Emanuel Hernández
Emanuel Hernández
About
Detail
AI Solutions Architect | Founder & AI CTO | Building Reliable AI Agents for Real Business Operations | Former Apple Engineer
Costa Rica
I design and deploy AI agents that operate inside real businesses, not just controlled demos.
My focus is building reliable agentic systems that can reason, use tools, access live business data, execute multi-step workflows, and involve people when human judgment is required.
One recent system has handled more than 27,000 real WhatsApp conversations, served over 26,000 customers, executed more than 29,000 grounded tool calls, and captured over 19,000 sales contacts.
That experience reinforced a principle that guides most of my work:
Reliable AI comes from strong system design, not simply from using a larger model.
The systems I build combine:
• Live data and tool integrations • Evaluation and regression testing • State and memory management
Observability and auditability • Clear autonomy boundaries • Deliberate human handoff • Production infrastructure and lifecycle management
I have architected and deployed AI and machine learning systems across customer operations, healthcare, recruiting, finance, sports modeling, and enterprise software.
My background includes building data and machine learning solutions at Apple, developing real-time sports pricing and risk systems, and founding Skillful AI, where I lead the development of production AI agents and the infrastructure required to operate them safely.
At Skillful AI, we are building modular systems for autonomous digital teams. These agents have defined responsibilities, access to tools and business systems, persistent context, evaluation mechanisms, and governance controls.
I am particularly interested in the difficult part of agentic AI:
How do we make autonomous systems reliable when the data changes, tools fail, users behave unpredictably, and mistakes have real operational consequences?
That means treating prompts as production assets, evaluating every meaningful change, grounding responses in live systems, making agent behavior observable, and defining exactly when the AI should stop and involve a person.
I work at the intersection of AI architecture, software engineering, business operations, and technical leadership.
My goal is not to build agents that look impressive in a demo.
It is to build AI systems that organizations can trust in production.
My focus is building reliable agentic systems that can reason, use tools, access live business data, execute multi-step workflows, and involve people when human judgment is required.
One recent system has handled more than 27,000 real WhatsApp conversations, served over 26,000 customers, executed more than 29,000 grounded tool calls, and captured over 19,000 sales contacts.
That experience reinforced a principle that guides most of my work:
Reliable AI comes from strong system design, not simply from using a larger model.
The systems I build combine:
• Live data and tool integrations • Evaluation and regression testing • State and memory management
Observability and auditability • Clear autonomy boundaries • Deliberate human handoff • Production infrastructure and lifecycle management
I have architected and deployed AI and machine learning systems across customer operations, healthcare, recruiting, finance, sports modeling, and enterprise software.
My background includes building data and machine learning solutions at Apple, developing real-time sports pricing and risk systems, and founding Skillful AI, where I lead the development of production AI agents and the infrastructure required to operate them safely.
At Skillful AI, we are building modular systems for autonomous digital teams. These agents have defined responsibilities, access to tools and business systems, persistent context, evaluation mechanisms, and governance controls.
I am particularly interested in the difficult part of agentic AI:
How do we make autonomous systems reliable when the data changes, tools fail, users behave unpredictably, and mistakes have real operational consequences?
That means treating prompts as production assets, evaluating every meaningful change, grounding responses in live systems, making agent behavior observable, and defining exactly when the AI should stop and involve a person.
I work at the intersection of AI architecture, software engineering, business operations, and technical leadership.
My goal is not to build agents that look impressive in a demo.
It is to build AI systems that organizations can trust in production.
Contact Emanuel regarding:
work
Full-time jobs