AI Agents: Loop, SubAgents, Communication, Observability
How AI Agents Run, Talk, and Get Watched
This week was all about AI Agents: how a single one runs, how many of them split work, how they talk to each other, and how we see inside them.
Let’s get started.
1. AI Agent Loop
Every AI Agent has an engine that keeps it going. That engine is the loop.
AI Agent Loop = Think to Act to Observe, repeat until the goal is achieved.
The LLM is the brain. The loop is the runtime that turns the brain’s decisions into real actions. The LLM does not call tools itself. The loop does. The LLM only decides which tool to use and what inputs to pass.
Every turn follows the same three-beat cycle: Think (the LLM decides), Act (the loop runs the tool), Observe (the result feeds back). Then it repeats.
Think, Act, Observe is the heartbeat of every AI Agent.
The AI Agent Loop is a small piece of code, about 20 lines, but it is the piece that turns a one-shot LLM into a system that can pursue a goal on its own.
Read it here: https://outcomeschool.com/blog/ai-agent-loop
2. AI SubAgents
One agent can only do so much. When a task gets big, we split it.
An AI SubAgent is a smaller, specialized agent that works under a main agent to handle a specific part of a larger task.
In simple words, the main agent is like a manager, and the SubAgents are like team members. Each team member is an expert at one specific thing. The manager decides who should do what, and then collects the results.
Why do we need them? A single agent can try, but it will struggle as the task becomes bigger: context overload, lack of focus, and no parallel work. So, here come the SubAgents to the rescue.
Read it here: https://outcomeschool.com/blog/ai-subagents
3. How AI Agents Communicate
Once we have many agents, they must talk to each other.
Agent communication is the way two or more AI agents send messages to each other to share information, ask for help, and work together to finish a task.
There are four main ways AI agents communicate:
Direct Communication: One agent talks straight to another agent.
Centralized Communication: A main agent sits in the middle and routes all messages.
Broadcast Communication: One agent sends a message to many agents at once.
Shared Memory Communication: Agents talk through a common memory instead of direct messages.
Without communication, every agent works alone in its own box. With communication, agents become a team that can solve much bigger problems together.
Read it here: https://outcomeschool.com/blog/how-ai-agents-communicate
4. AI Agent Observability
Agents take many hidden steps before giving us the final answer. We need a way to see inside.
AI Agent Observability is the practice of recording and understanding everything an AI Agent does internally, step by step, so that we can see why it behaved the way it did.
Think of it like a black box flight recorder in an airplane. The black box records everything during the flight. If something goes wrong, we open the black box and understand exactly what happened. AI Agent Observability is the black box for our agent.
And do not confuse it with evaluation:
Observability tells us what the agent did. Evaluation tells us how good it was.
Read it here: https://outcomeschool.com/blog/ai-agent-observability
So, the loop runs one agent, SubAgents split the work, communication connects them, and observability lets us see it all.
That’s it for this week.