Autonomous agent systems using LLMs, MCP, and multi-model orchestration. Built with enterprise security by a PhD who engineers AI at Gauntlet AI.
Chatbots answer questions. Agents do work.
An AI agent takes a goal, decomposes it into steps, accesses your tools and data through secure connections, handles errors, and delivers results. No human in the loop. No babysitting. Real autonomous execution.
This is the most demanding category of AI engineering, requiring deep expertise in LLMs, MCP (Model Context Protocol), multi-model orchestration, and production systems architecture. Dr. Zac Smith has been writing code since 1983. He holds a PhD in Computer Science with a specialization in Cybersecurity Engineering. He builds these systems at Gauntlet AI, where engineers train for $200k+/yr roles. This is his core discipline.
MCP Integration Done Right
Model Context Protocol connects your agents to tools, databases, and APIs through secure, native connections. No fragile middleware. No exposed credentials. Production-grade security from the architecture level.
Multi-Model Orchestration
GPT for generation, Claude for analysis, specialized models for domain work. The right model for each step, coordinated through custom orchestration code. Not a single-model hack.
Enterprise Security Throughout
Agent systems touch your most sensitive data and tools. Every connection encrypted. Every action logged. Every permission scoped. Built by someone whose PhD is in cybersecurity.
Prompt Engineering From 43 Years of Software Discipline
Dr. Smith teaches prompt engineering at Gauntlet AI. But prompt engineering without software engineering discipline produces brittle systems. Both together produce agents that work in production.
The Prototype-to-Production Gap
This is where most AI agent projects die. The demo works. Production breaks. We bridge that gap because we've done it at Fortune 500 scale, repeatedly, for decades.