Agentic Engineering Institute/C020: Agentic System Development Lifecycle

  • $249

C020: Agentic System Development Lifecycle

  • Course
  • 12 Lessons

Master the 7-loop operating model that turns agentic AI from fragile experiments into governed, production systems. Learn how to build, validate, operate, and scale cognitive systems with continuous, auditable evidence. A 3-hour hands-on course for teams moving beyond deterministic software to provable trust. Based on AEBOP T4.3.

AEI members save 20% with code MEM_C20_20.

Contents

Module 1: Foundation & Mindset Shift

Traditional software development assumes systems obey code, but agentic systems reason under uncertainty. This module exposes the five critical breakdowns where deterministic methods fail for cognitive systems and introduces the fundamental shift from linear phases to continuous loops. You'll understand why SDLC processes create exponential risk when applied to autonomous intelligence.

We establish the 7-loop operating model (Intent→Design→Build→Assure→Operate→Reflect→Adapt) as the new foundation for agentic engineering. You'll learn how living loops replace static phases, why evidence must flow continuously rather than being checkpointed, and how the Trust Ledger becomes your system's permanent memory of cognition under governance.

Lesson 1.1: Why Traditional SDLC Fails for Agentic Systems
Preview
Lesson 1.2: The 7-Loop Operating Model
Module 1 Mastery Assessment

Module 2: The Core Loops - Intent, Design, Build

Agentic systems begin with clear purpose and boundaries before any code is written. This module teaches how to create executable intent specifications (intent.yaml) that define measurable success criteria and non-negotiable boundaries. You'll then architect cognition through reasoning blueprints that specify decision flows, guardrails, and escalation paths in machine-readable formats.

The Build Loop transforms these specifications into implemented intelligence with embedded evidence generation. You'll learn structured logging for reasoning traces, integration patterns for validation hooks, and memory management for context handling. Every implementation decision must leave audit trails that connect back to the original intent and design.

Lesson 2.1: Intent & Design Loops
Lesson 2.2: Build Loop
Module 2 Mastery Assessment

The Assurance Loops - Assure, Operate, Reflect

Deployment is the starting line for agentic systems, not the finish. This module covers continuous validation through automated QA agents, drift detection, and semantic testing that runs faster than system evolution. You'll implement the Assure Loop with golden prompt suites, reasoning validation, and trust record creation that feeds directly into your governance systems.

Operation becomes active supervision with rollback authority and bounded autonomy. You'll learn to configure supervisor agents, establish operational dashboards, and implement reflection processes that convert incidents into learning. The Reflect Loop ensures every failure strengthens your system's governance rather than weakening trust.

Lesson 3.1: Assure Loop
Lesson 3.2: Operate & Reflect Loops
Module 3 Mastery Assessment

Module 4: Integration, Maturity & Scaling

Mature A-SDLC implementations create seamless evidence flow between all seven loops. This module teaches daily operational rhythms—morning verification, mid-day assurance, evening reflection—that maintain continuous governance. You'll implement loop handoff protocols, Trust Stand-Up rituals, and ledger integrity checks that prevent evidence gaps between teams and phases.

Scaling requires measurable progress and organizational alignment. You'll learn the 7-tier maturity ladder, implement metrics with action logic, and create role evolution paths that grow with your capabilities. Finally, we cover enterprise adoption patterns, compliance integration, and leadership dashboards that transform A-SDLC from project practice to organizational discipline.

Lesson 4.1: Loop Integration & Daily Operations
Lesson 4.2: Maturity, Metrics & Enterprise Scaling
Module 4 Mastery Assessment