Why Prompt Engineering Matters for Agents
When building AI agents, your prompts are the "source code" that defines agent behavior. A poorly written prompt leads to unpredictable, unreliable agents. A well-crafted prompt produces agents that consistently deliver high-quality results.
- Unlike one-shot prompts for chatbots, agent prompts must handle:
- Multi-step reasoning across complex workflows
- Tool selection — deciding which tools to use and when
- Error recovery — gracefully handling unexpected situations
- Output consistency — producing structured, parseable responses
System Prompts vs User Prompts
In agent architectures, there are two distinct prompt layers:
System Prompt — defines the agent's identity, capabilities, and constraints. This is set once during configuration and persists across all interactions.
User Prompt — the specific task or question for each interaction. This changes with every request.
from openclaw import Agent, AgentConfig
config = AgentConfig(
name="structured-agent",
model="gpt-4o",
system_prompt="""You are a data analysis agent. Your role is to:
CAPABILITIES:
- Analyze datasets and identify patterns
- Generate statistical summaries
- Create visualizations using Python code
CONSTRAINTS:
- Always validate data before analysis
- Report confidence levels with findings
- Never fabricate data points
OUTPUT FORMAT:
- Start with a one-line summary
- Follow with detailed findings
- End with recommended next steps""",
)
agent = Agent(config)
# The user prompt changes per request
response = agent.run("Analyze the trend in our Q4 sales data")Chain-of-Thought for Agents
Chain-of-thought (CoT) prompting is essential for agent reliability. By instructing agents to show their reasoning, you get more accurate results and better debuggability.
config = AgentConfig(
name="cot-agent",
model="gpt-4o",
system_prompt="""You are a problem-solving agent. For every task:
THINK: Analyze the problem and identify the approach
PLAN: List the steps you'll take (numbered)
EXECUTE: Carry out each step, showing your work
VERIFY: Check your results for correctness
RESPOND: Provide the final answer
Always follow this exact sequence. Never skip the THINK or VERIFY steps.""",
)This pattern dramatically reduces errors because the agent is forced to plan before acting and verify before responding.
Template Patterns
Here are battle-tested prompt templates for common agent patterns:
The Router Pattern Use this when your agent needs to delegate to specialized sub-agents:
ROUTER_PROMPT = """You are a routing agent. Analyze the user's request and
determine which specialist should handle it.
SPECIALISTS:
- CODE_AGENT: Programming questions, code review, debugging
- DATA_AGENT: Data analysis, SQL queries, visualizations
- RESEARCH_AGENT: Factual questions, summarization, comparison
RULES:
1. Choose exactly ONE specialist
2. Output format: ROUTE_TO: [SPECIALIST_NAME]
3. Include a brief reason for your choice
If the request is ambiguous, ask for clarification instead of guessing."""The Validator Pattern Use this to check agent outputs before returning them to users:
VALIDATOR_PROMPT = """Review the following agent output for quality and correctness.
CHECK:
1. Factual accuracy — flag any claims that seem incorrect
2. Completeness — does it fully address the request?
3. Format — does it match the expected output structure?
4. Safety — any harmful or inappropriate content?
Output: APPROVED or REVISION_NEEDED with specific feedback."""The Retry Pattern Build resilience into your agents with retry-aware prompts:
from openclaw import Agent, AgentConfig
config = AgentConfig(
name="resilient-agent",
model="gpt-4o",
system_prompt="""You are a resilient task agent.
If a tool call fails:
1. Analyze the error message
2. Determine if it's retryable (network, timeout) or permanent (auth, not found)
3. For retryable errors: try an alternative approach
4. For permanent errors: explain what happened and suggest manual steps
Never retry the exact same action more than once.""",
max_retries=3,
)Putting It All Together
Here's a complete example combining these techniques:
from openclaw import Agent, AgentConfig, ConversationMemory
config = AgentConfig(
name="production-agent",
model="gpt-4o",
system_prompt="""You are a senior software engineering assistant.
APPROACH (follow for every task):
1. UNDERSTAND: Restate the task in your own words
2. PLAN: Break it into numbered steps
3. EXECUTE: Complete each step carefully
4. REVIEW: Verify correctness and quality
COMMUNICATION:
- Be concise and technical
- Use code blocks for all code
- Explain non-obvious decisions
CONSTRAINTS:
- Follow language-specific best practices
- Consider edge cases and error handling
- Prioritize readability over cleverness""",
memory=ConversationMemory(max_turns=20),
temperature=0.3, # Lower temperature for more consistent output
)
agent = Agent(config)Key Takeaways
- System prompts are your agent's DNA — invest time in getting them right
- Chain-of-thought isn't optional — it's essential for reliability
- Use templates — proven patterns save time and reduce errors
- Lower temperature for production agents (0.1–0.4)
- Test edge cases — good prompts handle unexpected inputs gracefully
Next up: Building a RAG-powered research agent that can search and synthesize information from your own documents.
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