Harnessing the Power of Agentic LLMs: Exploring Context-Aware Coding with the Anthropic Sonnet Model 3.5

In this article, I want to delve into an intriguing topic: agenting capabilities with Large Language Models (LLMs). Let's start by understanding what an agent does. Much like how we break down complex tasks into smaller, manageable pieces, agents operate similarly. They decompose tasks and execute them step by step, mirroring human problem-solving approaches. This concept is particularly exciting when applied to modern LLMs.

I'll be highlighting one of the latest advancements in this field: the Anthropic Sonnet model 3.5, which comes equipped with agentic coding capabilities. One of the standout features of this model is its context-aware coding ability.

In a previous post My No Code Journey with Gen AI , I shared my journey in building an end-to-end application using Generative AI, where I discussed the challenges of maintaining context awareness. For instance, while tools like GitHub are great for referencing and modifying specific files, and ChatGPT can assist in generating code, they often require you to manually feed large chunks of code to ensure the AI doesn’t overlook existing structures. This becomes increasingly difficult as projects grow in complexity, requiring continuous copying and pasting of files for the AI to analyze and suggest accurate changes.

This is where the new agentic coding capabilities truly become a game-changer. Visual Studio has introduced a new extension called Claude Dev, an autonomous AI agent that integrates seamlessly into your IDE. It guides you through every step of the development process, asking for your permission at each stage to execute commands in your terminal. From setting up your project structure to building complete applications, Claude Dev ensures you’re in control while it handles the heavy lifting. Isn’t that fascinating?

For demonstration purposes, I asked Claude Dev to create a timesheet application tailored to the needs of both India and the US

Launching Claude Dev With Initial Prompt.

Claude Dev Requesting Permission to Set Up the Project Structure

Claude Dev Setting Up the Project Dependencies

Claude Dev Launching the Application with Errors

Claude Dev Automatically Correcting Errors in Real-Time

Claude Dev Launching the Final Application Without Errors

One particularly impressive feature is its ability to automatically correct errors as it progresses. Additionally, it provides a detailed account of token usage at every step.

While the use of Generative AI in the Software Development Life Cycle (SDLC) is clearly advantageous, AI agents represent the next step forward. These agents are not only context-aware but also capable of understanding entire projects at a granular level. This allows them to intelligently refactor code, optimize performance, identify and resolve potential issues, and even suggest innovative solutions that a human developer might overlook. With these capabilities, AI agents have the potential to solve real-world problems, accelerate development timelines, and significantly improve overall efficiency.

As responsible AI practices continue to mature, they will guide the development and deployment of AI agents, enhancing their capabilities and ensuring they address increasingly complex scenarios in an ethical and transparent manner. This alignment will help decision-makers more confidently assess whether their AI investments are yielding tangible benefits.

I’d love to hear your thoughts on these emerging agentic capabilities in LLMs. How do you see them impacting the future of software development ? Feel free to share your comments or experiences below. Thanks for reading!