ai-agent-design

To design effective AI agents and reduce hallucination, apply structured prompt engineering, implement robust guardrails, and use domain-specific evaluation strategies to measure and constrain model outputs.

Writing system prompts for a customer support agent requires clear, hierarchical instructions that define the persona, specify available tools, and establish strict rules for handling user requests.

Select the right LLM for your AI agent and optimize costs by evaluating model capabilities against specific task requirements, balancing performance tiers, and applying cost optimization strategies during model selection.

Designing robust tools for AI agents involves creating clear interfaces, defining precise input schemas, and establishing error handling protocols to ensure reliable multi-agent coordination and execution.

Multi-agent coordination works by defining specialized roles, establishing communication protocols between agents, and implementing guardrails to ensure reliable handoffs and collective task completion.

Yes, you can use domain-agnostic guidelines to build specialized AI agents by applying universal principles for prompt engineering and tool design, then adapting evaluation strategies to your specific use case.