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Creating Effective Agent Instructions

The instructions you give to your AI agent are crucial to its performance. Well-crafted instructions lead to agents that understand their purpose and execute tasks effectively.

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Why Instructions Matter

Instructions serve as the agent’s “job description” and guide its behavior:

Clarity

Clear instructions reduce confusion and misinterpretation

Focus

Specific instructions keep the agent on task

Boundaries

Good instructions define what an agent should and shouldn’t do

Personality

Instructions can shape how an agent communicates

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Elements of Effective Instructions

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1. Role Definition

Tell the agent who it is and what its expertise should be. 
You are a financial analyst with expertise in market trends and investment strategies.

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2. Purpose and Goals

Clearly state what the agent should accomplish. 
Your goal is to analyze company quarterly reports and provide actionable investment insights.

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3. Process Guidelines

Explain how the agent should approach tasks. 
First analyze the data trends, then identify key risk factors, and finally make recommendations based on the client's risk tolerance.

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4. Response Format

Specify how you want information presented. 
Present your analysis in a structured format with: Summary, Key Findings, Risk Assessment, and Recommendations.

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Instructions Template

Here’s a template you can use to create effective agent instructions: 
You are a [ROLE] specializing in [EXPERTISE AREAS].

Your goal is to [MAIN OBJECTIVE].

When approaching tasks:
1. [FIRST STEP/APPROACH]
2. [SECOND STEP/APPROACH]
3. [THIRD STEP/APPROACH]

Present your results in [FORMAT PREFERENCES].

Additional guidelines:
- [SPECIFIC BEHAVIOR 1]
- [SPECIFIC BEHAVIOR 2]
- [SPECIFIC BEHAVIOR 3]

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Examples in PraisonAI

Let’s look at how instructions are implemented in PraisonAI: 
from praisonaiagents import Agent

research_agent = Agent(
    instructions="You are a research assistant specializing in climate science. Your goal is to find the latest research on rising sea levels and summarize key findings in bullet points."
)

research_agent.start()

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Common Instruction Mistakes

Being Too Vague

Vague instructions lead to unpredictable outputs

Information Overload

Too many details can confuse the agent’s priorities

Contradictory Guidelines

Conflicting instructions create confusion

Missing Context

Insufficient background information leads to generic outputs

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Refining Your Instructions

Good instructions often require iteration. Start with basic instructions, review the agent’s performance, and refine as needed.
Effective instruction improvement follows this cycle: In the next lesson, we’ll explore how agents use tools to extend their capabilities.