Enterprise AI Engineering Advanced

AI Engineering Advanced: Production-Grade Enterprise AI for Senior Teams

Move beyond single agents to scalable, governed multi-agent systems. Teams design planner-executor architectures with memory and tool use, practice semantic routing, and explore Model Context Protocol for controlled, multi-endpoint execution—suitable for regulated, on-prem or air-gapped environments. This course is part of Caltech’s Enterprise AI Engineering series (Foundations, Intermediate, Advanced), a sequence that organizations can target to different skill levels; learners may progress through all three over time. All three tiers belong to the same series and can be sequenced or spaced to cascade learning across roles—enabling cohorts to advance from foundational literacy to applied prototyping and, ultimately, governed multi-agent design.

  • Learners Foundational
  • Time Client definable
  • Duration 5 Days; Definable
  • Program Type Customizable Programs
  • Certificate Type Certificate
  • Format
    Any Format/Location
  • CEUS Available
  • PDUS Available
  • Program Number AIEngA-Custom
  • Fees Group Rate
  • See full course info

Move beyond single agents to scalable, governed multi-agent systems. Teams design planner-executor architectures with memory and tool use, practice semantic routing, and explore Model Context Protocol for controlled, multi-endpoint execution—suitable for regulated, on-prem or air-gapped environments. This course is part of Caltech’s Enterprise AI Engineering series (Foundations, Intermediate, Advanced), a sequence that organizations can target to different skill levels; learners may progress through all three over time. All three tiers belong to the same series and can be sequenced or spaced to cascade learning across roles—enabling cohorts to advance from foundational literacy to applied prototyping and, ultimately, governed multi-agent design.

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Enterprise AI Engineering Advanced

Program Experience

Participants will architect multi-agent workflows for targeted functions (for example, procurement, HR, or R&D). We implement planning, tool use, and memory; add semantic routing; and integrate MCP for policy-aligned, auditable control across endpoints. The in-class project demonstrates orchestration, governance touchpoints, and scale considerations—evaluated via agent-to-agent reliability tests. Teams leave with reference pipelines, checklists, and a next-step outline to inform internal hardening or subsequent pilots. 

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Course Info

Benefits

Your team will learn to:

  • Architect planner-executor multi-agent systems with tool use and memory

  • Apply semantic routing and policy checks for safe task delegation across agents

  • Use Model Context Protocol (MCP) for multi-endpoint control with auditability

  • Run agent-to-agent reliability tests and design evaluation gates for scale

  • Select deployment options (closed API, open-weight, on-prem/air-gapped) by risk and cost

  • Produce governed, instrumented prototypes suitable for internal review paths

Meet the Educators

Instructors

Picture of Mike Frantz, PhD

Mike Frantz, PhD

AI Systems

Photo of Harish Kashyap

Harish Kashyap

AI Systems