DEV Community: Ismail Alam

Your Coding Agent Is Just an Elegant Loop

Ismail Alam — Sat, 08 Aug 2026 08:33:55 +0000

AI coding tools often feel like magic. But behind the scenes, even the best agents rely on three simple steps:

Look — reads your codebase and error logs
Think — plans the best change
Do — edits code and runs tests to verify

If a test fails, it adjusts and tries again. If it passes, it moves forward. This iterative loop is why agents succeed in complex codebases where a single prompt fails.

Next time an agent solves a tough problem, notice the loop:

What context did it analyze?
How did it decide?
How did it verify the fix?

It isn't magic - just a thoughtful, tight loop executed with patience.

Hope this helps someone.

About me : I am a staff product analyst having interest in Product, Analytics, ML, GenAI, DE, Physics, Art, Literature. You can take mini AI course or read my mini blogs here : https://aisimplified.live

Understanding Both Fundamentals & Abstractions Is Important

Ismail Alam — Sat, 08 Aug 2026 08:27:43 +0000

In the process of making something useful, there are multiple kinds of abstractions created, use them but know the fundamentals. Abstractions make usage easier but knowing fundamentals always help you reason from first principles.

In physics, Newton's laws of motion and gravity are the fundamentals. Theorems on energy conservation and planetary mechanics etc are just high-level derivations - abstractions built on top of them.

In computing, assembly and machine code are the fundamentals used to interact directly with hardware. High-level languages like COBOL, C++, and Python act as abstractions. They make programming user-friendly, but that convenience costs efficiency.

In GenAI, for example, MCP is also a kind of abstraction layered over raw APIs. It makes tool usage seamless for LLMs, but that convenience has a price tag : it can bloat the context window, burning through tons of extra tokens.

To understand the crux of any system and avoid getting allured by every shiny new theorem/tool, we can master the fundamentals and weigh an abstraction's cost against its convenience. Only then can we build and choose better paths - e.g. knowing when to let an agent call an API directly from bash instead of relying on MCP every time etc.

Hope this helps someone.

Stay Teachable

Ismail Alam — Sat, 08 Aug 2026 08:27:41 +0000

It is important to be open to all kinds of ideas, perspectives and opinions. A sense that there exists unique logic, reasoning, struggle, stories in every field allows you to receive new information with an open mind.

So it is important to always keep doing these internal checks:

Is my reasoning getting too tied to what I think is correct.
Do I often entertain ideas which contradicts POVs of my liking.
Am I open like a vessel where every idea can pass and then I store the ones that stand up to logic.

Start seeking something and then it will eventually seek you.

If you want to learn something (yes, even a new tech paradigm, language, or system architecture), start learning it very well, so first it reveals itself in its truest form. Then you will be so good that your contributions will enhance the field.

Hope this helps someone.

Play in GenAI is now in its applications

Ismail Alam — Sat, 08 Aug 2026 08:22:28 +0000

There is no doubt that LLMs are advancing but now they have mainly transitioned to incremental refinements.

Why will this happen?

Learning for LLMs, which happens through data, is bound to plateau sooner or later, when most of unique patterns are learnt (and new info to be gained from data is marginal). There may not be a huge improvement unless there is some breakthrough like "attention is all you need" (2017) which made transformers dominant architecture.

Then why is GenAI now more popular than ever?

Companies are now focusing either on making GenAI applications useful for all kinds of IT work or trying to make infra powering GenAI/applications better, efficient and optimised.

Agentic coding, harnesses, RAG, voice assistants etc. are all great and popular applications of LLMs. Separating training and inference chips, KV cache optimisation etc are few examples of infra optimisation.

It's great time for people with ideas to be a builder

Hope this helps someone.

Between You and Users, Choose Your Users

Ismail Alam — Sat, 08 Aug 2026 08:22:26 +0000

Now that AI lets anyone build, the temptation is to build everything. A great co-builder is just a prompt away.

While we might be on the cutting edge, our average users are still adapting to basic tools like ChatGPT or Gemini — and that is fine.

It is important that we often build something which actually helps our users. If something will soon be an out-of-the-box feature, lacks unique value, or doesn't improve efficiency, it can be avoided.

Whether our users are internal or external, putting them first clarifies what is actually worth solving. Technology is just a way to realise a product idea to help in a user problem. Idea is paramount.

Hope this helps someone.

Be a Great Human-in-the-Loop (So Nothing Can Replace You)

Ismail Alam — Sat, 08 Aug 2026 08:16:33 +0000

To stand out, view tech through three simple lenses: problems, use cases, and solutions.

Take Big Data. Hadoop was revolutionary, but coupling storage and compute on-premise became inefficient as data exploded. The real problem wasn't Hadoop — it was the coupling. Cloud architectures solved this by decoupling storage (like AWS S3) from compute, letting both scale independently.

That decoupling is popular now, but it may not be the final answer.

Never fall in love with a tool or framework. Commit to first-principles problem-solving instead. When you view tools/tech as ways to solutions rather than absolute truths, you rise above the hype and start moving from being a user to innovator.

Hope this helps someone.

Agent Harness Creates Magic: Part 1

Ismail Alam — Sat, 08 Aug 2026 08:16:31 +0000

LLMs, however powerful, are inherently stateless. In a raw setting, you send a message, they respond, and then they immediately forget. If you were to interact with a bare, standalone model directly via an API, it would look like this:

You: Your name is Edison. Remember.
LLM: Sure. My name is Edison.
You: What is your name?
LLM: I am Model X by Company Y. How can I help? (Zero memory of Edison)

Then how do apps like Gemini or Claude seem to remember everything?

An underlying application harness is doing the heavy lifting. It preserves the context for you. Even though it feels like a simple chat interface, there is a harness working behind the scenes.

For every new message in a conversation, the harness automatically appends the entire chat history before sending it back to the model. So conversation looks like this:

You: Your name is Edison. Remember.
LLM: Sure. My name is Edison.
You: What is your name? (+ Harness silently appends the previous message i.e. your name is Edison. Remember.)
LLM: I am Edison.

This is 1st magic of agent harness i.e. context management — turning a stateless LLM into a continuous conversation (which feels personal to you or your work).

But there will be problem of huge context build up if harness keeps appending message history and sending it to LLM for a response. Isn't it? Will discuss how harness manages this & more in upcoming parts of harness posts.

Hope this helps someone.