How to quickly answer the main question
If you need toautomate business processeswith AI today, you need to write prompts that leverage the new modernagents(TinyFish, Kimi K3.1 Pro, Google Cloud Agent Compute Engine). This guide shows you a practical, tested method for creating effective prompts that can transform manual tasks into reliable workflows.
Why the new AI agents change the game for automation
In 2026 the agent landscape has matured: TinyFish has published a7-phase agent research architecturethat separates logic, data retrieval, and execution; Moonshot AI has launchedKimi K3.1 ProAgent Compute Engine, a microVM isolated per agent.
These advances allow prompts to:
- Manage complex workflows with fewer API calls.
- Maintain data confidentiality thanks to hardware isolation.
- Leverage extended contexts for cross-referencing multiple sources.
TinyFish and the agent research architecture
TinyFish defines a seven-phase lifecycle:. Writing prompts that respect this structure makes agents more reliable in business contexts.
Moonshot AI Kimi K3.1 Pro: extended context and MoE
Google Cloud Agent Compute Engine: microVM per each agent
Each agent receives an isolated microVM, without shared credentials. This eliminates many security issues and is ideal for automating sensitive processes such as human resources or accounting.
How to write prompts for business automation
Follow this step-by-step schema to create production-ready prompts.
1. Define role, purpose, and context
Include an explicit role instruction (e.g., "You are a senior data analyst"). Specify the purpose in a single sentence: "Generate a monthly sales report in Markdown format". Add relevant context: policies, input data, or constraints.
2. Use chain-of-thought and few examples
Ask the AI to "explain the reasoning step by step". Provide 1-2 input-output examples to reduce variability.
3. Incorporate functions or actions
If the workflow requires external actions (send email, create a ticket), define a function and instruct the agent to call it using a placeholder like[CALL_FUNCTION:send_email].
4. Optimize for long context
Practical example: automating the monthly sales report
Below is a Python snippet that uses LangChain to create an agent capable of generating reports.
from langchain.agents import create_react_agent, AgentExecutor
from langchain.tools import Tool
from langchain.llms import OpenAI
import pandas as pd
# Tool for loading sales data
def load_sales():
return pd.read_csv('sales_2026.csv')
# Tool for writing the report in markdown
def write_report_md(text):
with open('monthly_sales_report.md', 'w') as f:
f.write(text)
return 'Report saved'
# Definition of tools
tools = [
Tool(name='load_sales', func=load_sales, description='Load the October 2026 sales CSV'),
Tool(name='write_report', func=write_report_md, description='Write the report text in markdown'),
]
# Prompt that follows the TinyFish architecture
prompt = '''
Role: senior data analyst for an e-commerce company.
Goal: generate a markdown summary report of monthly sales.
Context: use the [load_sales] tool to read raw data, calculate total sales, top-selling products, and month-over-month trends.
Reasoning: explain each calculation step by step.
Action: if the calculation is correct, use the [write_report] tool to save the markdown.
'''
llm = OpenAI(model='gpt-4-turbo')
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
# Execution
result = executor.invoke({'input': 'Generate the report for October 2026'})
print(result)This agent leverages the Agent Compute Engine microVM (if deployed on GCP), extended context for the sales dataset, and a structured reasoning cycle that mirrors TinyFish's agent research model.
Key takeaways
- Adopt TinyFish's 7-phase architecturefor more reliable prompts.
- to include policies, code, and datasets directly in the prompt.
- Isolate each agent with a microVMvia Google Cloud Agent Compute Engine for enhanced security.
- Clearly define role, purpose, and contextat the beginning of the prompt.
- Add examples and chain-of-thoughtto reduce answer variability.
- Use tools and functionswhen the workflow requires external actions.
Conclusion
In 2026, creating effective prompts for business automation is not just about formulating requests; it's about leveraging modern agents, extended contexts, and secure isolation. By following the structured method above and integrating the latest innovations from TinyFish, Moonshot AI, and Google Cloud, you can transform manual processes into automated, production-ready workflows that scale reliably.
Start designing your first two prompts today: one for data analysis and one for email communication management. Test, measure, and refine: results will come quickly.
IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years like 2024, 2023, etc. The content must be fresh and relevant to the present. CURRENT CONTEXT (October 2026): Here are some recent trends and news to inspire you: - Kyutai CADRE: Video Autoencoders That Cut World Model Lag Below 230 ms: Kyutai released CADRE, a family of 7 open causal diffusion video autoencoders for interactive world models. World models built on CADRE run at 20 fps ... [2026-10-24] - NVIDIA Releases Nemotron 3.5 Bolt E4B 4GB: An Open Small Language Model for Local Voice Agents, Game Characters and Robots: NVIDIA released Nemotron 3.5 Bolt E4B 4GB, an open small language model post-trained from Gemma 4 E4B for local voice assistants, game characters and ... [2026-10-24] - Google Cloud Launches Agent Compute Engine: A MicroVM per AI Agent: Google Cloud announced Agent Compute Engine, a compute service that gives every AI agent its own hardware-isolated microVM with no credentials inside.... [2026-10-23] Use this current information to create an original and relevant prompt for 2026.