Weekly Retro #01: The Reality of Shipping AI Agents & Micro-SaaS

There is a distinct difference between coding in a silo and shipping in the wild. For years, I focused on the code—optimizing algorithms, refining architectures, and obsessing over clean commits. But code that sits in a private repository doesn’t solve problems, and it certainly doesn’t build a business.

This week marked the start of a shift: Proof over promises.

As an AI Automation Engineer, my goal is to build systems that work. This series, the Weekly Retro, is my commitment to transparency. It isn’t a highlight reel; it’s a documentation of the engineering reality. It covers the agents I built, the Micro-SaaS features I shipped, the infrastructure that crumbled under pressure, and the raw data resulting from those actions.

Here is the retrospective for Week #01.

The Montage: What Shipped

This week was heavy on foundation work. Building intelligent agents requires more than just an OpenAI API key; it requires robust state management and reliable vector storage. Here is what made it to production.

1. The "Docs-to-Code" RAG Agent

I found myself constantly context-switching between documentation tabs and my IDE when working with new libraries like LangGraph. To solve this, I built a specialized RAG (Retrieval-Augmented Generation) agent.

• The Tech Stack: Python, LangChain, Pinecone (Vector DB), and GPT-4o.
• The Mechanism: The agent scrapes specific documentation URLs, chunks the text based on semantic meaning rather than arbitrary character counts, and embeds them into Pinecone. When queried, it doesn't just return text; it returns executable code snippets formatted for immediate insertion.
• The Win: It reduced my documentation lookup time by approximately 40%. The agent understands context better than a standard Ctrl+F because it synthesizes information from multiple pages (e.g., combining authentication docs with API endpoint docs).

2. Micro-SaaS Boilerplate: Auth & Payments

Building Micro-SaaS tools requires repetitive setup. I spent the middle of the week refactoring my Next.js starter kit.

• Shipped: A unified authentication wrapper using Clerk, integrated directly with Stripe webhooks.
• The Logic: I implemented a listening system for Stripe events (checkout.session.completed). Upon a successful payment, the system automatically updates the user's metadata in the database to provision access tokens for the AI tools.
• Why it matters: This eliminates manual provisioning. A user pays, the webhook fires, the database updates, and the UI unlocks—zero human intervention.

The Breakages: What Failed

In engineering, if nothing breaks, you aren't moving fast enough. Week 1 had its fair share of failures.

1. The Vercel Serverless Timeout

The Incident: I attempted to deploy a long-running research agent on Vercel's standard serverless functions.

The Error: 504 Gateway Timeout.

The Root Cause: The agent was designed to perform iterative Google searches, scrape results, and synthesize a report. This process takes about 45-60 seconds. Vercel's hobby tier caps serverless functions at 10 seconds (and Pro at 60 seconds, which is still risky for LLM chains).

The Fix (In Progress): I am migrating the heavy compute logic to a separate backend using FastAPI hosted on a standard VPS (DigitalOcean) or moving to asynchronous background jobs using Inngest. This separates the frontend UI from the long-running AI logic.

2. Context Window Overflow

The Incident: While testing the "Docs-to-Code" agent, I fed it an entire library's changelog to analyze for breaking changes.

The Error: Token limit exceeded.

The Lesson: Even with large context windows (128k tokens), sloppy prompting and poor chunking strategies will hit walls. I was passing raw HTML with excessive metadata that provided no value to the LLM. I've since added a preprocessing step to strip non-essential HTML tags before tokenization.

The Metrics: Data & Growth

Building is half the battle; distribution is the other half. I am tracking specific KPIs to ensure that what I build actually reaches developers and creators. Here is the baseline for Week 1.

Traffic & Engagement

• Portfolio Views: 142 unique visitors. (Source: LinkedIn & X direct links).
• GitHub Stars: +4 on the open-source agent repo.
• Newsletter/Blog Subs: 12 new subscribers.

Analysis

The numbers are modest, which is expected for Week 1. However, the conversion rate from "visitor" to "subscriber" is roughly 8%, which is decent for a technical blog. The traffic source is heavily skewed towards LinkedIn, suggesting that the "professional breakdown" content style resonates more there than on X (Twitter).

Key Insight: Technical deep-dives perform better than generic "AI is the future" posts. The audience wants to see the code, the architecture diagrams, and the specific prompts used. They crave the how, not just the what.

The Pivot: Strategy for Week 2

Based on the wins and failures of this week, I am adjusting the trajectory for the upcoming sprint.

1. Asynchronous Architecture

I cannot rely on synchronous serverless functions for complex agents. Next week's engineering focus is setting up a robust message queue (Redis/BullMQ) or utilizing Inngest. This ensures that when a user asks an agent to "build a marketing plan," the UI doesn't hang while the LLM thinks.

2. From "Chat" to "UI"

Most AI tools currently look like chatbots. I want to move away from the chat interface. Next week, I am experimenting with Generative UI. Instead of the agent replying with text, I want it to render a React component or a dynamic dashboard based on the data it retrieves.

3. Content Tweak: "Build With Me"

Instead of just showing the result, I will record a short, unedited session of the debugging process. Showing the friction of development builds more trust than a polished demo.

Final Thoughts

Week 1 was about breaking the inertia. The systems are imperfect, the code has TODO comments scattered throughout, and the user base is small. But the loop has started: Build, Ship, Analyze, Iterate.

If you are a developer looking to integrate AI agents into your workflow, or a creator wanting to understand the mechanics behind the automation, stick around. We are just getting started.

Next week's target: Shipping the Generative UI component and fixing the serverless timeout issue. See you in the IDE.