Inside an agent’s brain: Why AI Needs Open Orchestration

#ai #architecture #powerfuldevs #opensource

Okay, let’s start with a confession. I was at an AI and agents event earlier this year, and they put on a movie night. The film was The Matrix. And… it was the first time I’d ever seen it (for real). In The Matrix, there’s this part where they explain how humans built intelligent systems that eventually took over — and now those systems show up as the “agents.” 👀

What was pure sci-fi then is suddenly becoming our reality. We’re all building the first generation of these agents right now, and it’s clear this new “agentic era” is going to change everything.

But let’s be honest: building these agents today is one of the coolest things a developer can do, but it also feels… a bit stuck, right? Let’s dive into it.

The Agentic Problem

You’ve created this amazing automation process with agents, but it only speaks one language, only works with one specific AI brain, and can’t play nicely with other agents. It’s like you built a superhero who’s grounded for life. And the problem continues:

A new, shiny AI model comes out. It’s faster, cheaper, and everyone’s talking about it. You want to upgrade your agent’s brain, but… you can’t. It’s so tangled up in the old model that switching would mean starting over from scratch. Ouch.

Your agent needs a friend. You want your new booking agent to chat with your friend’s scheduling agent. But they were built in different “universes,” so they can’t talk to each other without a bunch of clunky, custom-built translation gadgets that are guaranteed to break.

Your agent does something weird. It just ignored a command or used the wrong tool. Why? Who knows!

We’re accidentally building our agents inside digital cages or commonly called black boxes. We get a little convenience upfront, but we lose all our freedom. It’s a problem, and as a fellow builder, I know how much that stings.

How Agent’s brain work

First, to start opening this black box, we need to start understanding how agents work, so what actually happens inside an agent’s “brain”? It’s a process. And understanding that process is the key to unlocking their true potential.

At its core, every AI agent runs on a loop, often called an “orchestration loop.” Think of it as the agent’s decision-making engine. Let’s break it down with a simple task: “What’s the weather in London right now?” (I guess I dont need an agent for that 🌧️ but let’s go!)

This loop has four main stages:

Plan (The “Hmm, what should I do?” stage):

First, the agent looks at your request (“What’s the weather in London?”) and its available tools (e.g., search_web, get_current_weather, calculator). It then formulates a plan. Using a reasoning technique like ReAct (Reason + Act), its internal monologue might look like this:

Thought: The user is asking for the weather in a specific city, “London”. I have a tool called get_current_weather that takes a city as a parameter. I should use that tool.

Action:

Call get_current_weather(city="London")

Tool Selection & Use (The “Let me grab the right gadget” stage)

This is where the agent translates its plan into an actual function call. It needs to know the exact name of the tool (get_current_weather) and the correct parameters (city=”London”). It then executes that function.

# Pseudocode for what the agent executes weather_api_result = get_current_weather(city="London")

Observe (The “Okay, what happened?” stage)

The tool runs and returns a result. This result is called an “observation.” It might be a clean JSON object or a messy chunk of HTML.

Observation: {"temperature": "18°C", "condition": "Partly Cloudy"}

Reason (The “Let’s make sense of this” stage)

The agent takes this new information (the observation) and feeds it back into its brain. It compares the result to the original goal and decides if the task is finished.

Thought: I have the weather data. The observation is {“temperature”: “18°C”, “condition”: “Partly Cloudy”}. This directly answers the user’s question. I am done.

Final Answer: “The weather in London right now is 18°C and Partly Cloudy.”

This Plan -> Tool Use -> Observe -> Reason loop is the fundamental heartbeat of every modern AI agent.

So, Why is This So Hard in the Real World?

That simple loop looks easy on paper, but when you start building, you hit a wall of frustrating problems, similar to what we mentioned above.

Brittleness: What if the weather API is down? Or returns an error? Your agent’s loop will break unless you’ve written tons of custom error-handling logic.
Lack of Standards: The way you define a tool for one framework is completely different from another. The prompt you use for GPT-4’s reasoning might fail miserably with Claude 3. You end up rewriting the same logic over and over, locking you into one specific setup.
The Model Problem: Because your agent’s core logic — its planning, tool definitions, and reasoning prompts — is tied to one proprietary system, you can’t easily switch models or platforms. You can check out any time you like, but you can never leave.

Universal Translator for Agents

So, how do we fix this? We need Open Orchestration.

Think of it like a universal adapter or a common language that all agents can understand.

Remember how a USB-C cable lets you connect a million different devices? Open orchestration does the same thing for AI agents. It’s a set of simple, open rules for how agents should:

Get and complete tasks.
Use their tools (like searching the web or sending an email).
Talk to each other without getting confused.

When we have this common language, amazing things happen:

Your agent becomes a world traveler. Build your agent’s logic once, and then let it use any AI brain you want — GPT-5, Claude 4, that cool open-source model you found on GitHub. Just swap it out!
Agents can finally team up! Your “shopping agent” can easily work with a “delivery agent” and a “calendar agent” to create a super-powered team. It just works!
No more black boxes. You can see exactly what your agent is thinking and why it’s making its choices. Debugging becomes a fun puzzle instead of a frustrating mystery.

We all build faster, together. We can share and borrow cool new tools and skills for our agents, helping everyone level up. The whole community wins!

Let’s Build This Agentic Era Together!

This isn’t just an idea we’re excited about; it’s what we’re building right now at gen ai works. We believe the future of AI should be open, collaborative, and a ton of fun.

That’s why we’re kicking things off by creating the gen ai works agentic — and we’re open-sourcing it from day one.

This isn’t just another tool. It’s our first draft of that universal translator we all need. We know it’s not perfect, which is why we need you! Come tell us what’s broken, what’s missing, and what would make your life easier.

The AI revolution is here. Let’s make sure we build it on a foundation of freedom and teamwork.

Come hang out with us and let’s get our agents talking!