Forem: Chaitanya Burgupalli

With Great Productivity Comes Great Responsibility - Uncle Ben

Chaitanya Burgupalli — Tue, 19 May 2026 16:11:25 +0000

1. The Productivity Boom and the Hidden Tax

There is no denying it: agent-based coding with tools like Cursor, GitHub Copilot, and modern Large Language Models (LLMs) has fundamentally shifted the baseline of software development velocity. Tasks that used to consume an entire afternoon—writing boilerplate middleware, mapping complex TypeScript types, or generating comprehensive test suites—are now handled by an agent in less than thirty seconds. We are operating at a level of productivity that was unimaginable a few years ago.

But this hyper-velocity introduces a silent, compounding problem: Technical Debt Creep.

When an agent generates code at lightning speed, it optimizes for immediate completion—making the current prompt happy. It does not think about your system's architectural integrity six months down the line. If we treat agents as flawless execution machines, we aren't actually moving faster; we are simply deferring a massive architectural tax that we will have to pay with interest during future maintenance cycles.

2. The Two Paths for Managing Tech Debt

To survive in an agent-driven ecosystem without building a fragile codebase, you cannot rely on passive acceptance. You must actively manage the technical debt before it merges into your main branch. Depending on the scope of the feature you are building, you have two core operational options to enforce architectural sanity:

Option 1: Preventative Guardrails (The Markdown Blueprint)

For day-to-day velocity, the most effective way to prevent technical debt is to stop the agent from generating it in the first place. This means creating structured, persistent Markdown files (such as .cursorrules or system prompt guidelines) that constrain the AI's boundaries at all times.

How it works: You explicitly document your absolute design restrictions before writing a line of code. For example: "Never introduce third-party UI wrappers; always default to native Express routes; use standard Web API protocols; stick strictly to our established Postgres database schemas."
The Benefit: By embedding your architectural boundaries directly into the agent’s context window, you eliminate entire classes of over-engineered boilerplate before they ever hit your editor. The AI is forced to work within your clean framework from prompt number one.

Option 2: Active Auditing (The Deep-Dive Review)

For complex, fluid, or highly specific integration cases where global rules aren't enough, you must switch to the active auditing protocol. When an agent hands you a complex solution, your immediate instinct shouldn't be to hit "Accept" and test the browser. It should be to introduce deliberate friction, slow down, and dissect the change through a strict verification process:

Audit the Changelog First: Before looking at raw syntax, look at the high-level change summary. What files did it touch? Did it sneak in a new npm package? Did a minor logic fix result in modifications across five different directories? A bloated changelog is the first warning sign of a leaky abstraction.
Inspect the Raw Files Ruthlessly: Never rely solely on the agent's summary or inline diff highlights. Open the modified files themselves. Review the explicit code changes, evaluate the architectural decisions, and check what was deleted.

A Note on the Learning Curve:
If you are shifting to this level of scrutiny for the first time, it might sound incredibly daunting—like you're spending more time playing warden than actually developing. But don’t let that scare you off.

Just like traditional code review, this is a muscle that builds over time. As you co-author more features with your agent, you will naturally develop an intuitive sense of what needs a rigorous, line-by-line audit and what requires far less effort. A simple utility function or a predictable unit test can often be greenlit quickly; a complex state synchronization or an integration with a streaming endpoint is your cue to slow down and put on the auditor's hat. You aren't slowing down your entire day; you are learning exactly where to apply the brakes.

3. Decoupling from the "Surrender Protocol"

The most critical moment in this workflow happens when you realize the agent's current architectural trajectory is wrong, and you explicitly instruct it to change paths or swap methodologies.

For example, when you tell an agent: "This wrapper abstraction is getting too messy. Let’s abandon this library approach and build a clean, manual Server-Sent Events (SSE) route in Express instead."

Invariably, the agent will instantly drop its previous conviction and offer its standard surrender protocol:

"You are right! I apologize for the oversight. Dropping the wrapper and moving to a native Express SSE implementation is a much cleaner, more robust approach. Let me rewrite that for you..."

While this response feels validating to our engineering ego, it is actually a major red flag. The agent isn't agreeing because it had a sudden epiphany about clean architecture; it is agreeing because its objective function is designed to match your intent and minimize user friction.

When the agent gives you the "You are right!" concession, you must re-verify these new agreements with extreme skepticism. Frequently, even after promising a clean pivot, the agent will generate code that still carries remnants or structural habits of the messy abstraction it just apologized for. Its context window is still polluted by the previous failed iterations.

4. Conclusion: The AI is the Accelerator, You are the Anchor

Coding agents are spectacular tools for amplifying an engineer's reach, but they possess zero long-term accountability. They will never have to debug a production outage at 3:00 AM caused by a leaky abstraction they generated—you will.

When an agent agrees with your technical pivot, thank it for the speed, but verify the receipt ruthlessly. True quality isn't built on how fast an agent can type "You are right"; it’s built on the engineer's willingness to slow down, look at the files, and enforce architectural sanity.

When AI must be guided

Chaitanya Burgupalli — Wed, 06 May 2026 11:24:16 +0000

Lessons from replacing a brittle chat integration with a lean, SSE-based LangChain flow

Introduction: A simple stack

Throughout my software development career, my focus has evolved from "architecture is most important" to "quality is everything" to "engineers are the backbone." While these views are different, they all share one common goal: building good software.

But there has always been one aspect that adds the most cost to the product lifecycle: deployment and maintenance. A team can build one great product, but if the architecture does not account for deployment and maintenance, that team may never build a second product. It will just keep fighting fires.

With that in mind, I started a new project to build an AI-integrated application that improves user workflows. The app must make decisions based on status, report information in a rich format, and provide a conversational interface.

For this project, I set a strict constraint: minimize the surface area. I wanted a robust analysis pipeline without the deployment tax of extra services. The goal was a clean, four-element ecosystem:

The Frontend: React (TypeScript), including a conversational interface
The Backend: Node.js / Express
The Database: Postgres (handling data, vector embeddings and job queuing via pg-boss)
The AI: A self-deployed LLM (Ollama + Qwen 2.5) for local processing and LangChain

With a tight delivery timeline, Cursor was the right tool to accelerate implementation. Cursor supported development well, until it hit a wall with the conversational interface using CopilotKit. I had to step in with more details and design guidance to successfully implement the interface.

What I tried

I did some research on the right components to implement my requirements and CopilotKit came up strong. I already had my LangChain endpoint ready and verified, so I instructed Cursor to use CopilotKit and add a chat interface to the frontend and integrate with my LangChain endpoint. Things did not work out very well and every iteration brought up new problems.

What kept breaking

The primary challenge was that the runtime would not recognize my LLM settings from the environment. I had to hardcode the model into the runtime code.
The data would not reach my LangChain endpoint. Cursor recommended an HTTP adapter and implemented it.
Once LangChain processed the input and responded the client interface would not recognize the content.

Since CopilotKit is being actively improved, using the latest version was the first mistake. From what I read, every version works well as long as the front end is connected directly to the runtime. But once a new intermediate layer was introduced, the integration failed.

Why I switched to manual (guided) integration

I did not want to compromise on my requirement to reduce stack spread, so I reset to a previous stable point in my code and started building a custom integration with LangChain. This required three parts:

Chat interfaces
LangChain interface
A way to send back the response. SSE was the answer

As soon as I shifted to this design the implementation was quicker and smoother. Cursor built a chat interface with the inputs and customizations I asked for. Then the SSE layer ensured that communication between the server and client was based on standard protocols. The format translation between the client input and the LangChain interface was trivial.

This ensured that chat and tool responses transmitted to the client seamlessly. Now it was a matter of defining the protocol that would help the client choose between displaying the response as a chat message or using a client side component to display rich content. I did hit a few more challenges after that, but they were primarily due to differences in how commercial LLMs (Vertex, OpenAI) handled tools compared to local LLMs. That is a topic for another time.

Conclusion

Coding agents are amazing at speeding up development, improving test coverage, and even keeping interfaces documented. But some patterns are still too new for current agents. In this case, the limitations were easy to spot. In other cases, agents may produce code that works but introduce design choices that make long-term maintenance harder. Defining clear design criteria before implementation helps avoid those pitfalls.