The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
A massive logistics enterprise was losing millions due to delayed shipments, human errors in customs paperwork, and supply chain blind spots across 40 different countries. The Solution: We architected a "Multi-Agent Swarm" – a network of specialized, autonomous AI agents. One agent monitors global weather and GPS data, another agent drafts customs paperwork using LLMs, and a third agent negotiates shipping rates in real-time. Everything is tied together via an Event-Driven Architecture. The Impact: Eliminated human bottlenecks entirely. The AI swarm now autonomously reroutes global shipments before weather delays happen and executes supplier payments automatically, saving the client $2M+ annually in operational delays.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Challenges
View all
Related posts
What if your PC could actually listen, understand, and do the work for you?
I’m building that idea into something real.
Meet AURA — my personal AI voice assistant for Windows.
Instead of building another chatbot that only answers questions, I’m experimenting with an AI agent that can actually interact with my computer and help with everyday work.
So far, AURA can:
• Have live voice conversations
• Convert speech to text and respond with voice
• Open applications like Excel, Power BI, Chrome, and Windows Settings
• Search the web and YouTube through voice
• Perform basic PC and file operations
• Understand requests through Gemini
• Execute actions through a safety-controlled automation layer
• Continue listening after completing a task
But this is only the beginning.
My long-term goal is to make AURA understand my actual workspace — including what’s happening on my screen and inside applications — so I can simply say:
“Open my Excel file, check the sales data, clean the date column, and add a formula for total revenue.”
And let AURA handle the workflow.
I also want it to become useful for my own Data Analytics work — from Excel and Power BI workflows to repetitive desktop tasks, research, and automation.
I’m building AURA with Python, AI, voice technologies, desktop automation, Excel, Power BI, and LLMs, while keeping safety and human control at the core.
It’s still far from the final vision.
But that’s exactly what makes the project interesting.
I’m not just learning how AI agents work.
I’m trying to build one that I can actually use every day.
Building in public. Learning by building. 🚀
#AI #AIAgents #Python #AIAssistant #VoiceAI #DesktopAutomation #DataAnalytics #PowerBI #Excel #Automation #BuildInPublic
The screen-reading step is where I would put the guardrail. Once AURA can read what is in the window, everything on that screen is input - and a page it opened from a voice search can hold a sentence written to be read as an instruction. I spent a while trying to rob my own...
As an AI Agent Developer, I build agents into an operating flow rather than leaving them as a voice demo. Dispatchly lets lending, insurance, and staffing teams upload leads, choose a use case, run campaigns, and route qualified results into a CRM, with a transcript and recording for every call. I add approvals around consequential actions and read-back after integrations. If you need an AI agent connected to real operations, message me.
🤖 What if AI stopped waiting for prompts — and started working toward goals?
We’ve moved from chatbots → copilots → AI agents.
The next shift, I believe, is AI systems that can plan, collaborate, execute, and adapt on their own.
Imagine giving AI a goal instead of a list of instructions:
“Analyze this problem, research what’s needed, figure out the best approach, execute the tasks, and keep me updated.”
Behind the scenes, multiple specialized agents could:
🔹 Break the goal into tasks
🔹 Research & reason over information
🔹 Use tools and APIs
🔹 Collaborate with other agents
🔹 Validate their own work
🔹 Adapt when something fails
🔹 Deliver the final outcome
That’s the direction I’ve been exploring with WhatAIUpTo — moving from “AI that answers” → “AI that gets work done.”
The interesting question isn’t “What can AI generate?” anymore.
It’s: “What can AI accomplish without being told every step?”
We’re just getting started. 🚀
#AI #GenerativeAI #AgenticAI #AutonomousAgents #AIEngineering #FutureOfAI #MultiAgentSystems #ArtificialIntelligence
