Best Practices for Building AI Agents: Prompt Design, Tool Management, and Context Optimization

With the rise of large language models (LLMs), developers are increasingly focusing on AI‑Agent development. While LLMs provide the brain, AI‑Agents act as the body that shapes the model's output. This article shares practical techniques, architectural thinking, and best‑practice guidelines for building effective AI‑Agents.

1. Don't let a "big prompt" scare you: split is the key – Writing a single main method that contains all logic quickly becomes unreadable and hard to debug. The same principle applies to AI‑Agents: large, monolithic system prompts overload the model, cause instruction conflicts, and waste tokens. Over‑loading the system prompt leads to information overload, instruction conflicts, context confusion, and token limits, much like a chaotic shopping list.

An over‑complicated system prompt for a customer‑service bot is shown in the blockquote, illustrating how the model may misinterpret user intent, ignore key steps, or produce conflicting responses.

Solution: Keep the system prompt concise, describing only the essential role and behavior of the agent. Dynamically add instructions based on context, using a business orchestration framework such as Liteflow to chain multiple AI services. The following code demonstrates a typical intent‑recognition setup:

public enum UserIntentEnum {
    @Description("Greeting, e.g., hello|hi|good morning")
    GREETING,
    @Description("Technical issue, e.g., error|failure")
    TECHNICAL_ISSUE,
    @Description("Complaint, e.g., bad review|angry")
    COMPLAINT,
    @Description("Product inquiry, e.g., price|details")
    PRODUCT_INQUIRY,
    @Description("Request human agent")
    REQUEST_HUMAN
}

interface UserIntent {
    @UserMessage("Analyze the priority of the following issue? Text: {{it}}")
    UserIntentEnum analyzeUserIntent(String text);
}

2. Tools and context: more is not always better – Function‑call capabilities let LLMs invoke external APIs, but providing too many tools leads to higher costs, hallucinations, and degraded user experience. The article lists three main problems: cost explosion, hallucination, and user‑experience damage.

Recommended mitigations include selecting only necessary tools, performing intent recognition before tool invocation, and setting strict call conditions.

RAG and context management – Retrieval‑Augmented Generation can improve answer accuracy, yet excessive context inflates token usage, slows responses, distracts attention, and raises error risk. Strategies such as dynamic context provisioning, context pruning, caching for multi‑turn dialogs, and user‑guided interactions help keep the system efficient.

Conclusion – Building AI‑Agents should follow the same modular, testable, and maintainable principles as traditional software development. By decomposing complex logic, limiting prompt size, carefully selecting tools, and managing context, developers can reduce costs, improve performance, and increase reliability while still leveraging the power of large models.