Development on EnRedAndo Me - Carlos Prados

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.

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.

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.