30 Pro Tips & Best Practices (What Nobody Tells You)

Beginners build agents that break after a week. Experts build agents that run for months without intervention.

Here's what separates them.

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Prompt Engineering Secrets

Tip 1: Structure Your Prompts Like This

Bad prompt:

Write an email response.

Good prompt:

You are a customer service expert for [COMPANY NAME].

Read this email and write a response that:
1. Addresses their question directly
2. Uses a friendly professional tone
3. Keeps it under 150 words
4. Ends with "Let me know if you need anything else!"

Email: {{email_content}}

Your response:

Why it works: Clear role, specific instructions, format guidance, constraints.

Results: 80% better response quality.

Tip 2: Use Examples in Your Prompts

Add 2-3 examples of perfect responses:

Here are examples of great responses:

Example 1:
Customer: "When will my order ship?"
Response: "Your order ships within 24 hours! You'll get tracking via email. Let me know if you need anything else!"

Example 2:
Customer: "I need a refund"
Response: "I've started your refund process. You'll see it in 3-5 business days. Let me know if you need anything else!"

Now respond to this email:
{{email_content}}

Why it works: AI learns your exact style and format from examples.

Results: 90% consistency in tone and structure.

Tip 3: Chain of Thought for Complex Tasks

For complex analysis, make AI think step-by-step:

Analyze this company for sales potential.

Think through this step-by-step:
1. First, what industry are they in?
2. Based on that, what problems do they likely face?
3. Do we solve those problems?
4. What is their estimated budget based on company size?
5. Final score 1-10 with reasoning.

Company data: {{data}}

Why it works: Forces logical reasoning instead of quick guessing.

Results: 60% more accurate analysis.

Tip 4: Constrain Output Format

Tell AI exactly how to format responses:

Respond in this exact format:

SCORE: [1-10]
REASON: [One sentence]
RECOMMENDATION: [CONTACT NOW / NURTURE / SKIP]
TALKING_POINTS:
- Point 1
- Point 2
- Point 3

Do not add any other text or explanation.

Why it works: Makes parsing and automation easier, prevents hallucination in formatting.

Results: 100% parseable outputs.

Tip 5: The "Output Only" Trick

When you need clean data without explanation:

Extract the company name from this email.

Output ONLY the company name, nothing else.
No explanation. No additional text.
Just the company name.

Why it works: Prevents AI from adding helpful but unwanted commentary.

Results: Clean, usable data every time.

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Cost Optimization Hacks

Tip 6: Cache Repeated Responses

Before calling OpenAI, check if you've answered this before:

// Hash the question
const questionHash = hashFunction(customerQuestion);

// Check cache (Google Sheets, Redis, etc.)
const cached = await checkCache(questionHash);

if (cached && cached.age < 7_days) {
  return cached.response; // Free!
} else {
  const response = await openAI(customerQuestion);
  await saveToCache(questionHash, response);
  return response;
}

Savings: 60-80% on API costs for repeat questions.

Tip 7: Use the Right Model for the Job

GPT-4o-mini for (16x cheaper):

• Simple questions
• Classification tasks
• Short responses
• Data extraction
• Categorization

GPT-4o for (better but expensive):

• Long content creation
• Complex analysis
• Creative work
• When quality matters more than cost

Cost difference: $0.15/1M tokens vs. $5.00/1M tokens

Most tasks work fine with mini. Use premium only when needed.

Tip 8: Batch Processing

Inefficient:

Process 100 emails → 100 API calls

Efficient:

Collect 10 emails → 1 API call with all 10
Process batch → Return all responses

Prompt for batch:

Here are 10 emails. For each, write a response.

Format:
EMAIL 1 RESPONSE: [your response]
EMAIL 2 RESPONSE: [your response]
...

Emails:
1. [email 1]
2. [email 2]
...

Savings: 50-70% on operations and costs.

Tip 9: Reduce Token Usage

Long prompt (wasteful):

You are an extremely helpful and professional customer service representative who works for our company and helps customers with their questions in a friendly and professional manner while maintaining a positive attitude...

Short prompt (efficient):

You are a helpful customer service agent. Be professional and friendly.

Same meaning. 70% fewer tokens.

Review all your prompts: Cut unnecessary words.

Tip 10: Monitor Costs Weekly

Every Friday:

1. Check OpenAI dashboard
2. Review spend by agent
3. Identify expensive agents
4. Optimize or pause them
5. Set budget alerts

Prevents: Surprise $500 bills at end of month.

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Error Handling Mastery

Tip 11: Always Have a Fallback

Every AI call should have a plan B:

try {
  const response = await openAI(prompt);
  return response;
} catch (error) {
  // Plan B: Human intervention
  await sendNotification({
    to: "team@company.com",
    subject: "AI Agent Failed",
    body: `Error: ${error}\nData: ${originalData}`
  });
  
  // Plan C: Generic response
  await sendCustomer({
    message: "Got your message! A human will respond within 1 hour."
  });
  
  // Save for manual processing
  await saveForManualReview(originalData);
}

Never fail silently. Always have 2-3 fallback options.

Tip 12: The Retry Pattern

If API fails, retry with exponential backoff:

async function callAIWithRetry(prompt, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await openAI(prompt);
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      
      // Wait before retry: 2s, 5s, 10s
      const waitTime = Math.pow(2, i) * 1000;
      await sleep(waitTime);
    }
  }
}

Handles: Temporary API issues, rate limits, network blips.

Tip 13: Validate AI Output

After AI generates response, validate it:

const response = await openAI(prompt);

// Validation checks
if (response.length < 10) {
  // Too short, probably failed
  return retry();
}

if (response.includes("I cannot") || response.includes("I apologize")) {
  // AI refused or couldn't answer
  return tryDifferentPrompt();
}

if (!response.match(/[A-Za-z]{5,}/)) {
  // Not real text, probably error
  return regenerate();
}

// Passed all checks
return response;

Catches bad outputs before they reach customers.

Tip 14: Log Everything

Every agent execution should log:

• Input received
• Actions taken
• Output generated
• Errors encountered
• Execution time
• API cost

Review logs weekly to catch patterns and issues.

await logExecution({
  agent: 'email_responder',
  timestamp: Date.now(),
  input: customerEmail,
  output: aiResponse,
  cost: apiCost,
  duration: executionTime,
  success: true
});

Tip 15: Set Execution Timeouts

If agent runs longer than expected, kill it:

const timeout = 30000; // 30 seconds

const result = await Promise.race([
  runAgent(data),
  new Promise((_, reject) => 
    setTimeout(() => reject('Timeout'), timeout)
  )
]);

Prevents: Infinite loops, runaway costs, hanging processes.

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Security & Privacy

Tip 16: Never Store API Keys in Plain Text

Bad:

const apiKey = "sk-1234567890abcdef";

Good:

const apiKey = process.env.OPENAI_API_KEY;

Use: Environment variables, secret managers (AWS Secrets Manager, 1Password, etc.)

Tip 17: Sanitize User Input

Before passing to AI, clean the input:

function sanitizeInput(input) {
  // Remove potential prompt injections
  input = input.replace(/ignore previous instructions/gi, "");
  input = input.replace(/system:/gi, "");
  input = input.replace(/\[INST\]/gi, "");
  
  // Limit length (prevent token bombs)
  input = input.substring(0, 5000);
  
  // Remove dangerous characters
  input = input.replace(/[<>]/g, "");
  
  return input.trim();
}

Prevents: Prompt injection attacks, token abuse.

Tip 18: Use Separate API Keys per Agent

Don't: Use one OpenAI key for all agents

Do: Create separate keys:

• email-agent-key
• lead-research-key
• content-generator-key

Benefits:

• If one compromised, others safe
• Track costs per agent
• Set limits per key
• Easier debugging

Tip 19: Rate Limit User-Facing Agents

If agent is public-facing (e.g., chatbot on website):

const userLimits = {
  requestsPerMinute: 5,
  requestsPerHour: 30,
  requestsPerDay: 100
};

if (await exceedsLimit(userIP, userLimits)) {
  return "You've reached your limit. Please try again later.";
}

Prevents: Abuse, cost overruns, DoS attacks.

Tip 20: Review Sensitive Operations

For actions like:

• Sending money
• Deleting data
• Changing passwords
• Canceling subscriptions

Always require human approval:

if (action.sensitive) {
  await sendApprovalRequest(admin);
  await waitForApproval();
  // Only proceed after human approves
}

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Performance Optimization

Tip 21: Parallel Processing When Possible

Slow way (sequential):

const companyInfo = await researchCompany(name);  // 30s
const personInfo = await researchPerson(email);    // 20s
const competitorInfo = await researchCompetitors(); // 40s
// Total: 90 seconds

Fast way (parallel):

const [companyInfo, personInfo, competitorInfo] = await Promise.all([
  researchCompany(name),
  researchPerson(email),
  researchCompetitors()
]);
// Total: 40 seconds (longest individual task)

3x faster for independent operations.

Tip 22: Use Webhooks for Real-Time Triggers

Polling (slow):

Check email every 15 minutes → Wastes operations, delays response

Webhooks (instant):

Email arrives → Webhook fires → Agent processes immediately

Better UX. Saves operations. More professional.

Tip 23: Cache External API Calls

Company information doesn't change hourly:

// Before calling LinkedIn API
const cacheKey = `company_${domain}`;
const cached = await getFromCache(cacheKey);

if (cached && cached.age < 7 * 24 * 60 * 60 * 1000) {
  return cached.data; // Free & instant
}

const fresh = await linkedInAPI(domain);
await saveToCache(cacheKey, fresh, ttl: 7_days);
return fresh;

Faster responses. Lower costs.

Tip 24: Simplify Workflows Monthly

Every month, review each agent and ask:

"Can I remove any steps?"

Often you'll find:

• Unnecessary data fetching
• Redundant AI calls
• Unused features
• Over-complicated logic

Remove them. Simpler = Faster = Cheaper.

Tip 25: Use Streaming for Long Responses

Instead of waiting 30 seconds for complete response:

// Enable streaming
const stream = await openAI({
  prompt: prompt,
  stream: true
});

// Show response as it generates
for await (const chunk of stream) {
  displayToUser(chunk);
}

Better user experience. Feels 10x faster.

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Scaling Strategies

Tip 26: Start Small, Scale Gradually

Month 1: 1-2 agents, low volume
Month 2: Optimize, add 1-2 more
Month 3: Scale up volume
Month 4: Add advanced features

Don't: Try to build everything day 1.

Tip 27: Document Everything

For each agent, document:

• What it does
• How to trigger it
• What prompts are used
• Known issues
• How to fix common problems
• Emergency contacts

When it breaks at 2am, you'll thank yourself.

Tip 28: Version Control Your Agents

Keep versions:

• v1.0: Initial version
• v1.1: Fixed email parsing
• v1.2: Added error handling
• v2.0: Complete rewrite

Can rollback if new version breaks.

Tip 29: Monitor Performance Metrics

Track weekly:

• Success rate (% of successful executions)
• Response quality (sample check 10 random outputs)
• Cost per operation
• Time saved (estimate)
• User satisfaction (if customer-facing)

Identify what needs improvement.

Tip 30: Build Feedback Loops

After agent responds, collect feedback:

Was this response helpful?
[👍 Yes] [👎 No] [⚠️ Needs improvement]

If No or Needs improvement:
→ Log the issue with context
→ Review monthly
→ Adjust prompts based on patterns
→ A/B test improvements

Agents get better over time through feedback.

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Bonus Tips (Because We Like You)

Tip 31: The Golden Rule of Agents

One agent = One clear purpose

Bad: Email agent that also does calendar, CRM, social media, and makes coffee.

Good: Email agent that ONLY handles emails. Really, really well.

Why: Focused agents are easier to build, debug, and improve.

Tip 32: Name Things Clearly

Bad naming:

• Agent 1
• Agent 2
• Test scenario
• Final final v3

Good naming:

• customer_support_responder_v2
• lead_research_agent_production
• invoice_follow_up_automated

Future you will appreciate descriptive names.

Tip 33: The Friday Review Ritual

Every Friday, spend 15 minutes:

1. Check which agents ran this week
2. Review any errors or failures
3. Look at cost reports
4. Read 5-10 sample outputs
5. Note 1-2 improvements needed
6. Make one small optimization

15 minutes/week keeps everything running smoothly.

Tip 34: The "Would I Send This?" Test

Before automating responses, ask:

"Would I personally send this email to a customer?"

If no → Improve the agent
If yes → Safe to automate

Your reputation is on the line. Be honest.

Tip 35: Join Communities

Best places to learn and get help:

• Make.com Community
• n8n Forum
• r/automation on Reddit
• AI automation Discord servers

Real people solving real problems. Learn from their wins and mistakes.

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Common Mistakes (And How to Avoid Them)

Mistake 1: Over-Engineering

Building a perfect, complex AI agent before testing if it works.

Fix: Build simple first. Add features later based on real needs.

Mistake 2: No Testing Period

Launching agents in production without adequate testing.

Fix: Test manually for 1 week, review mode for 1 week, then automate.

Mistake 3: Ignoring Edge Cases

Agent works great for 90% of cases. Breaks spectacularly on 10%.

Fix: Test with weird inputs. Handle exceptions gracefully.

Mistake 4: No Human Override

Agent does something wrong, and there's no way to stop it.

Fix: Always have emergency stop button and human escalation path.

Mistake 5: Forgetting to Monitor

Set and forget. Months later, discover agent has been broken for weeks.

Fix: Weekly monitoring. Set calendar reminder. Check dashboards.

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The Meta-Skill: Continuous Improvement

Good agents aren't built. They're grown.

Week 1: Agent works at 60% quality
Week 2: Fixed 3 issues, now 70%
Week 3: Better prompts, now 80%
Month 2: Solid 90% quality
Month 3: Barely needs supervision, 95%+

This is normal. Embrace the iterative process.

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Your Action Plan

Today

Pick 5 tips from this list. Implement them in your existing agents.

This Week

Review all your agents using these tips. Improve one thing per agent.

This Month

Make Friday reviews a habit. Track improvements weekly.

This Quarter

Your agents should be 10x more reliable than when you started.

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The Final Truth

Most people build one agent and quit.

Why? They expect perfection on day 1.

Reality:

• First agent is always messy
• Second is better
• Fifth is good
• Tenth is great

The skill isn't building the perfect agent. It's building, testing, improving, repeating.

Do that for 3 months and you'll be ahead of 99% of people.

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What's Next?

You now have the best practices used by professionals.

But knowledge without action is worthless.

This week:

1. Pick your worst-performing agent
2. Apply 5 tips from this guide
3. Measure the improvement
4. Repeat with next agent

Next month:

• All agents running smoothly
• Costs optimized
• Error rates down
• Quality up

Want to see cutting-edge tools? Check out Advanced AI Tools that make your agents 10x more powerful.

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Need help optimizing your AI agents? Contact us for expert consultation and implementation support.