When Logan Rivenes looked at the job market earlier this year, one thing was clear: Competition was fierce. Every posting seemed to generate a flood of applications within hours, leaving most candidates lost in the noise. As a demand gen professional who had spent 15 years building systems that delivered pipeline and growth, Logan knew the odds were stacked against him. Rather than rely on luck, he decided to build a system of his own.
That system was powered by AI agents.
Logan is no stranger to tinkering. He describes himself as a “sales rep turned marketer” who loves DIY projects, from rebuilding a laundry room to restoring a 1979 ski boat. For him, the appeal of AI was not about adopting the newest shiny tool. It was about figuring out how to bend technology to solve a pressing problem. And at that moment, the problem was finding his next role.
Logan’s philosophy is straightforward: “out of the box is for amateurs.” Default configurations rarely fit the unique needs of a business or a person. Many AI tools offer surface-level solutions that leave real gaps untouched. Logan wanted to go deeper.
Instead of settling for job boards and generic searches, he began piecing together his own workflow. Using free tiers of Clay and Apollo for company research, Google Sheets for organizing data, and Agent.ai for automation, he created a system that could cut through the noise.
The starting point was company research. Logan knew the type of organizations he wanted to target: smaller HR tech firms where his background in demand generation and marketing operations would be valuable. Standard job boards offered no easy way to filter by size or firmographics. That limitation pushed him to build.
Here is how his workflow came together:
Rather than drown in an ocean of listings, Logan now had a focused shortlist that matched his skills and interests.
The impact of this system was immediate. By bypassing the crowded channels where “a thousand applications” piled up on every role, Logan was able to discover openings that were either underexposed or not listed on LinkedIn at all.
Once he identified promising opportunities, Logan leaned on his sales background. He researched companies, drafted tailored cover letters with help from ChatGPT, and reached out directly to contacts within the organization. The combination of targeted discovery and proactive outreach dramatically improved his hit rate.
Most importantly, it worked. Logan’s agent-powered process led him to HRBench, a company building innovative HR data solutions. He landed the role and now leads demand generation there.
Logan’s story isn't just about securing one job. It's proof that AI agents can make a tangible difference in everyday life. For users curious about experimenting, his journey highlights a few important takeaways.
First, you don't need to be an engineer to start. Logan compares building with Agent.ai to setting up workflows in HubSpot or drawing out a flowchart. The core is simply defining your goal, mapping steps, and connecting them in order. If you can think in terms of “if this, then that,” you can build an agent.
Second, you can begin with what's free and available. Logan deliberately stitched together free plans from multiple platforms. The trade-off was a bit of manual effort, but the reward was a powerful workflow without added cost. For anyone hesitant to try agents due to budget concerns, his example shows that experimentation can begin with zero spend.
Finally, tinkering is the real teacher. Logan emphasizes that the hardest part is deciding what to build, not the act of building. Once you start, you'll discover gaps, hit obstacles, and iterate. That process is where learning happens.
Not all of Logan’s agents were tied to his career. During the off-season of his fantasy football league, he built an “AI general manager” to help manage his struggling team.
The agent pulled roster data, free agent availability, and trade opportunities into Google Sheets, then used ChatGPT to suggest moves. It wasn't perfect, but it gave Logan insights and sparked conversations with his league mates. More importantly, it was fun.
This playful project demonstrates a critical point: You don't need a life-or-death challenge to try agents. Sometimes the best way to learn is through something enjoyable, whether that's managing a fantasy team, planning a trip, or organizing a hobby project. Fun use cases build confidence and skills that carry over into professional challenges.
Logan believes we're living in what he calls “the generalist’s era.” In his view, the ability to combine basic knowledge across multiple areas is more valuable than deep expertise in one. Tools like Agent.ai expand what generalists can accomplish, turning curiosity into action.
For job seekers, this mindset is empowering. You don't need to wait for a company to release a new feature or for a course to certify you. With agents, you can identify opportunities, automate research, and personalize outreach on your own. For professionals, the same principle applies to projects at work, side hustles, or creative experiments.
The significance of Logan’s journey is not only that he landed a job with AI. It's that he did so by combining the skills he already had with a willingness to try something new. He did not become a developer. He became a builder by approaching agents as workflows and experimenting with what was available.
His story illustrates the potential for any user who is willing to tinker. Whether you're navigating a job search, exploring a passion project, or simply curious about AI, agents provide a practical path forward.
If you're reading this and wondering how to begin, take Logan’s advice: Pick a problem that matters to you, start small, and don't be afraid to experiment. The tools are within reach; the only step left is to try.