Crosswind

Backend infrastructure that builds and maintains itself. Crosswind continuously provisions, monitors, and updates your cloud backend as your frontend evolves.

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Gemini 3 Integration Overview

Crosswind is a self-building, self-maintaining backend infrastructure platform enabled entirely by Gemini 3. It analyzes your frontend codebase, automatically designs and provisions cloud infrastructure (GCP/AWS), and continuously maintains your backend as your application evolves.

Gemini 3 features are absolutely central to Crosswind's capabilities:

  1. Advanced Reasoning enables the System Designer Agent to analyze complex codebases (500+ files), design production-ready cloud architectures, and make intelligent trade-offs between services (Cloud Run vs. Cloud Functions, Firestore vs. Cloud SQL).

  2. Code Execution allows the Provisioner Agent to generate and validate Terraform configurations, test Cloud Run handlers, and verify database migrations before deployment.

  3. Context Continuity (1M tokens) enables multi-agent orchestration across 7 specialized agents, passing full architecture context between Code Analyzer → System Designer → Provisioner → Verifier → Remediator without information loss.

  4. Thought Signature showing users <thinking> reasoning in real-time so they understand why Firestore was chosen over Cloud SQL or why Firebase Auth fits their use case.

  5. Google Search Grounding enables auto-remediation: when deployments fail, the Remediator Agent searches for recent Stack Overflow solutions, GCP pricing changes, and latest best practices to fix issues autonomously.

Inspiration

The Problem: Backend is the new bottleneck.

Frontend development has become nearly instant with AI copilots. But backend development still requires coordinating across backend engineers, DevOps teams, and SREs. Weeks are lost on repetitive work: authentication, CRUD APIs, queues, infrastructure wiring.

The Missing Link: Current tools break down as systems get real.

  • Backend-as-a-Service platforms (Supabase, Convex, Appwrite): Work for simple CRUD, but break when you need Redis, queues, search, custom networking
  • Full cloud platforms (GCP, AWS, Azure): Support everything you need, but everything becomes manual again

Teams either accept limits or accept complexity. There is no middle ground.

The Vision: What if backend could build and maintain itself as your application evolves?

I realized that Gemini 3's capabilities including advanced reasoning, code execution, context continuity, thought signature, and grounding with Google Search could create a continuously operating system that provisions and maintains production backends automatically, which gives you the power of GCP/AWS/Azure without the manual DevOps work.

What It Does

Crosswind is backend infrastructure that builds and maintains itself.

Unlike traditional Backend-as-a-Service platforms (Supabase, Convex, Appwrite) that break when you need complex production architectures, and unlike manual cloud platforms (GCP, AWS, Azure) that require constant DevOps work, Crosswind continuously provisions and maintains your backend as your application evolves.

Core Workflow

1. Import Frontend GitHub Repo

Connect your existing Next.js repository. Crosswind links to it and prepares for analysis.

2. Create Backend GitHub Repo

Crosswind creates a new GitHub repository in your account for the backend code. This is where all generated infrastructure code, Cloud Run/Cloud Functions handlers, and Terraform configs will be pushed.

3. Select Cloud Provider

Choose GCP or AWS. The backend will be deployed directly to your cloud account, not Crosswind's infrastructure. No vendor lock-in.

4. AI Analyzes & Auto-Generates (Streaming Updates)

Gemini 3 kicks off a multi-agent workflow. You see real-time progress as it happens:

Code Analysis (using 1M token context):

  • Reads your entire frontend codebase
  • Extracts API contracts: fetch('/api/products') → needs GET /api/products
  • Detects data models: User, Product, Order → needs database schemas
  • Identifies auth patterns: useAuth() → needs Firebase Auth/Identity Platform

Architecture Design (4 progressive phases with auto-approval):

  • Phase 1: Cloud services graph (Cloud Run, CloudSQL, Firestore, Firebase Auth)
  • Phase 2: API routes & endpoints matching frontend contracts
  • Phase 3: Database schemas (normalized tables with relationships)
  • Phase 4: Security rules (IAM policies, CORS, rate limits, encryption)

Each phase shows Gemini's reasoning (<thinking>) in real-time, so you understand why each decision was made.

Code Generation:

  • Production-ready handlers (Python/Node.js)
  • Terraform infrastructure-as-code
  • Database migration scripts
  • API Gateway configurations
  • Pushes all code to your backend GitHub repo

5. Deploy to Your Cloud

Review the generated architecture, then deploy:

  • Terraform provisions infrastructure in your GCP/AWS account
  • Functions are deployed
  • API Gateway is configured
  • Database tables are created
  • Integration tests run automatically
  • If tests fail → AI agents investigate with Google Search and auto-remediate

6. Continuous Self-Maintenance (Ongoing)

As you develop your frontend:

  • GitHub webhooks notify Crosswind of code changes
  • Detects new API calls (e.g., new payment form)
  • Automatically provisions matching backend updates
  • Pushes changes to backend repo → redeploys infrastructure
  • Auto-remediates deployment failures and infrastructure drift
  • Keeps backend in sync with zero DevOps work

Key Features

  • Continuous Self-Maintenance: Backend automatically evolves as your frontend changes—no manual DevOps work
  • Auto-Remediation: When deployments fail or infrastructure drifts, AI agents automatically investigate and fix issues
  • Contract-Driven Intelligence: Tracks API versions, detects changes, auto-approves safe updates
  • Multi-Cloud Support: Deploys to your GCP/AWS account—no proprietary PaaS lock-in
  • Real-Time Research: Uses Gemini's Google Search to find latest cloud service pricing, features, and best practices
  • Multi-Agent Orchestration: 7 specialized AI agents working 24/7 (Code Analyzer, Contract Registry, System Designer, Orchestrator, Provisioner, Verifier, Remediator)

How I Built It

Gemini 3's Unique Capabilities: Enabling a Self-Maintaining System

1. Advanced Reasoning → Intelligent Architecture Design

Gemini 3's extended thinking powers the System Designer Agent to:

  • Analyze complex frontend codebases (500+ files, 50,000+ lines)
  • Design GCP/AWS service graphs with proper connections
  • Balance trade-offs: serverless vs. containers, SQL vs. NoSQL, sync vs. async
  • Ensure security: IAM policies, VPC configurations, encryption

Example: For "e-commerce with payments," Gemini reasons:

  1. "Payment processing needs PCI compliance → Use Stripe, not storing cards"
  2. "High read/write on products → Firestore with built-in caching"
  3. "User sessions need persistence → Firebase Auth + Firestore sessions"
  4. "Order processing is async → Cloud Tasks queue + Cloud Run consumer"

2. Code Execution → Validated Infrastructure

Gemini 3's code execution lets the Provisioner Agent:

  • Generate Terraform configurations
  • Execute validation scripts locally before deployment
  • Test handlers with sample inputs
  • Verify database migrations don't break

3. Context Continuity → Multi-Agent State Management

The 1M token context enables stateful orchestration:

  • Designer Agent creates architecture → passes full context to Provisioner
  • Provisioner generates code → Verifier receives architecture + code
  • Remediator sees entire history: requirements, design, code, test results

Without this: Critical information would be lost between agent handoffs, causing inconsistent infrastructure.

4. Thought Signature → Progressive Design

Structured thinking enables 4-phase approval workflow:

  • Phase 1: <thinking>User needs auth + data + compute</thinking> → Proposes Firebase Auth + Firestore + Cloud Run
  • Phase 2: <thinking>Auth endpoints: /login, /register. Data: CRUD on /users, /products</thinking> → Generates OpenAPI spec
  • Phase 3: <thinking>Products collection needs: id, name, price, inventory, reference to users</thinking> → Designs Firestore schema
  • Phase 4: <thinking>Cloud Run needs Identity Platform integration. Firestore needs security rules</thinking> → Creates IAM policies & Firestore rules

Users see why each decision was made, building trust.

5. Grounding with Google Search → Real-Time Cloud Knowledge

Google Search makes Crosswind work from current information:

  • Pricing: "Cloud Run vs Cloud Functions pricing 2026" → Finds latest GCP costs
  • Service Selection: "Cloud SQL vs Firestore 2026" → Gets recent Google Cloud blog posts
  • Error Investigation: "Cloud Run service deployment failed" → Finds Stack Overflow answer from 2 weeks ago

Critical for auto-remediation: When infrastructure fails, Gemini researches the error, finds recent solutions, and fixes autonomously.

Multi-Agent System (Enabled by Gemini 3):

  1. Code Analyzer (Context Continuity): Frontend code → API contracts
  2. Contract Registry (Context Continuity): Tracks API versions and changes
  3. System Designer (Advanced Reasoning + Thought Signature): Requirements → Architecture
  4. DevOps Orchestrator (Advanced Reasoning): Architecture → Execution plan
  5. DevOps Provisioner (Code Execution): Plan → Generated + validated code
  6. DevOps Verifier (Code Execution): Test deployed endpoints
  7. Remediator (Code Execution + Google Search): Auto-fix failures + investigate errors

Agentic Tool System (30+ Production Tools):

Category Tool Purpose
Core Execution ShellTool Execute shell commands (AWS CLI, Terraform, gcloud, CDK, any CLI)
FileTool File operations (write, read, edit, delete) for code generation
IaCTool Deploy Infrastructure as Code (CloudFormation, Terraform, CDK)
CodeAnalysisTool Syntax validation, import analysis, test execution
HttpTool Make HTTP requests for API testing and verification
WaitTool Wait for AWS/GCP resources to reach desired state
WebSearchTool Search web for solutions, docs, examples
MemoryTool Persistent memory across agent sessions (key-value store)
TodoTool Task tracking for multi-step operations with progress
LintTool Run linters and static analysis (code quality checks)
Codebase CodebaseTool List directories, grep search, find files by pattern
GitTool Clone/pull repos, commit, push changes to backend repository
AWS AWSResourceTools Lookup ARNs, resolve Lambda integrations, prevent hallucination
SAMTools Lambda deployment (sam init, build, deploy, local invoke)
create_sam_template Generate SAM templates for serverless applications
sam_init Initialize new SAM project structure
sam_build Build Lambda deployment artifacts
sam_deploy Deploy Lambda functions to AWS
sam_local_invoke Test Lambda functions locally before deployment
GCP GCPTools gcloud CLI execution, Gemini-powered CLI research
gcloud_cli Execute gcloud commands with error handling
research_gcp_service Use Gemini to research GCP services and best practices
validate_gcp_resource Validate GCP resource configuration before deployment
Auth Testing AuthToolkit User Pool testing and verification
create_test_user Create ephemeral test users for authentication
authenticate Authenticate users and get JWT tokens
test_protected_endpoint Test protected API endpoints with auth tokens
delete_test_user Cleanup test users after verification
State ResourceStateRegistry Track deployed resources and their ARNs
check_exists Check if resource already exists
register Register new deployed resource
Terminal TerminalState Track terminal state (cwd, command history, exit codes)
StreamingFormatter Rich streaming output with diffs and progress

MCP Integration:

  • 64+ Model Context Protocol servers
  • Real-time GCP/AWS documentation lookup
  • Pricing calculators and best practices

Challenges I Ran Into

1. Balancing Gemini's Context Window with Agent State

Challenge: With 1M token context, I could fit entire codebases—but needed to manage what context gets passed between 7 agents. Too much → slow responses. Too little → agents lose critical information.

Solution: Built a smart context manager:

  • Passes full codebase to Code Analyzer (needs everything)
  • Passes architecture + relevant code to Provisioner (selective)
  • Passes only error logs + search results to Remediator (focused)

Response times stay under 3 seconds while maintaining continuity.

2. Prompting for Thought Signatures

Challenge: Getting Gemini to reliably output structured thinking (<thinking>) for progressive design required careful prompt engineering.

Solution: Used Pydantic schemas with forced structure:

class DesignPhase(BaseModel):
    thinking: str  # Must explain reasoning
    proposal: ServiceArchitecture
    alternatives_considered: List[str]

This forced Gemini to show its work, building user trust.

3. Deployment Verification

Generated code sometimes had subtle bugs (missing IAM permissions, wrong environment variables).

Solution: Built DevOps Verifier agent that runs integration tests against deployed endpoints. If tests fail, Remediator agent uses MCP to research fixes and regenerates code.

Accomplishments That I'm Proud Of

I didn't build "ChatGPT for infrastructure." Crosswind is a 24/7 multi-agent system that continuously monitors, provisions, and maintains your backend as your application evolves. Gemini 3 Pro powers 7 specialized agents that work together to keep your infrastructure self-healing and auto-updating.

What I Learned

1. Gemini 3's Tool-Calling is Outstanding

I tested 8 different models. Gemini 3 Pro made 22 tool calls for complex architecture generation, far better than any competitor. This capability is essential for my multi-agent system.

2. Google Search Grounding Changes Everything

Having real-time access to GCP/AWS documentation, pricing, and best practices through Gemini's search makes the system feel "alive." It's not generating code from stale training data, but also researching the latest information.

3. LLM Outputs Need Strict Validation

Even Gemini 3 hallucinates occasionally. Using Pydantic schemas to validate every generated architecture/code snippet improved reliability.

What's Next for Crosswind

Short-Term (2 months)

  1. Monitoring & Observability: Auto-generate Cloud Monitoring/Cloud Logging dashboards
  2. Cost Optimization: Use Gemini to analyze cloud bills and suggest savings
  3. Terraform State Management: Handle multi-environment deployments (dev/staging/prod)

Medium-Term (3-6 months)

  1. Azure Support: Add Azure Functions, Cosmos DB, Entra ID
  2. Advanced Testing: Generate E2E tests using Playwright/Cypress
  3. Database Migrations: Smart schema evolution with zero-downtime deploys
  4. Performance Optimization: Use Gemini to analyze traces and suggest caching strategies
  5. Multi-Region Deployments: Global CDN + regional Cloud Run services

Long-Term (6+ months)

  1. Kubernetes Support: Generate Helm charts and Kubernetes manifests
  2. Microservices Orchestration: Break monoliths into optimal service boundaries
  3. AI-Powered Incident Response: Auto-remediate production issues using Gemini's reasoning
  4. Cost Prediction Models: Forecast cloud spend using historical data + Gemini analysis

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