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Best Practices & Patterns

Best Practices for Building AI Agents

Learn from teams who’ve deployed thousands of agents in production. These guides cover everything from writing effective instructions to optimizing performance and ensuring security.

Getting Started

Writing Effective Agent Instructions How to write clear, actionable instructions that get the results you want.
Knowledge Base Optimization Structure your data for maximum agent performance and accuracy.
Testing & Quality Assurance Test your agents thoroughly before deploying to production.

By Use Case

Customer Support Agents

Best practices for building reliable, helpful support automation.

Coming Soon - We’re working on this guide. In the meantime, check out our Customer Support Solution.

Code Review Agents

Automate code review with consistent, thorough analysis.

Coming Soon - We’re working on this guide. In the meantime, check out our Code Automation Solution.

Data Analysis Agents

Build agents that turn data into actionable insights.

Coming Soon - We’re working on this guide. In the meantime, check out our Data Analysis Solution.

Advanced Topics

Security & Compliance Keep your agents secure and compliant with regulations.

Performance Optimization

Make your agents faster and more cost-effective.

Coming Soon - We’re working on this guide. In the meantime, see our API Documentation for optimization tips.

Error Handling & Recovery

Build resilient agents that handle failures gracefully.

Coming Soon - We’re working on this guide. In the meantime, check our Testing Guide.

Monitoring & Observability

Track agent performance and identify issues early.

Coming Soon - We’re working on this guide. In the meantime, see Account Settings for usage tracking.

Quick Tips

Do’s ✅

  • Be specific - Clear instructions get better results
  • Test thoroughly - Try edge cases before deploying
  • Start simple - Add complexity gradually
  • Monitor actively - Review conversations regularly
  • Iterate often - Improve based on real usage
  • Document everything - Keep track of what works

Don’ts ❌

  • Don’t be vague - “Be helpful” isn’t enough guidance
  • Don’t skip testing - Production isn’t the place to debug
  • Don’t over-complicate - Simple agents are more reliable
  • Don’t set and forget - Agents need ongoing refinement
  • Don’t ignore feedback - Users tell you what needs fixing
  • Don’t expose secrets - Never put API keys in instructions

Common Patterns

Pattern 1: Escalation Workflow

When to use: Support agents that need human backup

1. Agent attempts to answer
2. If confidence < 80%, ask clarifying questions
3. If still uncertain, escalate to human
4. Human reviews context and takes over

Pattern 2: Multi-Step Validation

When to use: Agents that perform actions (not just chat)

1. Agent proposes action
2. Show user what will happen
3. Get explicit confirmation
4. Execute action
5. Confirm completion

Pattern 3: Knowledge Base Fallback

When to use: Agents with multiple information sources

1. Check primary knowledge base
2. If no answer, check secondary sources
3. If still no answer, search web (if enabled)
4. If nothing found, admit uncertainty

Performance Benchmarks

Based on 10,000+ production agents:

MetricGoodGreatExcellent
Response Time< 5s< 3s< 1s
Accuracy> 80%> 90%> 95%
User Satisfaction> 3.5/5> 4.0/5> 4.5/5
Escalation Rate< 30%< 20%< 10%
Resolution Rate> 60%> 75%> 85%

Learning Path

Week 1: Foundations

  1. Read Agent Instructions Guide
  2. Complete Interactive Quickstart
  3. Build your first agent

Week 2: Optimization

  1. Read Knowledge Base Optimization
  2. Read Testing Guide
  3. Refine your agent based on testing

Week 3: Production

  1. Read Security Best Practices
  2. Monitor your agent’s performance
  3. Deploy to production with monitoring

Week 4: Scale

  1. Optimize based on usage patterns
  2. Implement error handling strategies
  3. Scale to handle more traffic

Community Wisdom

From Support Teams

“Start with a narrow scope. Our first agent only handled password resets. Once that worked perfectly, we expanded to other topics.” — Sarah, Head of Support at TechCorp

From Engineering Teams

“Test with real production data, not synthetic examples. We found edge cases we never would have imagined.” — Alex, Engineering Lead at DevTeam Inc

From Data Teams

“Keep your knowledge base organized. We use a clear folder structure and naming convention. Makes updates so much easier.” — Michael, Analytics Director at DataFlow

Get Help

Contributing

Have a best practice to share? We’d love to hear it!