Agentic AI Developer Training
End-to-End Autonomous AI Agent Development Lifecycle & Study Guide
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This document is the official academic curriculum guide covering the design, security and deployment of advanced, autonomous and collaborative AI agents running on Next.js 15Next.jsThe React framework for the web, built and maintained by Vercel. Powers full-stack apps with App Router, Server Components and Turbopack.nextjs.org →, Go (Golang)GoAn open-source programming language supported by Google. Built for simplicity, concurrency and fast, reliable backend services.go.dev →, iOS (Swift)SwiftApple's powerful and intuitive programming language for iOS, macOS, watchOS and beyond — safe, fast and expressive.swift.org →, Android (Kotlin)KotlinA modern language by JetBrains and Google's official choice for Android development — concise, safe and multiplatform.kotlinlang.org →, Terminal/CLI applications, Cursor IDECursorThe AI code editor by Anysphere. Agentic coding with frontier models, deeply integrated into the editor workflow.cursor.com →, OpenCodeOpenCodeAn open-source AI coding agent that lives in your terminal — model-agnostic, scriptable and built for autonomous workflows.opencode.ai →, GitHub CopilotGitHub CopilotGitHub's AI pair programmer. Code completions, chat and agentic coding across the IDE, terminal and github.com.github.com/features/copilot →, frontier models (Composer 2.5, Claude Opus 4.8, Fable 5), locally hosted open-weight models and independent cloud servers (VPS).
The logos above indicate the AI tools and platforms we actively use; all trademarks belong to their respective owners.
We know these AI tools inside out — and we stand behind that. For nearly four years, we have been delivering next-generation SDLCs to our customers, successfully and at scale.
The AI SDLC is no longer the classic delivery pipeline. When agents write, test and ship the code, operations refract into three interlocking disciplines — each covered in depth by this curriculum.
CI/CD, IaC and progressive delivery for code that agents write and ship around the clock.
Pipelines, infrastructure-as-code, container runtimes, canary rollouts and instant rollbacks on independent VPS targets. The curriculum treats deployment as an agent-driven loop: the fleet builds, the gates verify, every rollout is reversible by design — and a human approves anything that is not.
Guardrails, secret hygiene and threat models for systems where a model is also a developer.
Secret management, SAST and dependency scanning, prompt-injection and tool-abuse defense, least-privilege agent permissions and HITL gates before irreversible actions. You learn to threat-model not just the application but the agents building it — with full audit trails.
Eval harnesses, golden traces, token budgets and model routing across frontier and local models.
Model selection and routing across Composer 2.5, Claude Opus 4.8, Fable 5 and open-weight local models; eval suites and golden traces as regression tests; reasoning-chain observability; cost and latency SLOs; and when to reach for RAG, fine-tuning or pure prompting.
Custom programs and project partnership
Enterprises and large-scale engineering organizations can apply through a separate channel: MasterFabric designs private, tailored training programs and provides long-term project partnership — from agentic SDLC adoption to production rollout.
- +Tailored curriculum & private cohorts
- +Agentic SDLC adoption roadmap
- +Project partnership with MasterFabric
Why the Agentic AI Developer?
How the software engineer arrived at this stage — the last five years, refracted.
One input. A spectrum of autonomous output.
The Manual Era
Developers write every line by hand. Autocomplete suggests tokens, not intent. Stack Overflow is the second monitor.
The toolchain was deterministic: linters, debuggers and rule-based IntelliSense. Knowledge lived in documentation and Q&A forums, and the feedback loop between an idea and working software was measured in hours. Editors like VS Code dominated the desktop — but they understood characters, not goals.
AI-assisted code in production codebases
Token-level autocomplete: the editor knows syntax, not intent.
The Copilot Moment
Inline AI completion goes mainstream. Engineers start accepting whole functions. The keyboard remains the bottleneck.
GitHub Copilot put a language model inside the editor. Ghost text proposed entire functions from a single comment, and 'tab-tab programming' was born. Skeptics worried about licensing and correctness; pragmatists simply shipped faster. The editor learned to finish your sentence — but still not to start it.
Developers using AI completion weekly
Press Tab to accept — whole functions proposed from one comment.
The Conversation
Chat-first development emerges. LLMs explain, refactor and review. The developer becomes an editor of generated drafts.
ChatGPT-class models moved development into a dialogue. Engineers pasted stack traces and got explanations; refactors and test suites came back as drafts. Context windows grew, RAG appeared, and 'prompt engineering' entered job descriptions. The bottleneck shifted from typing speed to question quality.
Code review time reduced by AI tooling
Development becomes a dialogue: explain, refactor, review.
The Tool-Use Awakening
Models stop talking and start doing: function calling, JSON schemas, retrieval, terminals. The IDE becomes an execution surface.
Function calling and JSON Schema gave models hands. The Model Context Protocol standardized how tools are described and shared. Editors like Cursor let the model read files, run terminals and apply edits — under supervision. The unit of work changed from 'suggestion' to 'verified action'.
Enterprise pilots involving tool-calling agents
The model stops describing the fix and starts performing it.
The Agentic Shift
Long-horizon agents plan, execute and self-correct across repos, servers and devices. Human-in-the-loop becomes an architecture, not an apology.
Long-horizon agents arrived: they planned multi-step work, kept persistent memory, recovered from their own failures and paused for approval at dangerous boundaries. Background agents worked while engineers slept. Code review, CI triage and dependency upgrades became delegations rather than chores.
Teams shipping agent-driven workflows
Failure is no longer terminal — it is input for the next attempt.
The Orchestrator
The engineer no longer writes most of the code — they design, constrain and audit fleets of agents. A new discipline demands a new curriculum.
Today the senior engineer runs a fleet: an orchestrator decomposes intent, workers implement in parallel, judges evaluate output, and guardrails keep a human in the loop for irreversible actions. Architecture, constraints and verification — not syntax — are the craft. That craft is exactly what this curriculum teaches.
Engineering roles requiring agent orchestration skills
The engineer designs, constrains and audits — the fleet executes.
Keep scrolling — a new horizon rises for the engineer.
The prism is the developer. The beam is intent. What comes out the other side is no longer a single line of code — it is a coordinated spectrum of autonomous agents. This curriculum trains the engineer who holds the prism.
Curriculum Weight Distribution
Relative exam and project weight of each domain, sourced live from the markdown curriculum.
Domain 1: Prepare Agent Architecture and SDLC Processes
Domain 2: Implement Tool Use and Environment Interaction
Domain 3: Manage Memory, State, and Execution
Domain 4: Perform Evaluation, Error Analysis, and Tuning
Domain 5: Orchestrate Multi-Agent Coordination
Domain 6: Implement Guardrails and Accountability
Domain 7: Mobile Agent Integration & Platforms
Domain 8: CLI, System Automation and Terminal Applications
Curriculum Domains
Every section below is rendered from markdown source files. Edit the markdown — the site updates on the next request.
Introduction and the Spiral Learning Model
Read in A4 format →Domain 1: Prepare Agent Architecture and SDLC Processes
Read in A4 format →Domain 2: Implement Tool Use and Environment Interaction
Read in A4 format →Domain 3: Manage Memory, State, and Execution
Read in A4 format →Domain 4: Perform Evaluation, Error Analysis, and Tuning
Read in A4 format →Domain 5: Orchestrate Multi-Agent Coordination
Read in A4 format →Domain 6: Implement Guardrails and Accountability
Read in A4 format →Domain 7: Mobile Agent Integration & Platforms
NEW MODULERead in A4 format →Domain 8: CLI, System Automation and Terminal Applications
NEW MODULERead in A4 format →Certification & Graduation Project
Read in A4 format →Sample Certificate
Issued upon completion of all curriculum domains. The module list below is generated live from the markdown curriculum — publish a new module and it automatically appears on every future certificate.
Certificate of CompletionAgentic AI Developer Training
This certifies that
Ali Yurtsever
has successfully completed the following curriculum modules
Contact & Apply
Participants who wish to join must submit their information. Fill in the form — send it by email, or let an AI agent assemble and verify your application for you.
July 1, 2026
Max capacity: 25 seats per cohort
September 2026
Max capacity: 25 seats per cohort
Verify you are human to unlock sending
Apply through an AI agent
These deeplinks build an application prompt from your details and hand it straight to the agent — it verifies your information and drafts the email to academy@masterfabric.co for you. The prompt templates live in the project's /prompts reference folder.
You are an application assistant for MasterFabric Academy. Using the participant information below, prepare a formal application for the "Agentic AI Developer Training" program and draft an email ready to be sent to academy@masterfabric.co. ## Participant Information - Full name: — - Email: — - Role / Company: — - Experience: — - Preferred cohort: Cohort 1 — July 1, 2026 - Motivation: — ## Program Facts - Cohort 1 starts: July 1, 2026 - Cohort 2 starts: September 2026 - Maximum capacity: 25 seats per cohort - Applications: academy@masterfabric.co - Curriculum: 8 domains — agent architecture, tool use & MCP, memory/state, evaluation & self-healing, multi-agent orchestration, guardrails & HITL, mobile agent integration (iOS/Android), CLI & terminal automation. - Model stack: Composer 2.5, Claude Opus 4.8, Fable 5, plus locally hosted open-weight models (Ollama / vLLM / on-device). ## Your Task 1. Validate the participant information above; ask the participant for any missing or inconsistent fields before proceeding. 2. Draft a short, professional application email (subject + body) addressed to academy@masterfabric.co. 3. Mention the preferred cohort explicitly and note that capacity is limited to 25 seats. 4. Show the final draft to the participant and, if approved, help them send it.