This guide explains how to structure a production-ready AI agent using the Learn–Teach–Master architecture.

Implementing an AI agent microservice requires a shift from traditional "if-then" programming to an orchestrator-worker pattern. Below is a comprehensive developer's guide to implementing this framework.

Developer Guide: Building Modular AI Agents

This guide explains how to structure a production-ready AI agent using the Learn–Teach–Master architecture.

1. Core Philosophy: The Orchestrator Pattern

Instead of writing a giant script, we separate concerns into four distinct layers. This allows a developer to swap a model (e.g., from OpenAI to Gemini) or a tool (e.g., from Search to Database) without breaking the system.

| Layer | Component | Developer Responsibility |

|---|---|---|

| Interface | api/routes.py | Handles HTTP requests and response schemas. |

| Brain | core/orchestrator.py | Logic for "Thinking": Planning, Refinement, and Routing. |

| Capabilities | tools/ | Python classes that give the agent "hands" (e.g., API clients). |

| Engine | providers/ | The bridge to the LLM (LiteLLM abstraction). |

2. Implementation Steps

Step A: The Engine (LLM Abstraction)

We use LiteLLM because it provides a unified interface. A developer only needs to change the model string to switch between Bedrock, Azure, or Gemini.

# app/providers/llm_provider.py

from litellm import completion

class LiteLLMProvider:

def __init__(self, model="gpt-4o-mini"):

self.model = model

def generate(self, prompt: str):

# The 'completion' call is standardized for 100+ LLMs

response = completion(model=self.model, messages=[{"role": "user", "content": prompt}])

return response.choices[0].message.content

Step B: The Brain (The Orchestrator)

The Orchestrator manages the Agentic Loop. In a more advanced version, this is where you implement "Reflection" (where the agent checks its own work).

# app/core/orchestrator.py

class AgentOrchestrator:

def __init__(self, llm_provider, tools=None):

self.llm = llm_provider

self.tools = tools or []

def handle(self, prompt: str):

# 1. ENRICH: Add system instructions or context

enriched_prompt = f"System: You are an expert assistant. User: {prompt}"

# 2. THINK/ACT: Get response from LLM

response = self.llm.generate(enriched_prompt)

# 3. REFINE: Clean the output or check for errors

return response.strip()

Step C: The Interface (FastAPI)

By wrapping the agent in FastAPI, we make it a Microservice. Other services (written in Java, Go, etc.) can now use your Python agent.

# app/api/routes.py

@router.post("/generate")

async def generate(request: PromptRequest):

# Injection: We create the provider and pass it to the orchestrator

llm = LiteLLMProvider(model="gemini/gemini-1.5-pro")

orchestrator = AgentOrchestrator(llm_provider=llm)

return {"response": orchestrator.handle(request.prompt)}

3. Advanced Feature Checklist for Developers

To move from a "starter kit" to a "production agent," a developer should implement these three features:

* Tool Use (Function Calling): Use the LLM to decide when to call functions in app/tools/.

* Short-Term Memory: Pass the last 5 messages back to the LLM so it remembers the conversation context.

* Observability: Add logging to app/core/orchestrator.py to track why the agent made a specific decision.

4. Why This Structure?

* Scalability: You can deploy 10 instances of this agent in Docker/Kubernetes.

* Testability: You can write unit tests for the Orchestrator without needing to call the real LLM (by mocking the LLMProvider).

* Agility: When a better model comes out next week, you only change one line in your config.

This Python AI Agent tutorial provides a practical walkthrough of building and deploying similar agentic structures.

This video is highly relevant because it covers the end-to-end process of writing an agent and setting up the API layer, which directly complements the architectural steps outlined above.