The Good Tech Companies - Why AI Agents Should Handle the Mundane, So Humans Don’t Have To

Episode Date: May 29, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/why-ai-agents-should-handle-the-mundane-so-humans-dont-have-to. AI agents can handle repetitive tasks so humans can focus on strategy, innovation, and creativity—exploring PropRise’s vision for agentic automation. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-agents, #agentic-systems, #human-in-the-loop-ai, #proprise, #ai-in-real-estate, #automate-repetitive-tasks, #model-context-protocol, #good-company, and more. This story was written by: @jonstojanjournalist. Learn more about this writer by checking @jonstojanjournalist's about page, and for more stories, please visit hackernoon.com. Engineer Matteo Zamparini believes AI agents should do the repetitive work, freeing humans for strategic thinking. From real estate to clean energy, his work at PropRise shows how agentic systems powered by open standards can deliver reliable, human-guided automation that transforms productivity across industries.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Why AI agents should handle the mundane, so humans don't have to, by John Stoyan journalist. For engineer and innovator Matteo Zamparini, one the most significant productivity bottlenecks in the modern world isn't talent scarcity or lack of ambition, it's time wasted on repetitive tasks. From tracing parcel maps as a data science intern to co-founding a startup that identified renewable energy opportunities for commercial property owners, His career has followed a consistent theme. Build technology to automate grunt work so that human work can be used for more strategic tasks. As the founding engineer at Proprise,

a Y Combinator backed company, he builds AI agents that work around the clock to track huge amounts of disparate real estate data and generate insights that uncover potential investment opportunities. It all revolves around his vision of a world where AI doesn't just assist people, but joins them into the workforce to execute long-running tasks. The real cost of manual data analysis in a software-assisted world, lack of digital tools is no longer the main problem. Instead, as Zomperini discovered firsthand while at the University of California at Irvine, the problem tends to be the repetitive and time-consuming nature of tasks even the most talented humans need to tackle, from researchers to business professionals using

digital tools. As a student intern, he spent months using tools like digital maps, permitting sites, statistical computing software and real estate listings websites to collect and eventually analyze data that could be used for making investment and development decisions. I remember thinking. Satellite imaging built with Lideres Cool, he recounts. But is spending weeks on end on maps and websites trying to find the right data points really the best use of human intellect? This was one of the insights that sparked the idea for Spaceflare, a startup He founded

with fellow UC Irvine students after graduating in 2021. The model He built used public records and satellite imagery to identify properties that could be a good fit for solar panel installation, enabling clean energy companies and property owners to quickly identify potential opportunities for energy generation and revenue. A process that would likely have taken weeks of manual analysis could now be done in minutes with space flare. Building AI agents that act and reason, within reason. Today, Zomperini is the developer behind the AI agents that power PropRise. These autonomous agents act as 24-7 AI investment analysts that sift through countless data points ranging from real estate listings and financials to city permits, local news, and

market trends to uncover opportunities that human analysts might miss or take months to compile. PropRise is not just another machine learning tool that automates data collection. Its agents operate independently, structuring the data they find and continually evaluating the credibility and accuracy of a wide range of sources. They pursue objectives set by human operators, but can also escalate decisions back to them to ensure guardrails and oversight over significant decisions. We developed agentic systems that can reason, act, and even seek human feedback or approval mid-task," he explains. This blend of autonomy and human oversight is needed to build reliable AI workflows.

He sees this human-in-the-loop approach as a crucial component to a future in which AI agents can take over all manual work without risking loss of control over strategic decisions. Reliability over novelty. Architecting agentic systems for real world use. In Zomperini's eyes, the emergence of open standards like model context protocol, MCP, AGNTCY, human layer or the language BAML is the single most important trend in AI. To him, they are the foundational building blocks for the next generation of AI systems, and they already power his architecture models. When software outputs feed into financial models or strategic decisions, the results they produce need to be verifiable. For the data they analyze to be comprehensive, it has to be easily accessible.

Emerging frameworks like human layer, BAML, MCP, and AGNTCY are creating a baseline that can make this type of agentic ecosystem possible. Human Layer, for example, delivers a protocol that adds robust human-in-the-loop oversight to autonomous AI agents. It intercepts high-stakes actions and routes them to humans, through Slack, email, SMS, and other channels for approvals, feedback, or escalation, then feeds the response back so agents can continue working safely. MCP is an open standard that standardizes how AI models connect to data inputs, allowing large language models, LLMs, to plug directly into diverse database sand APIs to create a more comprehensive pool of information and agent-to-agent collaboration.

AGNTCY is an open ecosystem for agent-to-agent interoperability that takes the concept one step further by defining a shared identity layer and interaction model for AI agents, enabling tools made by different developers to share information securely and predictably across platforms. AGNTCY's goal is to create an Internet of Agents, similar to the shared data protocols in the 1990s that drove global adoption of the Internet. Together, these frameworks enable AI agents that are not only powerful, but deployable, testable, and maintainable. Building a template for the future of work, shared standards and languages are key to developing AI which can reliably automate mundane work. And they apply far beyond

commercial real estate. At their best, they enable AI agents and agentic systems to transform how professionals and experts work across industries. With AI assistance, we can essentially free people's time, Zamperi concludes. When we can truly trust automated work and recommendations to be comprehensive and accurate, these systems can do more than just improve existing workflows, they can make space for new industries and innovation. For Zamparini, the future of AI is not a better digital assistant, it's a system that lets humans build new things faster, advances current industries, and even spawns new ones. Rather than a productivity upgrade, it's redirecting human intellect and creativity.

AI agents remove the noise, clearing the clutter so builders can move faster, think deeper, and dream bigger. Thank you for listening to this Hacker Noon story, read by Artificial Intelligence. Visit HackerNoon.com to read, write, learn and publish.