Automate 14-Phase Implementations: Zero Hallucinations, No Human Intervention

You have a 14-phase implementation plan. Database schema, API endpoints, authentication, billing integration, admin dashboard, tests, documentation. Traditionally, this takes weeks of human coding with endless context-switching. With Snipara + RLM-Runtime, you can automate the entire pipeline—clean code, passing tests, zero hallucinations, no human intervention required.

The Vision: Hands-Off Implementation

Imagine describing a feature at 9 AM and returning at 5 PM to find it complete: database tables created, API endpoints built, tests passing, documentation written—all following your team's coding standards. This isn't science fiction. It's what happens when you combine:

Real Scenario: Building a Multi-Tenant Billing System

Let's walk through automating a complex feature: a complete billing system with Stripe integration for a multi-tenant SaaS application. This involves:

• Database schema for subscriptions, invoices, and usage tracking
• Stripe integration with webhook handling
• Usage-based billing calculations
• Customer portal and admin dashboard
• Email notifications for billing events
• Comprehensive test coverage

The 14-Phase Implementation Plan

The Automation Architecture

Here's how the system works at a high level:

Step-by-Step: Setting Up Automation

1. Upload Your Implementation Plan

First, upload your feature specification to Snipara:

# Upload the implementation plan

rlm_upload_document(

    path="docs/features/billing-system.md",

    content="""# Multi-Tenant Billing System

## Overview

Complete Stripe integration with usage-based billing...

## Phase 1: Database Schema

- Create Subscription model with Stripe references

- Create Invoice model with line items

- Create UsageRecord model for metered billing

...

"""

)

2. Generate the Execution Plan

# Let Snipara analyze and create optimal execution order

plan = rlm_plan(

    query="Implement billing system from docs/features/billing-system.md",

    max_tokens=16000,

    strategy="relevance_first"  # Prioritize by dependency order

)

Returns: Topologically sorted phases with dependencies

3. Run the Automation Loop

from rlm import RLM

rlm = RLM(

    backend="anthropic",       # or "openai", "litellm"

    environment="docker",      # Full isolation

    max_depth=5,               # Max iterations per phase

    snipara_api_key="rlm_...",

    snipara_project_slug="my-saas"

)

# Execute all phases

for phase in plan["phases"]:

    result = rlm.completion(f"""

        Execute: {phase['description']}

        Context from Snipara provides:

        - Relevant existing code patterns

        - Team coding standards (MANDATORY rules)

        - Actual function signatures to use

        Tasks:

        1. Write implementation code

        2. Write unit tests

        3. Run tests with pytest

        4. Fix any failures and re-run

        5. Return only when ALL tests pass

    """)

    # Save progress for resume capability

    rlm_remember(

        type="context",

        content=f"Phase {phase['id']} complete: {result.summary}"

    )

Why It Works: The Quality Guarantees

Measured Results

Deep Dive: Phase 5 (Webhook Handling)

Let's look at how one phase actually executes. Phase 5 implements Stripe webhook handling—a complex task requiring security, event routing, and idempotency.

What Snipara Returns (Context)

The Iteration Loop in Action

Production Automation Script

Here's a complete script you can use to automate multi-phase implementations:

#!/usr/bin/env python3

"""Automated multi-phase implementation with Snipara + RLM-Runtime"""

import json

import os

from datetime import datetime

from rlm import RLM

# Configuration

SNIPARA_API_KEY = os.environ["SNIPARA_API_KEY"]

SNIPARA_PROJECT = os.environ["SNIPARA_PROJECT"]

IMPLEMENTATION_DOC = "docs/features/billing-system.md"

def run_automation():

    # Initialize RLM with Snipara integration

    rlm = RLM(

        backend="anthropic",

        model="claude-sonnet-4-20250514",

        environment="docker",

        max_depth=5,

        snipara_api_key=SNIPARA_API_KEY,

        snipara_project_slug=SNIPARA_PROJECT,

        verbose=True

    )

    # Step 1: Generate execution plan

    plan_result = rlm.completion(f"""

        Use rlm_plan to create execution plan for:

        {IMPLEMENTATION_DOC}

        Return the phases with dependencies as JSON.

    """)

    phases = json.loads(plan_result.response)

    print(f"Generated {len(phases)} phases")

    # Step 2: Execute each phase

    results = []

    for i, phase in enumerate(phases, 1):

        print(f"\n{'='*50}")

        print(f"Phase {i}/{len(phases)}: {phase['name']}")

        print(f"{'='*50}")

        result = rlm.completion(f"""

            Execute Phase {i}: {phase['name']}

            Description: {phase['description']}

            IMPORTANT:

            1. Query Snipara for relevant context FIRST

            2. Follow ALL team coding standards

            3. Write comprehensive tests

            4. Run tests and iterate until ALL pass

            5. Only return success when tests are green

        """)

        results.append({

            "phase": i,

            "name": phase['name'],

            "success": result.success,

            "iterations": result.iterations,

            "tokens_used": result.tokens_used

        })

        # Store progress for resume capability

        rlm.completion(f"""

            Use rlm_remember to save:

            Phase {i} ({phase['name']}) completed successfully

            Iterations: {result.iterations}

            category="implementation", type="context"

        """)

    # Summary

    print(f"\n{'='*50}")

    print("AUTOMATION COMPLETE")

    print(f"{'='*50}")

    successful = sum(1 for r in results if r['success'])

    print(f"Phases completed: {successful}/{len(phases)}")

    print(f"Total tokens: {sum(r['tokens_used'] for r in results):,}")

if __name__ == "__main__":

    run_automation()

When to Use This Approach

Cost Breakdown

Running the 14-phase billing system implementation with Claude Sonnet:

The Future of Implementation

Complex implementations don't have to mean weeks of manual coding. With Snipara's context optimization and RLM-Runtime's sandboxed execution, you can:

1. Define once — Write your implementation plan as documentation
2. Automate completely — Let the system execute phases with zero intervention
3. Trust the output — Every line follows your patterns and passes your tests

The code is clean. The tests pass. The hallucinations are gone. And you didn't write a single line manually.