AI agents are software programs powered by LLMs that autonomously complete goals by breaking down complex tasks and using tools and knowledge bases. Key components include the LLM (brain), system instructions (playbook), tools (hands), short-term chat history (whiteboard), and long-term memory via databases like Milvus for RAG. The instructions tie everything together; without them, the agent lacks direction.

Tip #1: Draw it out — create a flow chart of the business process and agent workflow using tools like draw.io and Mermaid.js. This ensures team alignment, maps decision points, and aids debugging. Tip #2: Use AI developer tools such as IBM Bob to assist with writing, refining, and updating instructions, catching mistakes and ensuring consistency. Tip #3: Structure instructions with clear sections — Identity, Goals, Constraints, Tools, and Workflow — using Markdown for readability. Tip #4: Define tool usage explicitly, specifying when and how to invoke each tool with required parameters.

Tip #5: Include few-shot examples showing ideal interactions to guide agent behavior. Tip #6: Handle edge cases and errors by defining fallback behaviors and escalation paths. Tip #7: Version control instructions using Git to track changes and enable rollbacks. Tip #8: Test iteratively with real scenarios, refining instructions based on observed agent performance.