Agent Development

Building an AI agent involves more than just prompting a Large Language Model (LLM). It requires a systematic engineering approach to design, implement, and refine the agent's behavior.

The Development Lifecycle

1. Define the Agent's Role and Scope

Before writing code, clearly define what the agent should do.

• Goal: What is the primary objective? (e.g., "Schedule meetings," "Write code," "Analyze data.")
• Persona: What is the tone and style?
• Constraints: What should the agent not do?

2. Design the Architecture

Choose the right cognitive architecture for the task.

• Single Prompt: For simple, one-shot tasks.
• Chain of Thought: For tasks requiring reasoning.
• ReAct (Reason + Act): For agents that need to use tools and interact with the environment.
• Multi-Agent: For complex workflows best decomposed into sub-tasks.

3. Tool Definition

Agents need tools to interact with the world. Define the interfaces (APIs) the agent can call.

• Search: Web access for up-to-date info.
• Database: efficient retrieval of structured data.
• calculator: Precise math operations.
• Custom Functions: API calls specific to your business logic.

4. Prompt Engineering

Crafting the system prompt is a critical step.

• Role Priming: "You are an expert software engineer..."
• Instruction Tuning: Clearly numbered steps for execution.
• Few-Shot Prompting: Providing examples of desired input-output pairs.

5. Memory Management

Decide how the agent handles context.

• Short-term Memory: The context window of the current session.
• Long-term Memory: Vector databases (RAG) to store and retrieve information across sessions.

Core Components

class Agent:
    def __init__(self, name, tools, memory):
        self.name = name
        self.tools = tools
        self.memory = memory
        self.llm = LLMClient()

    def run(self, user_input):
        # 1. Retrieve relevant context
        context = self.memory.search(user_input)

        # 2. Plan (Reasoning)
        plan = self.llm.generate_plan(user_input, context)

        # 3. Execute (Action)
        result = self.execute_tools(plan)

        # 4. Reflect (Critique)
        final_answer = self.llm.synthesize(result)
        
        return final_answer

Common Challenges

• Hallucination: The agent inventing facts. Mitigation: Grounding in retrieved data (RAG).
• Looping: The agent getting stuck in a repetitive cycle. Mitigation: Max iteration limits and specific stop conditions.
• Context Window Limits: Running out of space for conversation history. Mitigation: Summarization and selective context injection.