October 19, 2025

Reflections from my first few weeks using agentic coding tools

Like any truly disruptive technology, AI agents have the power to reshuffle the deck. Done right, they offer laggards a leapfrog opportunity to rewire their competitiveness. Done wrong—or not at all—they risk accelerating the decline of today's market leaders. This is a moment of strategic divergence. While the technology will continue to evolve, it is already mature enough to drive real, transformative change across industries.

— McKinsey, "Seizing the agentic AI advantage" (June 2025)

What prompted me to start using agentic coding tools?

Earlier this month I attended a Claude Code workshop that Alex Lieberman and Arman Hezarkhani from Tenex hosted. Prior to that event I had never actually experimented with Claude Code or watched a formal demo of the tool, despite having seen it mentioned countless times online. What I observed during the workshop motivated me to install Claude Code and explore its capabilities firsthand. I was particularly interested in how my coding efforts might benefit from:

  • Parallelism (agents working through separate tasks concurrently, as opposed to an individual task being worked through one step at a time via a chat interface like ChatGPT)
  • Autonomous task completion (e.g. via sharing requirements with an AI, having the AI write tests in accordance with said requirements, having the AI execute the aforementioned tests and iterate on implementation approaches until all relevant tests are passing)
  • Providing AI with expanded codebase context (providing an AI access to, except where I restrict things purposefully, an entire project rather than just snippets that I manually paste into a chat interface)
  • Providing AI with a wider range of tooling capabilities, as compared to what it has access to via a traditional chat interface (empowering the AI to execute scripts, capture and analyze screenshots across window sizes, directly edit files, create and analyze browser logs, download and make use of designated third-party packages, take advantage of MCPs, etc)

I assigned Claude Code some tasks and put it to work on some portfolio site improvements that I had been meaning to get around to. I was stunned by

  • How significantly the aforementioned agentic capabilities impacted my development experience.
  • Claude Code's performance as measured by task completion quality and speed.

Claude Code has since become a tool that I use every day, and agentic AI systems have dramatically changed my approach to writing code, building software, and to completing work more generally.

What exactly are AI agents—and what does "agentic AI" mean?

I'll include some relevant definitions from OpenAI and Google below. OpenAI defines agents as:

systems that independently accomplish tasks on your behalf. A workflow is a sequence of steps that must be executed to meet the user's goal, whether that's resolving a customer service issue, booking a restaurant reservation, committing a code change, or generating a report.

...an agent possesses core characteristics that allow it to act reliably and consistently on behalf of a user:

  1. It leverages an LLM to manage workflow execution and make decisions. It recognizes when a workflow is complete and can proactively correct its actions if needed. In case of failure, it can halt execution and transfer control back to the user.
  2. It has access to various tools to interact with external systems—both to gather context and to take actions—and dynamically selects the appropriate tools depending on the workflow's current state, always operating within clearly defined guardrails.

Google defines agentic AI as:

an advanced form of artificial intelligence focused on autonomous decision-making and action. Unlike traditional AI, which primarily responds to commands or analyzes data, agentic AI can set goals, plan, and execute tasks with minimal human intervention. This emerging technology has the potential to revolutionize various industries by automating complex processes and optimizing workflows.

How does using agents differ from using ChatGPT?

Let's use an analogy to illustrate the difference.

Baking a cake without agentic tools

  • A human describes their desired result to ChatGPT. ChatGPT replies with step-by-step instructions that the human is responsible for executing. The human procures the ingredients, preps the kitchen, completes all required measuring and mixing, operates the oven and oversees the baking process, and completes all required cleanup.
  • If something goes wrong, the human describes the problem to ChatGPT, ChatGPT provides feedback, and the human takes action in accordance with said feedback.

In this example, AI (ChatGPT as a traditional AI chat interface) serves as a guide and provides direction - a human is required to execute the AI's instructions. In this example the AI is reactive (it replies to prompts), it does not actively monitor progress and it generates text as its output.

Baking a cake with agentic tools

  • A human shares their desired result with an agent, and provides the agent with access to all relevant tools (e.g. car keys for driving to the store, a credit card for purchasing groceries, an alarm code for entering the house). The agent takes things from there and identifies viable recipes, procures the ingredients, and completes all required prep, baking, and cleanup.
  • Before the agent presents the finished cake to the human, it validates that the cake meets the success criteria that were defined by the human. If during its testing the agent identifies any issues, at that point the agent will iterate until the cake satisfies all success criteria.

In this example, AI completes work autonomously, it actively monitors progress and makes adjustments as needed, and generates a cake that meets the specified success criteria as its output.

The Goldman Sachs CIO on their first experience using "one of the new agentic coding tools"

Early models, for all their fluency, felt like they were operating with a kind of impostor syndrome, optimized for credibility over correctness. But then came the inflection point, a step-change brought about by a new class of "reasoning" models. Suddenly, we weren't just conversing with a statistical machine that predicted the next word in a sequence; we were getting a peek into a nascent form of cognition.

The first time I experimented with one of the new agentic coding tools, I felt that familiar spark of magic. I tasked it with a personal project I'd never found the time for: migrating a charity website from a simple web builder to a proper, modern CI/CD environment. For the next twenty minutes, it went to work, asking clarifying questions, requesting credentials, and providing status updates. It felt less like using a tool and more like collaborating with a junior developer. When it presented me with a fully deployable package, complete with impeccable documentation and unit tests, I was floored.

Of course, it wasn't perfect. It made mistakes. It got stuck. It required my supervision and, crucially, my judgment to steer it back on course. The experience drove home a lesson I've learned the hard way over a long career: you cannot afford to trust blindly. Yet, the process was fascinating. Peeking into its "chain of thought" was like watching a mind at work—messy, non-linear, full of starts, stops, and self-corrections, not unlike our own human reasoning. It wasn't a straight line; it was a random walk toward a solution. Here was the kernel of something new: not just an intelligence that could generate content, but one that could generate a plan.

This is the promise of agentic frameworks. It's the difference between a static subway map and a dynamic GPS that reroutes you in real-time. A classic rules-based automaton follows a fixed path; when it encounters an unexpected obstacle, it breaks. An AI agent, powered by a reasoning model, has the potential to observe, adapt, and find another way. It possesses a form of digital common sense that allows it to navigate the countless edge cases of reality. It represents a shift from simply telling a computer "what" to do, to explaining "why" we need something done and trusting it to figure out the "how."

— Marco Argenti, CIO of Goldman Sachs, excerpted from Antonio Gullí's "Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems"

Further reading

Because there is broad consensus that agentic AI qualifies as a paradigm shift and a historically significant innovation, fortunately there is no shortage of high-quality agentic systems-related learning material for anyone looking to get up to speed. I'll share some resources below for both engineers and non-engineers.

An accessible introduction to Claude Code and agentic coding for non-engineers

I thought that this "Claude Code for Non-Coders | A Complete Beginner's Guide" YouTube tutorial was notably well done (topics covered, pace, structure, thoroughness, ease of understanding, audio quality). It walks viewers through installing and setting up the software that is required to take advantage of Claude Code (e.g. an IDE like Cursor, Node.js), showcases how to interact with Claude Code using natural language to facilitate your desired result, and demonstrates how to "save progress" to GitHub and deploy an application to publicly accessible URLs via Vercel.

If while watching the tutorial you find yourself confused by any terms or concepts, try sharing a description of what you find confusing with ChatGPT or another LLM. Also please feel free to drop me a line.

Agentic coding resources for folks that have an engineering background

  • I would recommend experimenting with Claude Code if you have not done so already. Here is a link to the Claude Code developer documentation. And here is a link to a free course that Anthropic put together, that "provides comprehensive training on using Claude Code for software development tasks, covering the underlying architecture of AI coding assistants, practical implementation techniques, and advanced integration strategies."

    For reference, Anthropic is the company that created and maintains Claude Code. In September 2025 Anthropic announced a $13B raise at $183B post-money valuation. Anthropic is considered one of the leading AI labs, as measured by e.g. model performance and product innovation.

  • I haven't completed this "Agentic AI" course but decided to share it here anyways. It was created by Andrew Ng. Andrew founded Coursera (NYSE: COUR), was the Director of the Stanford AI Lab, and started the Google Brain team. The course is intended to provide "a fundamental understanding and practical knowledge to develop production-ready agentic applications, from design patterns to deployment and evaluation."

Some agent-focused pages from leading tech companies

Amazon, Google, IBM, Meta, Microsoft, NVIDIA, OpenAI, Oracle, Salesforce, Snowflake

Additional resources

Thank you for your time

If you have any questions or want to connect on anything that I wrote about above, please email me or book some time on my calendar. Any and all feedback is of course so appreciated.