Building the Agent’s “Brain” and “Nervous System”
In Part 1, we overturned traditional AI Agent perceptions, establishing that “Micro Agent” is the correct path to production-grade applications. This thinking sparked heated discussion. But a deeper question immediately followed: The philosophy is great, but how should a reliable Micro Agent’s internals be designed? And how do we avoid falling into another “black-box framework” trap in practice?
Today, Tam will reveal the core technology for building “Micro Agents.” He compares internal structure to an organism with “brain” and “nervous system.” The “brain” handles thinking and decision-making; the “nervous system” handles perception and control. As developers, we must understand and personally build these two core components.
Let’s look at how Tam is building this digital mindset.
- by Tam -
Chapter 1: The Agent’s “Brain”—Precise Decision Core
The “brain” is the Agent’s decision center, responsible for transforming abstract goals into specific, executable action intentions. Its core task: make one and only one correct decision at every moment.
1.1 From Natural Language to Structured Intent
The brain’s most basic function is “translation.” It receives humans’ fuzzy natural language instructions and outputs structured JSON that machines can understand. For example, receiving “Create a $750 payment link for Terri” should output:
{
"intent": "create_payment_link",
"parameters": {
"amount": 750,
"customer": "cust_128934ddasf9"
}
}The key: this JSON itself is a “Tool Call.” It’s merely a description of action intent, not an actual function. This achieves perfect decoupling between LLM’s “decision” and our subsequent deterministic code’s “execution.”
1.2 Treat Prompts as the “Brain’s Operating System”
If JSON is the “brain’s” output instructions, then Prompts are its built-in, immutable “operating system” or “constitution.” We must have absolute control over it, not outsource to black-box frameworks. A good Prompt defines the Agent’s role, capabilities, thinking style, and behavioral boundaries.
# SYSTEM PROMPT for deploybot
You are a helpful assistant responsible for managing deployments.
You ensure safe deployments by following best practices and correct procedures.
You can use tools like deploy_backend, deploy_frontend, etc.
For sensitive operations, use request_approval to get human verification.
Always think first, then act...We should version-control, test, and iterate Prompts like managing code—this is the proper approach to building professional-grade Agents.
Chapter 2: The Agent’s “Nervous System”—State Perception and Behavior Control
If the “brain” handles decisions, the “nervous system” handles sensing history, managing state, and precisely converting “brain” decision signals into actual actions.
2.1 Perception and Memory: Carefully Constructed Context Window
Agents “perceive” the world through context windows. We shouldn’t limit ourselves to standard message formats from model providers, but through “context engineering,” construct custom formats (like XML/YAML style) with higher information density, improving efficiency and model performance.
More importantly, all this context should come from a unified event stream (Thread). This event stream records complete history from initial instructions through every tool call and every error—the Agent’s “long-term memory” and system’s sole “source of truth.” This dramatically simplifies state management, making pause, resume, and debugging effortless.
2.2 Behavior Execution: Self-Controlled Core Loop
This is the central nervous system connecting “brain” to “action.” We must write the Agent’s core loop ourselves, not call a packaged agent.run(). This loop’s essence is a switch statement deciding based on the “brain’s” returned intent.
while True:
next_step = await determine_next_step(thread)
if next_step.intent == 'fetch_data':
result = await fetch_data_tool(next_step.parameters)
thread.add_event(result)
continue # Continue loop, let AI make next decision
elif next_step.intent == 'request_human_approval':
await notify_human(next_step)
await save_thread_to_db(thread)
break # Break loop, wait for external event to resumeBy self-controlling this loop, we achieve differentiated control logic: “continue” for simple tasks, “break” for complex tasks requiring human input, and insert “pre-approval” steps before high-risk operations.
Conclusion: You’re Not AI’s User—You’re Its Architect
Reviewing this article, through combined application of core “12 Factors” principles, we personally built a Micro Agent’s “brain” and “nervous system.” By owning Prompts (legislative), Context (judicial), and Control Flow (executive)—these three powers—we transform Agents from mysterious black boxes into predictable, controllable, trustworthy system components.
This mindset shift is crucial: we’re no longer passive “users” of AI capabilities, but proactive “system architects” wielding AI’s power.
With this, we’ve built a complete, independent Micro Agent. But in the real world, it can’t be an island. In the series finale, we’ll explore how to take this Agent out of the lab, efficiently collaborate with humans and other systems, and handle various exceptions to become a truly “robust” member in production environments. Stay tuned for the “Practice Edition.”
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