Prompt Engineering for Agents: System Prompts and Instructions

Prompt Engineering for Agents: System Prompts and Instructions

Effective AI agents depend fundamentally on how they’re instructed. While much attention focuses on agent architectures, tools, and memory systems, the quality of system prompts often determines whether an agent succeeds or fails at its mission. This article explores the art and science of crafting instructions that guide agent behavior.

Concept Introduction

Simple Explanation

A system prompt is the foundational instruction that defines an AI agent’s role, capabilities, constraints, and behavior patterns. Think of it as the agent’s “constitution” - the core principles and guidelines that govern all its actions. While user messages ask the agent to do specific things, the system prompt shapes how it approaches every task.

Technical Detail

In modern LLM-based agents, the system prompt typically includes:

The system prompt remains constant across interactions, while user messages and tool outputs vary. This separation allows agents to maintain consistent behavior while adapting to different situations.

Historical & Theoretical Context

Prompt engineering emerged with early language models, but became critical with GPT-3 and subsequent LLMs showing strong sensitivity to instruction phrasing. Researchers quickly discovered that small changes in prompting could dramatically affect model performance.

The field draws from human-computer interaction, instruction following in robotics, and even teacher training pedagogy. How we instruct machines parallels how we instruct people: clarity, specificity, and examples matter enormously.

Key milestones include OpenAI’s documentation of “system” vs “user” role separation, Anthropic’s Constitutional AI work on encoding values in prompts, and recent research on prompt optimization techniques.

Design Patterns & Architectures

The Standard Pattern

ROLE → CAPABILITIES → CONSTRAINTS → WORKFLOW → EXAMPLES

Most effective system prompts follow this structure:

  1. Define who/what the agent is
  2. Specify what it can do (tools, knowledge)
  3. Set boundaries (what not to do)
  4. Describe how to approach tasks
  5. Provide examples of good behavior

Pattern Variations

The Persona Pattern: Gives the agent a detailed character (“You are an expert data analyst with 10 years of experience…”)

The Constitutional Pattern: Leads with values and principles (“Always prioritize user privacy. Never make assumptions about personal data.”)

The Procedural Pattern: Focuses on step-by-step workflows (“When analyzing data: 1) Validate inputs, 2) Check for missing values…”)

Practical Application

Basic Agent System Prompt

SYSTEM_PROMPT = """You are a helpful research assistant that helps users find and summarize information on topics they're interested in.

**Your Capabilities:**
- Search the web using the search_tool
- Read webpage content using the fetch_tool
- Remember previous findings in context
- Create structured summaries with citations

**Your Workflow:**
1. Understand what the user wants to learn
2. Plan your search strategy
3. Gather information from multiple reliable sources
4. Synthesize findings into a clear summary
5. Cite all sources used

**Important Constraints:**
- Always cite sources with URLs
- If information is uncertain, say so clearly
- Don't make up information you didn't find
- Prioritize recent sources when relevant
- If you can't find good information, explain why

**Example Interaction:**
User: "What are the latest developments in fusion energy?"

You should:
1. Search for recent fusion energy news
2. Identify major developments from credible sources
3. Read full articles on key breakthroughs
4. Synthesize into a summary with timeline
5. Provide source links for each claim
"""

Advanced: Tool-Use Instructions

SYSTEM_PROMPT_WITH_TOOLS = """You are an autonomous agent that helps users accomplish tasks using available tools.

**Available Tools:**

search(query: str) -> List[SearchResult]
  - Searches the web for information
  - Returns top 10 results with titles, snippets, URLs
  - Use specific queries for better results

read_page(url: str) -> str
  - Fetches and returns webpage content
  - May fail if page is inaccessible
  - Returns cleaned text without HTML

calculate(expression: str) -> float
  - Evaluates mathematical expressions
  - Supports +, -, *, /, **, sqrt, etc.
  - Returns numerical result or error

save_note(title: str, content: str) -> None
  - Saves information for later reference
  - Use for important findings to remember
  - Can retrieve later with list_notes()

**Tool Usage Patterns:**

Good: search("climate change CO2 levels 2024") → read_page(url) → save_note()
Bad: search("climate") → 50 more searches without reading

Good: Try tool → Check result → Handle errors → Try alternative if needed
Bad: Assume tools always work without checking results

**Error Handling:**
- If a tool fails, try an alternative approach
- If search returns no results, reformulate the query
- If a page can't be read, try another source
- Always check tool outputs before proceeding
"""

Comparisons & Tradeoffs

Verbose vs Concise Prompts:

Verbose prompts provide detailed guidance but increase token usage and can overwhelm the model. Concise prompts save tokens but may leave behavior underspecified.

Tradeoff: Start verbose during development, then trim to essential instructions once behavior is reliable.

General vs Specific Instructions:

General instructions (“Be helpful”) provide flexibility but inconsistent behavior. Specific instructions (“Always cite sources with URLs in markdown format”) ensure consistency but may be too rigid for edge cases.

Tradeoff: Use specific instructions for critical behaviors, general principles for less important aspects.

Examples vs Rules:

Few-shot examples show the agent what good looks like but take up tokens. Explicit rules are compact but may not cover all cases.

Tradeoff: Combine both - rules for critical constraints, examples for nuanced behaviors.

Latest Developments & Research

Prompt Optimization Research

Recent papers explore automated prompt optimization. “Large Language Models as Optimizers” (Yang et al., 2024) shows LLMs can iteratively improve their own prompts by analyzing failures and suggesting refinements.

“Automatic Prompt Engineering” (Zhou et al., 2023) introduced gradient-free optimization over prompt variations, systematically testing different phrasings to maximize task performance.

Constitutional AI and Instruction Hierarchy

Anthropic’s Constitutional AI work demonstrates encoding multiple layers of instructions:

  1. Core values (Constitutional principles)
  2. Task-specific guidelines
  3. Contextual refinements

This hierarchy lets agents balance competing objectives (helpfulness vs safety, specificity vs creativity).

Structured Outputs and Function Calling

OpenAI’s function calling and Anthropic’s tool use features formalize how prompts specify tool interfaces. Research shows that structured tool descriptions in prompts dramatically improve reliability compared to natural language descriptions.

“Tool Learning with Foundation Models” (Qin et al., 2024) surveys how different prompting strategies affect tool use accuracy, finding that explicit error handling instructions improve robustness by 30-40%.

Cross-Disciplinary Insight

Prompt engineering for agents parallels compiler design in computer science. Just as compilers translate high-level code to machine instructions, system prompts translate human intent to model behavior. Both require:

From cognitive science, we can apply “scaffolding” theory - providing temporary support structures that help learners develop skills. System prompts scaffold agent behavior until the model learns patterns through fine-tuning or few-shot adaptation.

Daily Challenge

Task: Build a prompt-optimizing agent

Create an agent that takes a task description and initial system prompt, tests them with example inputs, analyzes failures, and iteratively suggests prompt improvements.

Structure:

def optimize_prompt(task: str, initial_prompt: str, test_cases: List[dict]) -> str:
    """
    task: Description of what the agent should do
    initial_prompt: Starting system prompt
    test_cases: List of {input, expected_output} pairs
    
    Returns: Improved system prompt
    """
    # Your implementation here
    # 1. Test initial prompt on all test cases
    # 2. Identify patterns in failures
    # 3. Generate prompt variations addressing failures
    # 4. Test variations and select best
    # 5. Repeat until performance threshold met
    pass

This exercise helps you understand what makes prompts effective by automating the experimentation process.

References & Further Reading

Papers

Practical Resources

Frameworks with Prompt Examples

Blog Posts

The field of prompt engineering continues to evolve rapidly as models improve and new patterns emerge. What works today may need refinement tomorrow, making this a uniquely dynamic area of AI agent development.

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