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Field manual / orientation
Document PRX-FM-001 · Revision A

Training objective / production practice

Master agentic AI engineering, from first principles to production.

Build real agentic systems through provider-agnostic labs, adaptive assessment, and production-grade engineering practice.

Begin orientation Guided sequence · 3 courses
Fig. 01-A
Abstract visualization of an agentic AI system: interconnected nodes coordinating tools and reasoning.
Reference architecture / coordinated reasoning and tool use
02
Training sequence / course index

Curriculum index

A guided path through three courses

Each course builds on the last. The next sequence unlocks after demonstrated mastery of the current material.

  1. Course 01 10 modules

    AI Agent Basics: Foundations for Building Agentic Systems

    1. How an LLM Actually Works
    2. Prompting Fundamentals
    3. Tool & Function Calling
    4. Structured Outputs
    5. Context Windows & Memory Basics
    6. Retrieval-Augmented Generation (RAG) Intro
    7. Single-Agent Loops: ReAct & Plan-Execute
    8. Evaluation & Guardrails
    9. Cost, Latency & the Provider Landscape
    10. Final integration Capstone: Build & Defend an Integrated Single Agent
  2. Course 02 14 modules

    AI Agentic Systems Engineering

    1. Orientation & Toolchain
    2. Production-Grade RAG
    3. RAG Evaluation & Ops
    4. Advanced Tool Use & API Integration
    5. Memory Architectures
    6. Multi-Agent Orchestration
    7. Agent Protocols — MCP & A2A/AG-UI
    8. Workflow & State Machines (Durable Execution)
    9. Reliability Engineering
    10. Serving & Deployment
    11. Evaluation, Testing & Regression
    12. Observability, Tracing & Security
    13. Cost/Performance & Provider Deep Dives
    14. Final integration Capstone — Production-Ready Agentic System
  3. Course 03 11 modules

    Advanced AI Agentic Systems Engineering: Leading Enterprise Agentic Architecture & Build-Out

    1. Enterprise Reference Architectures
    2. Advanced Multi-Agent Coordination
    3. Scaling & Distributed Agent Systems
    4. Platform Decisions: Build-vs-Buy, Gateways, and Routing
    5. Data & Knowledge Architecture at Scale
    6. Evaluation Programs Org-Wide
    7. Governance, Safety, Compliance & Risk
    8. Cost Governance & FinOps/TokenOps
    9. Custom Models & Training
    10. Team Topology, Delivery & Technical Direction
    11. Final integration Capstone: Lead a Full Enterprise Engagement Architecture