Many developers still see AI as just a smarter autocomplete or a better search tool. But the industry is quickly moving from simply chatting with AI to actually delegating tasks to it. If you want to automate your daily workflow with AI agents, you don’t need a big cloud setup or costly API subscriptions. You can build a system on your own computer that gets things done for you.
In this article, I’ll show you how to build your own personal workflow assistant today.
Unpacking AI Agents
To see why agents are so useful, let’s look at how they differ from standard Large Language Models.
A standard LLM takes your prompt and predicts what words should come next, based only on its training data. An AI agent, on the other hand, is an LLM that also has a reasoning process and a set of tools it can use.
When you give an agent a goal, it does more than just give you an answer. It plans out steps, writes code to use its tools, like web search, a calculator, or database access, checks the results, and keeps going until the job is done. This connects generating text with actually taking action.
Automate Your Daily Workflow Using AI Agents: Building Your Local Assistant
A great way to see this in action is by building a morning briefing assistant. Instead of checking the news, weather, and your calendar by hand, you can write a Python script to do it all automatically.
For this project, we’ll use smolagents, a lightweight library from Hugging Face, and run our LLM locally with Ollama. Running models on your own machine is a big advantage for privacy and saving money, especially when you’re just trying things out.
Build production-ready AI engineering skills with Hands-On GenAI, LLMs & AI Agents.
Before proceeding, install these libraries:
pip install smolagents litellm duckduckgo-search
Here’s the full code for this workflow. We’ll look at it first, then I’ll walk you through how it works step by step:
import os
from smolagents import CodeAgent, DuckDuckGoSearchTool, LiteLLMModel
# 1. Run a free local LLM using Ollama
# Install Ollama and pull a model first:
# ollama pull qwen3:8b
# or
# ollama pull llama3.2:3b
model = LiteLLMModel(
model_id="ollama/qwen3:8b",
api_base="http://localhost:11434"
)
# 2. Give the agent access to web search
search_tool = DuckDuckGoSearchTool()
# 3. Create the agent
agent = CodeAgent(
tools=[search_tool],
model=model,
additional_authorized_imports=["datetime"]
)
# 4. Define the task
task_prompt = """
You are my daily workflow assistant.
Please perform the following tasks:
1. Find the latest 3 news headlines about Open Source AI frameworks.
2. Get the current weather conditions for Kangra, Himachal Pradesh.
3. Use Python's datetime module to get today's date.
4. Generate a concise professional morning briefing.
"""
# 5. Execute the workflow
print("Running agent...\n")
response = agent.run(task_prompt)
print("\n----- Morning Briefing -----\n")
print(response)Let’s break down the workflow step by step:
- The Local Brain: Rather than paying for API calls, we use LiteLLMModel to connect to a local Ollama instance on port 11434. I recommend models like qwen2.5:8b or llama3.2:3b for local agent tasks. They’re small enough for a regular laptop but smart enough to write the Python code your agent needs.
- Equipping Tools: Without tools, an agent can’t access current information. By setting up DuckDuckGoSearchTool(), we let the LLM search the live internet.
- Assembling the Agent: The CodeAgent is central here. When you give it a task, it writes Python code, runs it safely, and uses the results. See the additional_authorized_imports=[“datetime”]? This lets the agent get the real system time, showing it’s running real code instead of just making up a date.
- Defining the Workflow: Our prompt is very specific. It asks for industry news and the weather in Kangra, Himachal Pradesh. Since the agent can access the web, it will search for the latest Kangra weather, get AI news, figure out the date, and put it all together in a clear report.
Next Step: Level Up Your AI Engineering Skills
If you want to go beyond a single Python script and really master agent workflows, structured learning can help you move faster. Here are two courses I recommend:
- AI Agent Developer Specialization: This is the ideal next step after building your first agent.
- Generative AI for Everyone: Taught by AI expert Andrew Ng, this course is great if you’re still learning the differences between traditional ML and GenAI.
Final Thoughts
One common mistake I see when people move from ML algorithms to GenAI is making their first setup too complicated. I’ve built complex RAG-based apps and multi-agent systems for clients, and the main rule is always: start small.
Don’t try to build an agent that manages everything at once. Begin by automating one simple task, like summarizing unread emails or getting your daily metrics. Learn how the agent deals with tool errors and unexpected search results before you expand.
I hope you found this article helpful for automating your daily workflow with AI agents.
For more AI and machine learning tips, follow me on Instagram. My book, Hands-On GenAI, LLMs & AI Agents, can also help you advance your AI career.
