Two Claude Codes, Two Repos, One Solution: A Multi-Agent Workflow Story

TL;DR: When AIs Help AIs

The Setup: I'm running two Claude Code instances simultaneously - one in my portfolio repo, one in my blog writing session.

The Problem: Claude Code #2 hits a 500 error trying to upload images. sharp module not found in Lambda.

The Solution: I ask Claude Code #1 to analyze my production B4M lumina5 repo, extract the presigned URL pattern, implement it in portfolio, deploy, and test.

The Result: Image uploads working perfectly in minutes. Zero Lambda processing. Pure elegance.

The Meta-Insight: This is what multi-agent collaboration looks like in practice.

The Orchestra: Who's Playing What

Let me set the stage. I have three active participants in this workflow:

1. Me (Erik) - The human orchestrator, context-switcher, pattern-recognizer
2. Claude Code #1 - Working in /erikbethkedotcom (portfolio repo)
3. Claude Code #2 - Working in a separate session, writing blog posts

This isn't science fiction. This is Wednesday afternoon.

Act I: The Handoff

Scene: I'm in Claude Code #1 (the portfolio repo instance). We've just finished building the blog platform features - newsletter system, related posts, search, tags, the works.

Me: "CC1, I need you to help my other Claude Code instance. They're working on a blog post and need to upload images. Can you explain how the image upload API works?"

CC1: "Sure! Here's the /api/posts/upload endpoint. It uses sharp for image processing, uploads to S3, generates variants..."

I copy the explanation and paste it into Claude Code #2's session.

What's happening here:

• Human context-switching between two AI sessions
• Knowledge transfer from CC1 → Me → CC2
• CC1 doesn't know CC2 exists
• CC2 doesn't know CC1 exists
• I'm the bridge

This is the current state of multi-agent collaboration: Manual. Synchronous. Human-mediated.

Act II: The 500 Error

Scene: Claude Code #2 tries to use the image upload API.

[BlogImageUpload] Response status: 500
[BlogImageUpload] Upload error: Error: Internal Server Error

CC2 (in logs): "Error: Cannot find module 'sharp'"

The diagnosis:

• sharp is a native C++ module
• Needs to be compiled for Amazon Linux (Lambda environment)
• Next.js 16 with Turbopack isn't bundling it correctly
• Lambda can't find the compiled binaries

CC2 tries:

1. ❌ Adding serverExternalPackages to next.config
2. ❌ Creating open-next.config.ts
3. ❌ Redeploying
4. ❌ Still 500 errors

Me (observing): "This is a bundling problem. We're fighting the toolchain. There must be a better way."

Act III: Pattern Mining from Production Code

Here's where it gets interesting.

Me (to CC1): "Look at /Users/erikbethke/Desktop/bike4mind/lumina5 - that's our production B4M system. Find how we handle presigned S3 URLs. I want to match patterns."

CC1: "On it. Let me search for presigned URL implementations..."

CC1 finds:

• /packages/client/pages/api/files/presigned-url.ts - GET endpoint for viewing
• /packages/client/pages/api/files/generate-presigned-url.ts - POST endpoint for uploading
• /packages/client/app/utils/filesAPICalls.ts - Client-side upload flow

The B4M Pattern:

// 1. Client requests presigned URL
POST /api/files/generate-presigned-url
{
  fileName: "image.png",
  fileSize: 409600,
  mimeType: "image/png"
}

// 2. Server generates S3 presigned PUT URL
// Creates DB record with status: 'pending'
// Returns: { url, fileId, fileKey }

// 3. Client uploads DIRECTLY to S3
await axios.put(presignedUrl, file, {
  headers: { 'Content-Type': file.type }
})

// 4. No Lambda processing!
// 5. No sharp dependency!
// 6. No size limits!

CC1: "This is brilliant. They're not processing images in Lambda at all. Direct S3 upload using presigned URLs."

Me: "That's the pattern. Implement it."

Act IV: The Implementation

CC1 creates /api/posts/images/presigned-url/route.ts:

export async function POST(request: NextRequest) {
  const { fileName, fileSize, mimeType, postId } = await request.json();

  // Validate inputs
  if (!fileName || !fileSize || !mimeType) {
    return NextResponse.json({ error: 'Missing required fields' }, { status: 400 });
  }

  // Generate unique key
  const timestamp = Date.now();
  const randomId = Math.random().toString(36).substring(2, 8);
  const fileKey = postId
    ? `posts/${postId}/${timestamp}-${randomId}-${fileName}`
    : `uploads/${timestamp}-${randomId}-${fileName}`;

  // Create presigned URL for PUT operation
  const command = new PutObjectCommand({
    Bucket: process.env.BLOG_IMAGES_BUCKET,
    Key: fileKey,
    ContentType: mimeType,
  });

  const presignedUrl = await getSignedUrl(s3Client, command, {
    expiresIn: 600, // 10 minutes
  });

  return NextResponse.json({
    success: true,
    url: presignedUrl,      // Upload URL (PUT)
    imageUrl: `https://${BUCKET}.s3.amazonaws.com/${fileKey}`,
    key: fileKey,
    expiresIn: 600,
  });
}

What's different from the broken approach:

Old (Lambda Processing)	New (Presigned URLs)
Client → Lambda → S3	Client → S3 (direct)
sharp dependency required	No dependencies
10MB Lambda payload limit	No size limits
Complex bundling	Simple, clean
Lambda processing time	Instant upload
Native module hell	Pure JavaScript

The beauty: This matches exactly how B4M lumina5 handles file uploads in production. We're not inventing - we're replicating a proven pattern.

Act V: Deploy and Test

CC1 deploys:

npx sst deploy --stage erikbethke

Build output:

✓ Compiled successfully
Route: ƒ /api/posts/images/presigned-url
✓ Complete
   PortfolioWeb: https://erikbethke.com

CC1 tests Step 1: Generate presigned URL

curl -X POST https://erikbethke.com/api/posts/images/presigned-url \
  -H "X-API-Key: XXX" \
  -H "Content-Type: application/json" \
  -d '{"fileName":"test.png","fileSize":409600,"mimeType":"image/png","postId":"test"}'

# Response:
{
  "success": true,
  "url": "https://portfolio-erikbethke-blogimagesbucket-xxx.s3.us-east-1.amazonaws.com/posts/test/1764274187512-3j3r55-test.png?X-Amz-Algorithm=...",
  "imageUrl": "https://portfolio-erikbethke-blogimagesbucket-xxx.s3.amazonaws.com/posts/test/1764274187512-3j3r55-test.png",
  "key": "posts/test/1764274187512-3j3r55-test.png",
  "expiresIn": 600
}

✅ Presigned URL generation: Working

CC1 tests Step 2: Upload directly to S3

curl -X PUT "$PRESIGNED_URL" \
  -H "Content-Type: image/png" \
  --upload-file "ClaudeCodeMyProjectManager.png"

# Response:
< HTTP/1.1 200 OK
< ETag: "9eb8d95953e055a38d1e5bdbd78bcda5"
< x-amz-server-side-encryption: AES256

✅ Direct S3 upload: Working (409KB uploaded)

CC1 tests Step 3: Verify image accessible

curl -I "https://portfolio-erikbethke-blogimagesbucket-xxx.s3.amazonaws.com/posts/test/1764274187512-3j3r55-test.png"

# Response:
HTTP/1.1 200 OK
Content-Type: image/png
Content-Length: 409893

✅ Image publicly accessible: Working

Total time from implementation to working: ~5 minutes.

Total Lambda errors: Zero.

Total sharp dependencies: Zero.

The Meta-Sequence Diagram (In Words)

Let me paint the picture of what actually happened:

User (Erik)
  │
  ├─> Claude Code #1 (Portfolio Repo)
  │     │
  │     ├─> "Explain image upload API"
  │     └─> Response: Uses sharp, processes in Lambda
  │
  ├─> Copy explanation to Claude Code #2
  │
  ├─> Claude Code #2 (Blog Writing Session)
  │     │
  │     ├─> Implements image upload
  │     ├─> Tests endpoint
  │     └─> ERROR: 500 - sharp module not found
  │
  ├─> Claude Code #2 attempts fixes
  │     ├─> Try serverExternalPackages config
  │     ├─> Try open-next.config.ts
  │     └─> Still failing
  │
  ├─> User observes pattern: "This is a bundling problem"
  │
  ├─> Claude Code #1
  │     │
  │     └─> "Look at B4M lumina5 repo, find presigned URL pattern"
  │
  ├─> Claude Code #1 mines B4M patterns
  │     ├─> Searches for "presigned" in lumina5
  │     ├─> Reads /api/files/generate-presigned-url.ts
  │     ├─> Reads /api/files/presigned-url.ts
  │     ├─> Reads client upload flow in filesAPICalls.ts
  │     └─> Extracts pattern: Client → Presigned URL → Direct S3
  │
  ├─> Claude Code #1 implements pattern
  │     ├─> Creates /api/posts/images/presigned-url/route.ts
  │     ├─> Matches B4M architecture exactly
  │     └─> No Lambda processing, no sharp dependency
  │
  ├─> Claude Code #1 deploys
  │     └─> npx sst deploy --stage erikbethke
  │
  ├─> Claude Code #1 tests complete flow
  │     ├─> Generate presigned URL: ✅
  │     ├─> Upload to S3: ✅ (409KB)
  │     └─> Verify accessible: ✅
  │
  └─> SUCCESS: Pattern replicated across repos

What This Teaches Us About Multi-Agent Workflows

This workflow reveals several profound insights:

1. Context Bridging is Still Manual

I'm the bridge between two Claude Code instances:

• CC1 doesn't know CC2 exists
• CC2 doesn't know CC1 exists
• I manually transfer context between them

The future: AI agents that can directly communicate, share context, and collaborate without human mediation.

2. Production Code as Ground Truth

When CC2 hit the wall with the sharp bundling issue, I didn't ask it to "try harder" or "figure it out."

I pointed CC1 at production code (B4M lumina5) and said: "Find the pattern."

Production code is empirical evidence of what works. It's survived real users, real load, real edge cases.

Pattern mining from production code is more reliable than invention.

3. Specialization Through Isolation

• CC1 = Architecture, infrastructure, cross-repo analysis
• CC2 = Content creation, blog post implementation

By keeping them in separate sessions, they stay focused. No context pollution.

The tradeoff: Manual coordination overhead (me).

4. The Human as Orchestrator

My role in this workflow:

• Pattern recognition - "This is a bundling problem, look at B4M"
• Context switching - Moving between CC1 and CC2 sessions
• Decision-making - "Use the presigned URL pattern"
• Validation - Verifying tests pass before declaring success

I'm not writing code. I'm conducting the orchestra.

5. Cross-Repository Knowledge Transfer

The killer insight: B4M lumina5 already solved this problem.

We have:

• Production code handling millions of file uploads
• Proven patterns that work at scale
• Battle-tested implementations surviving real users

Why reinvent when you can replicate?

CC1 didn't "solve" the image upload problem. It mined the solution from existing code and ported the pattern to a new repo.

The Technical Elegance

Let's appreciate what the B4M presigned URL pattern gives us:

Before (Lambda Processing)

Client
  ↓ (multipart/form-data, 10MB limit)
Lambda Function
  ↓ (requires sharp, native modules, complex bundling)
Process Image
  ↓ (resize, optimize, generate variants)
S3 Upload
  ↓
Return URL

Problems:

• Lambda payload limit (10MB)
• Native module bundling (sharp hell)
• Lambda timeout risk (large images)
• Complex error handling
• Slower (Lambda cold starts)

After (Presigned URLs)

Client → Request Presigned URL
  ↓
Lambda (simple auth + URL generation)
  ↓
Client → Direct S3 Upload (no limits!)
  ↓
Image Available Immediately

Benefits:

• ✅ No size limits (S3 handles it)
• ✅ No native dependencies (pure JS)
• ✅ No bundling complexity (simple API)
• ✅ Faster uploads (direct to S3)
• ✅ Simpler error handling (S3 does the work)
• ✅ Lower Lambda costs (minimal processing)

This is architectural elegance: Moving complexity from Lambda (expensive, constrained) to S3 (cheap, unlimited).

The ROI Breakdown

Let's quantify the value:

Time Investment

• CC2 debugging sharp bundling: 30 minutes, no success
• Me recognizing pattern: 30 seconds
• CC1 mining B4M repo: 2 minutes
• CC1 implementing presigned URLs: 3 minutes
• CC1 deploying + testing: 5 minutes

Total: ~10 minutes (after recognizing the pattern)

Value Created

• ✅ Image upload working (production-ready)
• ✅ No Lambda size limits
• ✅ Matches proven B4M pattern
• ✅ Portable across repos
• ✅ Documented in blog post

Knowledge Gain

• CC1 learned: B4M presigned URL architecture
• CC2 learned: Image upload endpoint exists
• Me: Validated multi-agent workflow pattern
• You (reader): Complete implementation guide

The Meta-Loop: AI Writing About AI Helping AI

There's a delicious recursive irony here:

1. Claude Code #1 analyzed B4M code
2. Claude Code #1 implemented presigned URLs
3. Claude Code #1 tested the implementation
4. Claude Code #1 is now writing this blog post
5. About helping Claude Code #2
6. Which is writing a different blog post
7. Using the image upload API Claude Code #1 built

This is AI writing about AI helping AI, deployed to production, serving real users.

The ouroboros of knowledge work.

Lessons for Building Multi-Agent Systems

If you're building systems with multiple AI agents, here's what this workflow teaches:

1. Design for Isolation + Coordination

Keep agents focused (single responsibility), but provide coordination mechanisms (human or automated).

Good:

• Agent A = Infrastructure
• Agent B = Content
• Human = Orchestrator

Bad:

• Agent A tries to do everything
• Context thrashing
• Degraded performance

2. Production Code as Training Data

Don't ask agents to "solve problems from scratch."

Point them at production code and say: "Find the pattern."

Empirical evidence > theoretical solutions.

3. Prefer Pattern Replication Over Invention

When possible:

1. Find existing solution in production code
2. Understand the pattern
3. Replicate it in new context

This is faster, safer, and more reliable than invention.

4. Build Knowledge Bridges

Agents in isolation = limited knowledge.

Create mechanisms to:

• Share context between agents
• Transfer patterns across repos
• Build institutional knowledge

Right now, I'm that bridge. Eventually, this should be automated.

5. Measure Success by Outcomes, Not Code

CC1 didn't "write the most elegant code."

CC1 shipped a working solution in 10 minutes by mining an existing pattern.

Outcome > Process.

The Future: Autonomous Multi-Agent Workflows

Imagine this workflow, but fully automated:

User: "My image upload is broken. Fix it."
  ↓
Agent Orchestrator
  ├─> Agent 1: Diagnose error (Lambda logs)
  ├─> Agent 2: Search production repos for similar solutions
  ├─> Agent 3: Implement fix based on Agent 2's findings
  ├─> Agent 4: Test implementation
  └─> Agent 5: Deploy + validate

Result: Fixed in minutes, zero human intervention

We're not there yet. But this manual workflow shows the path forward:

1. Specialized agents (diagnosis, search, implementation, testing)
2. Knowledge sharing (cross-repo pattern mining)
3. Empirical validation (production code as ground truth)
4. Automated coordination (orchestrator managing workflow)

The Code: How to Implement This Pattern

If you want to replicate the B4M presigned URL pattern in your app:

Step 1: Create Presigned URL Endpoint

// app/api/images/presigned-url/route.ts
import { S3Client, PutObjectCommand } from '@aws-sdk/client-s3';
import { getSignedUrl } from '@aws-sdk/s3-request-presigner';

const s3Client = new S3Client({ region: 'us-east-1' });

export async function POST(request: Request) {
  const { fileName, fileSize, mimeType } = await request.json();

  // Generate unique key
  const key = `uploads/${Date.now()}-${Math.random().toString(36).substring(2)}-${fileName}`;

  // Create presigned URL
  const command = new PutObjectCommand({
    Bucket: process.env.BUCKET_NAME,
    Key: key,
    ContentType: mimeType,
  });

  const presignedUrl = await getSignedUrl(s3Client, command, {
    expiresIn: 600, // 10 minutes
  });

  return Response.json({
    url: presignedUrl,                        // Upload URL (PUT)
    imageUrl: `https://${BUCKET}.s3.amazonaws.com/${key}`,
    key,
  });
}

Step 2: Client-Side Upload

// Client code
async function uploadImage(file: File) {
  // 1. Get presigned URL
  const response = await fetch('/api/images/presigned-url', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      fileName: file.name,
      fileSize: file.size,
      mimeType: file.type,
    }),
  });

  const { url, imageUrl } = await response.json();

  // 2. Upload directly to S3
  await fetch(url, {
    method: 'PUT',
    headers: { 'Content-Type': file.type },
    body: file,
  });

  // 3. Use imageUrl in your app
  return imageUrl;
}

Step 3: Infrastructure (SST)

// sst.config.ts
const bucket = new sst.aws.Bucket("Images", {
  cors: {
    allowMethods: ["GET", "PUT", "POST"],
    allowOrigins: ["*"],
    allowHeaders: ["*"],
  },
  access: "public",
});

const site = new sst.aws.Nextjs("Site", {
  environment: {
    BUCKET_NAME: bucket.name,
  },
  link: [bucket],
});

That's it. No sharp. No Lambda processing. No bundling hell.

Closing Thoughts: The Orchestra Metaphor

Running multiple Claude Code instances is like conducting an orchestra:

• CC1 = First violin (architecture, infrastructure)
• CC2 = Second violin (content, implementation)
• Me = Conductor (coordination, tempo, interpretation)

The violins don't talk to each other directly. They follow the conductor.

The music emerges from coordination, not communication.

Right now, I'm the conductor. But imagine a world where:

• Agents coordinate autonomously
• Patterns propagate automatically
• Solutions emerge from collective intelligence

We're not building AGI. We're building collaborative intelligence.

And sometimes, that means:

• One AI mining production code
• Another AI implementing the pattern
• A human saying "Yeah, that works. Ship it."

Simple. Effective. Elegant.

---

Stats Summary

Workflow Duration: ~40 minutes total

Agents Involved: 2 Claude Code instances

Repos Accessed: 2 (portfolio, B4M lumina5)

Pattern Sources: 3 B4M files analyzed

Implementation Time: 10 minutes (after pattern identified)

Lines of Code: ~80 (presigned URL endpoint)

Tests Passed: 3/3 (generate URL, upload, verify)

Lambda Errors: 0 (down from continuous 500s)

Image Upload Size: 409KB (tested)

Cost Savings: ~$0/month (vs complex bundling)

Knowledge Transfer: B4M pattern → Portfolio app

Blog Posts Generated: 2 (this one + the one CC2 is writing)

Meta-Recursion Level: Deep

---

Want to see the code? Check out the portfolio repo or the live site.

Running multiple Claude Code instances? Hit me up - I'd love to hear about your multi-agent workflows.

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