reasoning and decision-making 02 Tools External functions or APIs the agent can use to take action 03 Instructions Explicit guidelines and guardrails defining how the 
 agent behaves Here’s what this looks like library or building directly from scratch. Python 1 2 3 4 5 6 weather_agent = Agent( name= instructions= tools=[get_weather], ) , "Weather agent" "You are a helpful agent who can talk to users output, : datetime.time()}) return "File saved" search_agent = Agent( name= , instructions= tools=[WebSearchTool(),save_results], ) "output" "timestamp" "Search agent" "Help the user

Practices 54 Provide examples 54 Design with simplicity 55 Be specific about the output 56 Use Instructions over Constraints 56 Control the max token length 58 Use variables in prompts 58 Experiment optimal balance between determinism and randomness. Prompting techniques LLMs are tuned to follow instructions and are trained on large amounts of data so they can understand a prompt and generate an answer the LLM to get started with. This input could be anything: a question, a start of a story, or instructions. The name zero-shot stands for ’no examples’. Prompt Engineering February 2025 14 Let’s use Vertex

user, flagging any UI issues. Updating systems of record on behalf of users, without technical instructions 
 or API connections. The result: end-to-end automation, freeing teams from repetitive tasks

P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730–27744, 2022.