Alright, now that we've covered the basics-what an agent is, how it works, the models, tools, and instructions-this chapter will bring it all together. As always, I’ll try to keep things as simple as possible.
Once you know what model (or models) you want to use, the next step is figuring out what tools your agent will use and defining how it will operate using clear instructions.
(We’ll build a sample model in the upcoming chapter to make this even more practical.)
Orchestration: What Does It Really Mean?
Let’s simplify this word: Orchestration is just a fancy way of saying “coordinating how different systems or tasks work together,” often with some automation involved.
In our case, orchestration means: How, what, and when your agent does something.
Now, orchestration in agent systems usually falls into two buckets:
- Single-agent systems – One model does everything in a loop.
- Multi-agent systems – Multiple agents work together, each doing their part.
Single-Agent System
Think of this like a clean input-output system.
You give it an input → the agent processes it based on its instructions → it uses the right tools → passes through guardrails → and gives you the output.
Straightforward. This works well for less complex use cases.
Example: A chess engine like Stockfish or AlphaZero is a classic single-agent setup. It looks at the board and decides the next best move-all on its own, no collaboration needed. A linear system. Simple, effective.
Multi-Agent System
Now this is where things get a