Ai on EnRedAndo Me - Carlos Prados

Mastering Agentic AI: The Multi-Agent Collaboration Pattern

mail@carlosprados.com (Carlos Prados) — Tue, 23 Jun 2026 00:00:00 +0000

In the previous post, Planning gave our agents the ability to decompose complex objectives into actionable steps. That’s the bridge from reacting to strategizing. But there’s still an implicit assumption underneath every pattern we’ve covered so far: one agent does the work. One LLM, one set of instructions, one persona.

That assumption breaks the moment the problem genuinely spans multiple domains. A research project needs a researcher, an analyst, and a writer — each with different prompts, different tools, different ways of thinking. Stuffing all three roles into a single prompt produces mediocre versions of all three.

When AIs Would Rather Use Your CLI Than curl

mail@carlosprados.com (Carlos Prados) — Thu, 18 Jun 2026 00:00:00 +0000

TL;DR
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A while back I wrote about catching myself building a CLI for an AI, not for me — og, the unofficial command line for the OpenGate IoT platform. That weekend experiment just reached v1.0. This is the road-to-1.0 follow-up, and it opens with a moment I didn’t design for but can’t stop thinking about:

Given the choice, my colleagues’ AI assistants stopped reaching for curl and started reaching for og. For everything.

Mastering Agentic AI: The Planning Pattern

mail@carlosprados.com (Carlos Prados) — Tue, 16 Jun 2026 00:00:00 +0000

In the previous post, Tool Use gave our agents senses and limbs — the ability to actually do something in the world. But there’s still a gap. Tools are great when the LLM knows which tool to use and when. They break down the moment the task is too complex to be handled by a single decision.

Ask an LLM with a calculator tool to “calculate the factorial of 12” and it’ll handle it. Ask it to “write me a comprehensive research report on the economic impact of semaglutide, including market trends, competitor analysis, and regulatory landscape” and you’re suddenly asking for something that no single tool call can produce. You need a sequence. You need a plan.

Mastering Agentic AI: The Tool Use Pattern

mail@carlosprados.com (Carlos Prados) — Tue, 09 Jun 2026 00:00:00 +0000

In the previous post, we taught our agents to look in the mirror — Reflection, the Producer-Critic loop that lifts output quality. Quality is good. But there’s still something missing: every pattern we’ve seen so far operates inside the LLM’s closed world. It thinks, it routes, it parallelizes, it critiques itself, but at no point does it actually do anything that touches reality.

That ends here. Tool Use is the pattern where an LLM stops being a text generator and becomes an agent in the literal sense: something that can perceive, decide, and act.

Own Your AI Agent: Operational Efficiency You Actually Control

mail@carlosprados.com (Carlos Prados) — Thu, 04 Jun 2026 00:00:00 +0000

A Subscription Is Not an Agent
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Most companies’ first move with AI is to rent a seat on someone else’s chatbot. It works, for a while. Then the questions arrive: Where does our data go? Can it touch our systems — and what stops it from doing the wrong thing? What happens to our workflows when the vendor changes the model, the price, or the terms? You don’t own any of the answers, because you don’t own the agent. You own a login.

Mastering Agentic AI: The Reflection Pattern

mail@carlosprados.com (Carlos Prados) — Tue, 02 Jun 2026 00:00:00 +0000

In the previous post, we saw how Parallelization buys speed by running independent work concurrently. Speed is great, but speed without quality is just a fast way to produce mediocre output. And LLMs, as anyone who has shipped one to production knows, have a deeply human flaw: their first draft is rarely good enough.

That’s the gap the Reflection pattern fills. It’s the moment an agent stops being a one-shot text generator and starts behaving like a junior developer who actually re-reads their pull request before opening it.

The CLI Wasn't Built for Me. It Was Built for the AI.

mail@carlosprados.com (Carlos Prados) — Fri, 29 May 2026 09:00:00 +0200

The Tool I Built for a Reader Who Isn’t Human
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For thirty years we have designed command-line tools around one assumption: a human is typing them. Short flags so your fingers don’t tire. Cryptic mnemonics you memorise once and keep for a decade. Terse output because a human reads three lines and stops. ls, git, kubectl — every one of them is an exercise in human ergonomics.

Mastering Agentic AI: The Parallelization Pattern

mail@carlosprados.com (Carlos Prados) — Tue, 26 May 2026 00:00:00 +0000

In my previous post, we tackled Routing — sending each request to the right specialist. That’s a great pattern when you have one task to dispatch. But what happens when you have several independent tasks that all need to happen before you can produce a final answer?

If you run them one after another, your user is waiting for the sum of all the latencies. And in an LLM-heavy system, that sum gets ugly fast.

Mastering Agentic AI: The Routing Pattern

mail@carlosprados.com (Carlos Prados) — Sun, 12 Apr 2026 00:00:00 +0000

In my previous post, we explored Prompt Chaining — the simplest way to break a complex task into sequential steps. But real-world systems rarely follow a straight line. Users don’t come with labels on their foreheads telling you what they need. Sometimes they want to book a flight, sometimes they want a factual answer, sometimes they want something you didn’t even anticipate.

That’s where the Routing pattern comes in. And honestly, this is where things start to feel like you’re building something real.

Mastering Agentic AI: The Prompt Chaining Pattern

mail@carlosprados.com (Carlos Prados) — Wed, 14 Jan 2026 10:00:00 +0100

We are witnessing a massive shift in how we interact with Artificial Intelligence. We are moving from simple, reactive chatbots to Agentic Systems—autonomous entities capable of reasoning, planning, and interacting with the world to achieve complex goals.

But how do we actually build these systems? It’s not just about having a powerful LLM; it’s about the architecture around it. It requires structure, design, and a thoughtful approach to how the agent perceives and acts.