Langgraph 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.

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.

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.