Wise Agents

Inspiration I was born and raised in Colombia, I came to the United States pursuing the American Dream. Throughout my life, I've seen how access to technology—and the lack of it—creates massive gaps in productivity, opportunity, and growth. After scaling my previous company, Wise Meetings, to over $1M ARR in Latin America, I saw that businesses everywhere struggled with the same problem: Organizations waste countless hours on tedious, repetitive work that technology should handle. At Stanford, surrounded by AI innovation, I realized that the next leap in productivity wouldn't come from simple automation or prompts — it would come from AI agents capable of thinking, deciding, and executing tasks like real teammates. That was the spark for Wise Agents.

What it does Wise Agents builds agentic AI systems that act like digital teammates — capable of managing workflows, handling decisions, and interacting with real business systems. Our agents automate core functions such as:

-Outreach & prospecting -Quote generation -Meeting handling & scheduling -Video research -Procurement & analytics All inside the user's existing systems, with no workflow disruption.

Challenges I ran into:Along the way, we faced challenges around agent reliability, workflow orchestration, memory sharing, API safety, and integrating with legacy systems, but each obstacle strengthened our approach and belief that AI is evolving from conversation to collaboration.

Accomplishments that I'm proud of: I’m proud of proving that agentic AI can operate as a true business teammate, not just a co-pilot. We built production-grade agents that automate outreach, quoting, scheduling, research, and analytics for real customers across multiple industries. We integrated with complex systems, delivered measurable efficiency gains, and helped teams reclaim hours of manual work. From building a scalable multi-agent architecture to earning early customer revenue and trust, our biggest accomplishment is showing that AI agents are ready to deliver real business value today — not in the future.

What I learned: Through this journey, I learned that the biggest bottleneck in AI adoption isn’t capability. It’s workflow integration, reliability, and context. AI agents need the right architecture, memory, guardrails, and tooling to operate inside real business environments. I also learned that successful automation isn’t about replacing people; it’s about augmenting them so they can focus on higher-leverage work. Most importantly, I discovered that building agentic systems requires a blend of engineering discipline, product empathy, and a deep understanding of business processes.

Built With

• aws-(lambda
• ec2)
• firecrawl
• llama-models
• mea
• multi-agent
• openai-api
• python
• s3