent (built with ADK) can delegate loyalty point checking to your company's existing Salesforce agent, without you having to rebuild that functionality.

📚 Learn more about A2A

4. A2UI (Agent-to-UI)

What it does: Lets agents generate native UIs declaratively instead of just returning text.

Before (text-only):

Agent: "I found 3 flights. Option 1: $450, Delta, 5h 30m..."

After (interactive UI):

{
  "type": "comparison_table",
  "columns": ["Airline", "Price", "Duration"],
  "rows": [
    {"airline": "Delta", "price": "$450", "duration": "5h 30m"},
    {"airline": "United", "price": "$480", "duration": "5h 15m"}
  ],
  "actions": [{"label": "Book Now", "handler": "book_flight"}]
}

What becomes possible:

Agents can present complex data visually
Users can interact with results (sort, filter, select)
UIs work across platforms (web, mobile, desktop)

📚 Learn more about A2UI

5. UCP (Universal Commerce Protocol)

What it does: Provides standardized primitives for agent-driven commerce.

Core Capabilities:

Product discovery
Cart management
Checkout flows
Payment authorization (with cryptographic proof)

Compatible with: Shopify, Stripe, PayPal, Adyen, Mastercard

What becomes possible:

Agents can handle end-to-end shopping experiences
Users maintain control over payment authorization
Works across different payment providers

📚 Learn more about UCP

How They Connect

The Stack (Bottom to Top)

Observability Layer - Logging, monitoring, debugging
Trust & Policy Layer - IAM, OAuth, consent management
A2A Inter-Agent Layer - Cross-vendor agent communication
MCP Tool Layer - Service integrations (Google MCP Servers)
Orchestration Layer - Multi-agent coordination (ADK)
UI Layer - Native UI generation (A2UI)

Commerce flows (UCP) work across all layers.

Use Case 1: Consumer Shopping Assistant

The Scenario

User: "I want a coffee machine under $200"

How the Stack Enables This

Step 1: Orchestration (ADK)

Main agent understands intent
Breaks down into sub-tasks

Step 2: Product Search (MCP)

Connects to e-commerce APIs
Searches across multiple retailers

Step 3: Loyalty Check (A2A)

Delegates to user's loyalty agent
Retrieves available discounts

Step 4: UI Presentation (A2UI)

Generates comparison table
Shows products with prices and discounts

Step 5: Checkout (UCP)

Handles cart and payment
Applies loyalty rewards
Confirms purchase

What This Unlocks

For users: Complete shopping in one conversation
For developers: Build once, works across retailers
For businesses: Integrate with existing loyalty systems

📚 Full walkthrough

Use Case 2: Enterprise Workflow Automation

The Scenario

Automating expense approval across multiple systems (Workday, Stripe, email)

Traditional Approach

Employee submits in Workday
Manager gets email, logs into Workday
Finance processes in Stripe
Manual coordination between systems

With the Stack

# Employee submits
expense_agent.submit(amount=450, category="travel")

# HR Agent validates (via A2A → Workday)
hr_agent.validate_expense(expense_id)

# Manager Agent approves (via A2A)
manager_agent.approve(expense_id)

# Payment Agent processes (via A2A → Stripe)
payment_agent.process_payment(expense_id)

# Confirmation sent
notification_agent.send_confirmation(employee_id)

What This Unlocks

For employees: Submit once, automatic routing
For managers: Approve from any interface
For IT: No custom integrations between systems

📚 Full walkthrough

Use Case 3: Developer CI/CD Pipeline

The Scenario

Automating code review, security scanning, testing, and deployment

The Flow

Developer commits → GitHub
Code Review Agent analyzes (via MCP → GitHub API)
Security Agent scans (via A2A)
Test Agent runs tests
Deploy Agent deploys (via MCP → Cloud Run)
Monitoring Agent tracks performance

What This Unlocks

For developers: Faster feedback loops
For security teams: Automated vulnerability detection
For ops: Consistent deployment process

📚 Full walkthrough

Comparing Approaches

Feature	Google Stack	Anthropic MCP	OpenAI Swarm
Multi-agent orchestration	✅ ADK	❌ No framework	✅ Swarm (OpenAI-only)
Agent-to-agent protocol	✅ A2A (vendor-neutral)	❌ Not included	❌ Proprietary
Native UI generation	✅ A2UI (cross-platform)	❌ Text-only	✅ MCP Apps (web-only)
Commerce primitives	✅ UCP	❌ Not included	❌ Not included
MCP Server Ecosystem	✅ Official Google servers	✅ Created MCP standard	✅ Compatible
Open Governance	✅ A2A/UCP (Linux Foundation)	✅ MCP (Linux Foundation)	❌ Proprietary

Production Considerations

Before deploying agents, consider:

Trust & Security

How will users consent to agent actions?
What audit logging is needed?
How are tool permissions managed?
How is payment authorization secured?

Safety & Governance

How to prevent hallucinations?
What guardrails are needed?
How to handle agent errors?
When should humans intervene?

User Experience

What happens if agents fail?
How to monitor latency?
How to show agent reasoning?
What fallback UX is needed?

📚 Full readiness checklist

Deployment Options

1. Fully Managed (Google Cloud)

Best for: Startups, rapid prototyping
Pros: Zero infrastructure management
Cons: Vendor lock-in

2. Hybrid (Multi-Cloud)

Best for: Enterprises with existing cloud investments
Pros: Flexibility, vendor neutrality
Cons: Complex orchestration

3. On-Premises

Best for: Regulated industries (healthcare, finance)
Pros: Full data control
Cons: Higher operational overhead

📚 Reference architecture

Getting Started

1. Explore the Documentation

📚 Complete knowledge base

2. Try Official Quickstarts

3. Join the Community

Key Takeaways

For Product Managers

Understand what's possible with agent interoperability
Explore use cases relevant to your domain
Consider how agents could improve user workflows

For Engineers

Learn how components work together
Experiment with official quickstarts
Build prototypes to test feasibility

For Executives

Evaluate strategic fit for your organization
Understand open standards vs. proprietary approaches
Consider vendor neutrality benefits

Sources

All information is sourced from official documentation:

ADK: Google Developers Blog (April 2025)
Google MCP Servers: Google Cloud Blog (Dec 2024)
A2A: Linux Foundation (June 2025)
A2UI: Google Developers Blog (Dec 2025)
UCP: Google Developers Blog (Jan 2026)

Author & Disclaimer

Author: Vikas Sahani

GitHub: @VIKAS9793

Repository: google-agentic-interop-stack

Disclaimer

This is an independent educational/research project. All third-party names, trademarks, protocols, documentation, and referenced materials belong to their respective owners. This project is not affiliated with or endorsed by Google, Anthropic, or other referenced organizations.

Last Updated: January 16, 2026

License: MIT

💬 Discussion

What use cases are you most excited about? How might you use these components in your projects? Share your thoughts in the comments! 👇

Product Titans Runner-up: Agentic AI for Hyper-Personalized Learning

Vikas Sahani — Thu, 15 Jan 2026 14:19:25 +0000

Runner-up Case Study: Agentic AI Learning Platform for India

Overview

This project was created as part of Product Titans: National Product Management Challenge, hosted on Unstop and organized by Book My Mentor.

I approached this as a real-world PM discovery case and built an end-to-end product case study for a Hyper-Personalized Learning & Skill Development Platform powered by Agentic AI, aligned to the needs and constraints of India’s learning and skilling ecosystem.

Result: Certificate of Excellence – Runner-up (Rank 2, Score 6.4)

Solo Team Name: North Star Hunter

Problem Statement

In India, learners don’t always struggle due to a lack of content. They struggle because learning is often not aligned to:

their current gaps
their pace
their language preferences
their desired outcomes (exam performance, job readiness)

This results in:

low retention
repeated learning cycles without progress
dependence on parallel systems such as offline coaching/tutoring

So the real product problem is not:

"Build another course platform."

It is:

Help learners achieve outcomes faster with clarity, guidance, and accountability.

Product Sense: Why AI (and why Agentic AI)?

This project intentionally avoids “AI for hype.”

The core question was:
Do we actually need AI here?

The evaluation led to a practical conclusion:

personalization is not only content-level (recommendation)
it requires diagnosis, planning, feedback loops, accountability, and adaptation
this is where agentic workflows can reduce friction and improve learning outcomes

Who This Helps (Personas)

I mapped key learner segments to ensure the platform works for real India-first contexts:

Tier-2 / Tier-3 value learners with budget constraints and limited time
Exam aspirants needing structured planning and gap identification
Working professionals seeking upskilling with outcome clarity
D2C power learners looking for optimized learning paths and progress tracking

Key Insights (Friction Points)

I analyzed the full journey from discovery to outcomes and identified recurring friction:

learners don’t know what to learn next
lack of structured feedback loops
low motivation and inconsistent habits
weak accountability mechanisms
mismatch between course completion and real outcomes

Proposed Solution (High-Level)

An agentic AI-powered learning platform that supports:

Skill gap diagnosis
Personalized learning plan generation
Daily/weekly accountability tracking
Adaptive feedback and course corrections
Outcome-based progress measurement (not vanity metrics)

Prioritization Framework

I used RICE prioritization to avoid feature overload and focus on what moves outcomes:

Reach
Impact
Confidence
Effort

This helped separate:

root causes (diagnosis, clarity, feedback loops) from
surface-level fixes (more videos, more quizzes)

Success Metrics (North Star + KPIs)

I avoided vanity usage metrics and designed a measurable success model.

North Star Metric

Verified Learner Outcome Rate

Supporting Metrics

activation rate (first meaningful learning action)
habit formation / weekly consistency
completion quality (not completion volume)
diagnostic-to-outcome conversion
retention linked to goal attainment

Responsible AI: Risks and Controls

Because this product is agentic AI-driven, I documented risks and governance controls:

explainability and transparency of recommendations
bias and unfair personalization risk
privacy and data protection principles
safe completion boundaries and human override
accountability (who owns decisions and how errors are handled)

The goal was to ensure AI improves outcomes without creating unsafe or misleading personalization.

Output Artifacts

This project includes:

problem framing and market gap analysis (India-first)
persona mapping
user journey mapping
RICE prioritization
North Star metric + KPI tree
experiment design and GTM sequencing
responsible AI risk analysis + mitigations

Learnings

This project was a major learning experience that strengthened my practical PM skills:

problem framing before solution
structured prioritization
measurable outcomes and experiment-first thinking
responsible AI and governance as core product design constraints

Disclaimer

This is an independent case study created for learning and evaluation purposes as part of the Product Titans challenge. It is not affiliated with or endorsed by any employer or platform beyond the official competition context.

"Krishi Rakshak: Lightweight EfficientNet-Based Crop Disease Classifier"

Vikas Sahani — Mon, 09 Jun 2025 09:06:23 +0000

🚀 Sharing my end-to-end ML project: Krishi Rakshak — a crop disease classifier built with efficientnet_b0 for smallholder farmers.

⚙️ Model Highlights:

90%+ accuracy on PlantVillage (38 classes)

Trained with AdamW, scheduler, early stopping

Used weighted loss to handle class imbalance

📊 Evaluation:

Manual metric validation + confusion matrix

Precision, recall, F1, per-class accuracy

🧩 Deployment:

PyTorch + Gradio UI, multilingual support

Designed for light, modular inference

🔗 GitHub: https://github.com/VIKAS9793/KrishiRakshak.git
📹 Demo: https://drive.google.com/file/d/1PDxYq5rOuCXZAldZlSd6Q3M5WIGXEtbJ/view?usp=drivesdk

Built independently from scratch — feedback welcome from ML/AgriTech community on optimization or scaling for noisy field data.

CustomerAI – An Open Source Toolkit to Detect & Mitigate Bias in Enterprise AI Systems

Vikas Sahani — Tue, 06 May 2025 08:23:32 +0000

TL;DR:
CustomerAI is an open-source prototype designed to identify and reduce bias in ML systems—especially in sectors like finance, healthcare, HR, and retail. It's cloud-ready, model-agnostic, and aimed at real-world, regulatory-compliant AI deployment.

Why I Built This

Bias in AI isn't just a theoretical problem—it's a real threat when ML systems influence financial approvals, medical treatments, hiring decisions, or product visibility. Yet, tools for practical bias detection in these contexts are limited, fragmented, or difficult to integrate.

CustomerAI is my attempt to close that gap—an open-source, developer-friendly fairness toolkit built with speed, clarity, and real-world use in mind.

Key Features

Bias Detection + Mitigation: With metrics, checks, and correction strategies

Framework-agnostic: Use with TensorFlow, PyTorch, Databricks, SageMaker

Cloud-Deployable: Run it on AWS, GCP, Azure with minimal setup

Regulatory Awareness: Built around NIST AI RMF and EU AI Act principles

Built Fast: Developed with AI assistants and prompt engineering in weeks

Use Cases

Finance: Fairness in credit scoring and loan approval

Healthcare: Balanced treatment predictions for diverse populations

Retail: Equitable product recommendations and pricing models

HR Tech: Debiasing hiring and screening algorithms

Architecture Overview

Core Components:

Data Preprocessing & Bias Audits

Fairness Metrics (e.g., Demographic Parity, Equal Opportunity)

Mitigation Strategies (reweighting, post-processing)

Cloud Deployment (via Docker/K8s scripts)

Getting Started

git clone https://github.com/VIKAS9793/CustomerAI_Project.git
cd CustomerAI_Project
pip install -r requirements.txt
jupyter notebook

Check out the usage examples in examples/ for how to evaluate fairness in your own datasets.

Roadmap

[ ] Web-based UI for bias reports

[ ] More fairness metrics & mitigation algorithms

[ ] CI/CD for cloud deployment

[ ] Integrations with monitoring tools like MLflow

Call for Contributors

This is an early prototype, and I’m actively looking for:

Feedback from ML engineers and researchers

Contributors to improve functionality, metrics, and documentation

Ideas for turning this into a reusable SDK or web service

GitHub & Feedback

Repo:
https://github.com/VIKAS9793/CustomerAI_Project.git

Drop a star if you find it useful!
Open an issue if you spot bugs or have ideas.
Pull Requests are very welcome!

Let's Connect

I’m especially interested in connecting with folks working in AI fairness, ML governance, or compliance tech. Share your thoughts in the comments—or reach out on GitHub.

DEV Community: Vikas Sahani

I Analyzed 3 Months of Google Antigravity IDE Failures - Here's What's Actually Breaking

The First Wall

What the Community Data Shows

The 7 System Failures Behind the Gap

1. Mid-Workflow Termination

2. Cross-Model Contagion

3. No Predictive Awareness

4. No Session State Continuity

5. Thinking Token Opacity

6. UI / Backend State Desync

7. Infinite Agent Loops

The Most Telling Signal: What Users Are Doing to Survive

The Proposal: Continuity Engine

Feature 1: Intelligent Task-Based Model Routing

Feature 2: Quota-Aware Predictive Execution (Pre-Flight Checks)

Feature 3: Fiduciary Circuit Breakers

Feature 4: Session State Continuity (Handoff)

Feature 5: Decoupled Quota Pools

The Honest Trade-offs

Full Case Study

I Built an MCP Server to Stop AI from Hallucinating Android Code — Here's What Happened

canonical_url: https://androjack-web.netlify.app

It Started With a Healthcare App That Kept Breaking

The Trust Crisis Is Real

What I Built: AndroJack MCP

VIKAS9793 / AndroJack-mcp

AndroJack: AI that actually knows Android. Real-time dependency tracking, modern architectures, and zero hallucinations.

AndroJack — The Jack of All Android Trades

An MCP server that equips your AI coding assistant with live, verified Android knowledge — so it builds from official sources, not from memory.

Table of Contents

🔥 The Crisis That Created This Tool

See It in Action

How It Works: 21 Specialized Grounding Tools

The Grounding Gate: Two Levels of Enforcement

Level 1 — The Suggestion

Level 2 — The Guarantee ⭐

The Loop-Back Validator (Level 3)

Get Started in 60 Seconds

Manual Config (if you prefer)

What's New in v1.6.4

🔴 CLI Routing Fix (Critical)

🟡 IDE Detection UX Improvement

🟢 Security Hardening

Early Traction (First Launch Cycle)

The Negative Constraints (What It Refuses to Generate)

Why MCP Is the Right Architecture for This

What's Coming Next

Try It

Exploring Google's Agentic Interoperability Stack: A Practical Guide

Exploring Google's Agentic Interoperability Stack: A Practical Guide

🎥 Watch the Visual Walkthrough

What Is This Stack?

The Core Problem

The Five Components Explained

1. ADK (Agent Development Kit)

2. Google MCP Servers

3. A2A (Agent-to-Agent Protocol)

4. A2UI (Agent-to-UI)

5. UCP (Universal Commerce Protocol)

How They Connect

The Stack (Bottom to Top)

Use Case 1: Consumer Shopping Assistant

The Scenario

How the Stack Enables This

What This Unlocks

Use Case 2: Enterprise Workflow Automation

The Scenario

Traditional Approach

With the Stack

What This Unlocks

Use Case 3: Developer CI/CD Pipeline

The Scenario

The Flow

What This Unlocks

Comparing Approaches

Production Considerations

Trust & Security

Safety & Governance

User Experience