Automating the Boring Stuff: How I Use AI Agents to Simplify Workflows

Editor’s Note: This blog is adapted from a talk by Aman Soni, an AI/ML Engineer at GeekyAnts and multi-patent holder. In this session, he explored how autonomous agents can automate complex tasks—from coding to querying private databases. Backed by real-world examples and a weekend project, his talk offered a hands-on look at the future of AI-driven workflows.

We will start simple, cover real case studies, and then dive into how I built a weekend project that turns SQL queries into conversations. Let’s go.

Every failed test case was reported back to the dev agent, and it resolved the issue. No Slack messages. No wait time. Just autonomous coordination.

Sounds futuristic? It’s already being tested. Walmart is using such agents—not for customers, but for their employees. A new joiner can ask a chatbot about stock, inventory, or the location of items and get answers instantly in natural language.

• Which employees earn above a certain amount?
  
• How many have more than 5 years of experience in a domain?
  

You either need to know SQL or build something smarter.

1. You ask a question in plain English, like “Who earns the most in this company?”
   
2. Agent 1: Generates the SQL query.
   
3. Agent 2: Validates it against the schema.
   
4. Agent 3: Tests it and checks if the result makes sense.
   
5. Final Agent: Converts it into a natural language response.
   

You get both the SQL query and the answer, with full transparency and zero coding. Plus, the agents talk to each other in real time—you can see them negotiating over the best query or result.

• Healthcare: Summarizing therapy sessions using voice-to-text and AI.
  
• Finance: Auto-generating reports and filtering fraud patterns.
  
• Retail: Like Walmart, enhancing employee training and ops.
  
• Education: Students no longer rely solely on Stack Overflow—they ask LLMs.
  
• Legal: Extracting lease terms or contract insights in seconds.
  

No matter the domain, if there is repetitive decision-making, agents can help.

• Human-in-the-loop approaches
  
• Attention-based decision tracking
  
• Custom metrics for agent accuracy and precision
  

Explainability in agent workflows is still an open challenge—and one worth solving.

Thanks for reading—and feel free to reach out if you want to jam on agent use cases. Let’s build smarter, not harder.