Diego Pacheco Tech blog

People building agents today are mostly doing one-shot. Meaning they write one and that's it. Yesterday, I was watching the YC Lightcone podcast: "Inside Claude Code With Its Creator Boris Cherny" and one of the things Boris, creator of Claude Code and head of Claude Code in anthropic, said is that they delete the CLAUD.MD a lot because they want the new models to take over. That insight tells us a lot that we cannot just settle for whatever prompts we have. Besides that, depending on how we write the prompt, we might use more or fewer tokens; there are ways to better structure agents, workflows, and skills. For this blog post, I will cover some lessons learned while building and improving agents, workflows, and skills. I did a bunch of experiments; in fact, I wrote 7 incarnations of my agent skill. To test the agent's skill, I asked the agent to build a Twitter-like application so I could evaluate the quality of the code and solution as a proxy for the agent's s...

AI coding Agents like Claude Code , OpenAI Codex , and Gemini CLI have disrupted how software engineering is done. IMHO, the most disruptive agents are Claude code and Codex. However, a lot of things already happened, some progress has been made, and there is some evolution in the space. We saw the birth of custom and subagents to avoid passing the whole context window down, custom commands  to have more control over a workflow, or when a specific task is executed. Hooks  add more determinism and make sure tests and linters are executed as part of the guardrails. From the explosion of MCPs to Multi-Agent Systems. There are many interesting changes and evolutions happened, we learned somethings while some things are still to be learned. For this blog post, I will cover some of the evolution in AI coding agents (mainly around Claude code). I did a lot of POC with agents, 74 Agent-related POCs at the moment. One thing I keep saying is that POCs are getting expensive, now not ...

The One does not simply use AI Agents in production. Before using AI agents in production, we need to understand that LLMs are token prediction machines and by nature are non-deterministic . No matter how good you specs are, AI will drop packages and make mistakes. Lack of determinism is just one aspect we need to keep in mind. We also need to keep in mind that it's very easy to jailbreak the models . Adding a chatbot directly to customers has dangers and not only in a security sense, but also for misuse and potentially legal problems. Even if that is all somehow managed and risk is minimized with proper guarantees, one still does not just use agents in production. 20-15 years ago, we would not just deploy APIs to production; we would use an API Gateway. Considering agents and LLMs, we need the same: an AI gateway infrastructure. What happens if your API provider (Anthropic, Google, or OpenAI, for instance) is down? Is your business down? 

Imagine you are coaching a basketball team. You want to train your team to be good at 2-point shooting from the inside. Now imagine for some weird reason you can't test that, and you need to play a whole 4 quarters basketball game in order to be able to maybe, with a lot of luck, score 2 points. That would suck, right? This actually sounds insane because we all know we can skip the whole game and just train 2 points from inside right? Well, what IF I told you the basketball game is often a people test software, and they cannot train exact scenarios (State Induction), and they actually need to test the whole thing (expensive E2E testing). What if we could write tests in a very different way, so that it would allow us to have massive parallelism, and perhaps multiple people could test the same thing at the same time, and it would work.

From time to time, the industry has a breakthrough, and things change. Sometimes the improvement is incremental, and other times it is very disruptive. Not all change stays forever; actually, new technologies tend to die sooner than old inventions like dishes, forks, knives, spoons, glasses, and many more. I remember web services dominating the corporate universe until rest came and put them to almost extinction. I remember EJB rise and fall like a flash, Netscape, and many others. Those transformations are not new, and they do happen from time to time. Before AI, we had other movements and other breakthroughs like DevOps, Cloud Computing, Agile, Mobile Phones, the Internet, and the Personal computer. AI, perhaps, is the most disruptive force we have seen so far. No other technology or movement has such mystique as AI does. Some call it the Genie; others, the Revolution of the machines (Skynet); others think it's AGI already. One interesting effect we see at this point is that AI b...

For a long time, software engineering had a stable way of doing software. Yes, not all companies had the same level of maturity and properly digested previous movements like Lean, Agile, and DevOps. However, right now we are living in a quite interesting time when we are reconsidering what we know and what worked in the past in favor of perhaps a better way to build software. Fully acknowledge that we don't have the answer to what the winner is yet. Many industries are being disrupted by AI, but no industry is being more disrupted than technology itself. We never saw anything like what we are seeing right now. The only way to go forward right now is to experiment, read, socialize, and reflect on what works and what does not work. 

AI might reshape how engineering works; there might be different workflows and different ways of shipping software. We have good and solid foundations in engineering, which are still relevant and can still guide us even in AI agentic times. If we go back more than 30-20 years, the traditional way of building software was highly influenced by RUP . From RUP come phases, roles, responsibilities, and structure. However, there was also waste, handoffs, a lack of focus on coding, and even a waterfall approach. After the agile movement, we learned that software could be built more effectively. Before we even start talking about AI and agents, we need to understand that companies have different levels of maturity and might not be all up to date with previous waves of innovation, such as Lean, Agile, DevOps, Lean Startup, and now AI Agentic Engineering. IF we look at the last almost 30 years of software engineering, we can notice a time called shift left. The idea is to pay more attention to q...