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Building AI Agents for Real-World Use Cases

AI Agents and Automation

AI agents are transforming how businesses operate. But the gap between AI hype and practical implementation remains wide. At Straw Labs, we focus on building AI agents that solve real problems and deliver measurable value.

What Makes a Good AI Agent?

A good AI agent isn't about using the latest model or the most complex architecture. It's about solving a specific problem well. We've built agents for customer support, workflow automation, and data processing—each tailored to its use case.

Key Principles We Follow

  • Start Simple: Begin with clear, focused use cases. A chatbot that handles one thing well beats one that handles everything poorly.
  • Measure Impact: Track real business metrics—response time, resolution rate, cost savings. If you can't measure it, you can't improve it.
  • Iterate Fast: Deploy quickly, gather feedback, improve. The best AI agents evolve with real-world usage.
  • Human in the Loop: AI should augment humans, not replace them. Build escalation paths and feedback mechanisms.

Real-World Example: WhatsApp Business Assistant

We recently built a WhatsApp-based AI assistant for a retail business. Instead of trying to handle everything, we focused on three core functions: order status, product availability, and store hours. The result? 70% reduction in support queries and faster response times.

// Simple agent architecture
const agent = {
  intent: detectIntent(message),
  context: getUserContext(userId),
  response: generateResponse(intent, context),
  action: executeAction(response)
}

The Tech Stack

We prefer open-source AI stacks for transparency and control. Our typical setup includes:

  • LangChain or custom orchestration for agent logic
  • Open-source LLMs (Llama, Mistral) or OpenAI for specific use cases
  • Vector databases for context and memory
  • Integration layers for WhatsApp, Slack, or web interfaces

Lessons Learned

After building dozens of AI agents, here's what we've learned: The technology is rarely the bottleneck. The real challenges are understanding the business problem, designing the right user experience, and maintaining the system over time. Focus on these, and the AI will follow.

Want to build an AI agent for your business? We'd love to help. Reach out to discuss your use case.