Advanced Architectures for Vertical AI Agents: 90-Lesson Curriculum (Code-First)

This curriculum combines theoretical understanding with immediate, hands-on coding for each lesson, designed for AI engineers and solution architects aiming for enterprise-grade VAIA deployment.

Module 1: Foundations of Generative AI and Autonomous Agents (15 Lessons)

L1: Python Setup for AI

• Learning Objective: Set up a robust Python development environment for large-scale AI projects (virtual environments, dependency management).

• Hands-On Coding Focus: Configure venv, install essential libraries (numpy, pandas, fastapi, pydantic).

L2: Advanced Python & Libraries

• Learning Objective: Master advanced Python concepts (decorators, async/await, dataclasses) and data structures essential for AI agent development.

• Hands-On Coding Focus: Implement async functions for non-blocking I/O; use pydantic for data validation.

L3: Transformer Architecture Deep Dive

• Learning Objective: Analyze the core mechanics of Transformer models, self-attention, and positional encoding.

• Hands-On Coding Focus: Step through a simplified Transformer block implementation (conceptual, not full training).

L4: Understanding LLM Parameters

• Learning Objective: Explore the impact of parameter counts, context windows, and training data on LLM capabilities and cost.

• Hands-On Coding Focus: Analyze API documentation for max_tokens, pricing models for different LLMs.

L5: The 2025 Model Landscape & Selection

• Learning Objective: Comparative analysis of GPT-5, Gemini 2.0, Claude 3.7, Llama 3.1, and the strategic role of efficient SLMs like Phi-4.

• Hands-On Coding Focus: Compare inference speeds/costs via API calls (OpenAI, Anthropic, Hugging Face Hub for open-source).

L6: Interacting with LLM APIs

• Learning Objective: Master API access, authentication, rate limits, and secure credential management for proprietary LLM services.

• Hands-On Coding Focus: Write Python scripts to make secure, authenticated calls to OpenAI/Anthropic APIs.

L7: Basic LLM Prompting & JSON Output

• Learning Objective: Design prompts for clear instructions, structured output (JSON), and error handling from LLM responses.

• Hands-On Coding Focus: Craft prompts for JSON extraction; parse LLM output, handle malformed JSON.

L8: Core Agent Theory: Definition & Components

• Learning Objective: Define the principles of autonomy, deliberation, and the necessary architectural components (Planning, Memory, Tool Use).

• Hands-On Coding Focus: Implement a basic “decision_maker” function; trace its logic.

L9: Implementing Agent Memory

• Learning Objective: Differentiate conversational and long-term memory; implement persistent context storage for personalized agent experiences.

• Hands-On Coding Focus: Use dict for short-term memory; simulate long-term memory with a simple file-based store.

L10: Hands-On: Building a Simple Agent

• Learning Objective: Build a minimal agent demonstrating memory retention and elementary goal-seeking behavior without external frameworks.

• Hands-On Coding Focus: Develop a Python class SimpleAgent with remember and act methods.

L11: Prompt Engineering Mastery: CoT

• Learning Objective: Master Chain-of-Thought (CoT) prompting for structured reasoning, logical step-by-step thinking, and complex task decomposition.

• Hands-On Coding Focus: Design prompts explicitly requiring CoT; evaluate clarity of LLM’s reasoning trace.

L12: Prompt Engineering Mastery: Few-Shot

• Learning Objective: Implement few-shot prompting techniques to guide LLMs with example inputs and desired outputs for specific tasks.

• Hands-On Coding Focus: Create a few-shot prompt for a classification task and test its performance.

L13: Introduction to Context Engineering

• Learning Objective: Optimizing prompt utility; managing complex context windows; techniques for compaction and filtering context flow.

• Hands-On Coding Focus: Implement a simple text summarizer to reduce context size; explore token counting libraries.

L14: State Management for Agents

• Learning Objective: Understand and implement robust state management for agents, ensuring continuity across turns and resilience to failures.

• Hands-On Coding Focus: Use dataclasses or Pydantic models to define agent state; serialize/deserialize state.

L15: Project 1: Basic Conversational Agent

• Learning Objective: Develop a Python-based agent prototype demonstrating persistent memory and elementary goal-seeking behavior.

• Hands-On Coding Focus: Develop a command-line conversational agent; integrate memory and state management.

Module 2: Building Blocks: LLM APIs, Tools, and Core RAG (15 Lessons)

L16: Tool Use & Function Calling

• Learning Objective: Implement the core mechanism for LLMs to interact with external data and operational systems via function calls.

• Hands-On Coding Focus: Create a simple Python function (get_weather); integrate it as a tool with OpenAI’s function calling API.

L17: Designing Robust Tool Schemas

• Learning Objective: Use structured data validation (e.g., Pydantic) to ensure unambiguous tool function signatures, parameters, and return types.

• Hands-On Coding Focus: Define tool schemas using JSONSchema or Pydantic for precise LLM interaction.

L18: Hands-On: Tool Execution Loop

• Learning Objective: Implement and debug a complete tool execution loop, handling LLM tool calls, function execution, and result feedback.

• Hands-On Coding Focus: Build a ToolExecutor class that dynamically calls functions based on LLM output.

L19: Introduction to Naïve RAG

• Learning Objective: Understand the ingestion pipeline, document chunking strategies, and the static query-retrieve-respond model.

• Hands-On Coding Focus: Implement basic document chunking (e.g., character-based); use a simple in-memory dict as a knowledge base.

L20: Vector Databases & Indexing

• Learning Objective: Select and manage vector stores (Pinecone, ChromaDB); optimize embeddings and metadata for retrieval performance.

• Hands-On Coding Focus: Set up a local ChromaDB instance; index sample documents and perform vector similarity search.

L21: Advanced Embeddings & Chunking

• Learning Objective: Explore different embedding models and advanced chunking strategies (e.g., semantic chunking, recursive chunking) for RAG.

• Hands-On Coding Focus: Experiment with Sentence Transformers; implement different chunking algorithms.

L22: Retrieval Optimization: Reranking

• Learning Objective: Implement post-retrieval reranking techniques to boost the relevance and quality of retrieved documents for the LLM.

• Hands-On Coding Focus: Integrate a reranking model (e.g., from Hugging Face) into a basic RAG pipeline.

L23: Hybrid Search & Filtering

• Learning Objective: Combine vector search with keyword search (hybrid search) and metadata filtering for more precise retrieval.

• Hands-On Coding Focus: Implement a hybrid search query combining vector search with SQL-like metadata filters in ChromaDB.

L24: Modular RAG with LangChain/LlamaIndex

• Learning Objective: Utilize LangChain or LlamaIndex to construct flexible, modular, and extensible RAG pipelines.

• Hands-On Coding Focus: Build a RAG chain using RetrievalQA or a custom Runnable sequence in LangChain.

L25: Advanced Prompting for RAG

• Learning Objective: Design system prompts that guide the LLM to use retrieved context effectively, avoiding hallucinations and staying “on-topic.”

• Hands-On Coding Focus: Craft RAG-specific system prompts; test grounding by intentionally providing irrelevant context.

L26: Handling Ambiguity & Multi-Turn RAG

• Learning Objective: Strategies for managing ambiguous queries and maintaining context across multi-turn RAG interactions.

• Hands-On Coding Focus: Implement a ConversationBufferMemory with a RAG chain; test multi-turn Q&A.

L27: RAG Evaluation Metrics & Benchmarks

• Learning Objective: Understand key metrics for RAG performance (groundedness, relevance, completeness) and establish evaluation benchmarks.

• Hands-On Coding Focus: Use RAG evaluation libraries (e.g., Ragas) to run basic evaluations on a RAG system.

L28: Project 2: Tool-Equipped Agent Build

• Learning Objective: Build an agent capable of retrieving information from a vector database and performing tool calls based on user intent.

• Hands-On Coding Focus: Combine the SimpleAgent from L10 with a RAG pipeline and a custom tool; integrate in a CLI app.

L29: Project 2 Review: Accuracy & Latency

• Learning Objective: Evaluate tool reliability, RAG groundedness, and system latency against predefined performance targets.

• Hands-On Coding Focus: Develop unit tests for tool calls; manually verify RAG responses against source documents.

L30: Introduction to Observability for RAG

• Learning Objective: Begin integrating observability tools to track RAG component performance, latency, and token usage.

• Hands-On Coding Focus: Add print statements or basic logging to trace RAG pipeline steps; measure component execution times.

Module 3: Agentic Reasoning, Planning, and Advanced Architectures (15 Lessons)

L31: Deep Dive into ReAct Planning

• Learning Objective: Implement the iterative Observe $\rightarrow$ Thought $\rightarrow$ Action $\rightarrow$ Observe loop for adaptive decision-making in agents.

• Hands-On Coding Focus: Implement a basic ReAct loop in Python; use LLM to generate Thought and Action based on Observation.

L32: Hands-On: ReAct Agent Build

• Learning Objective: Construct a simple ReAct agent capable of interacting with a set of predefined tools to achieve a goal.

• Hands-On Coding Focus: Develop a ReActAgent class; integrate tools; test with simple tasks (e.g., “Find current stock price of Google”).

L33: Implementing Self-Correction (Reflexion)

• Learning Objective: Integrate advanced loops (e.g., Reflexion) to improve agent outcomes autonomously through iterative critique and self-reflection.

• Hands-On Coding Focus: Add a Reflect step to the ReAct loop, where the LLM evaluates its previous steps and refines its plan.

L34: Planning Loop Controls & Budgeting

• Learning Objective: Implement mandatory iteration limits, token boundaries, and cost controls to manage agent autonomy and budget adherence.

• Hands-On Coding Focus: Add max_iterations and max_tokens_per_turn to the ReAct agent; raise exceptions if limits are exceeded.

L35: Architecting Agentic RAG

• Learning Objective: Understand the necessary multi-agent architecture for advanced retrieval, validation, and traceable response generation.

• Hands-On Coding Focus: Diagram and sketch the component interactions of an Agentic RAG system (Planner, Retriever, Validator, Synthesizer).

L36: The Planner Agent: Query Decomposition

• Learning Objective: Design the Planner Agent’s logic for interpreting complex queries, decomposing them into sub-questions, and formulating initial retrieval strategies.

• Hands-On Coding Focus: Implement a PlannerAgent that uses an LLM to break down a user query into a list of search queries.

L37: The Retriever Agent & Reranker Tool

• Learning Objective: Build the Retriever Agent to execute searches and integrate reranker tools to refine and boost the relevance of retrieved chunks.

• Hands-On Coding Focus: Implement a RetrieverAgent that calls the Planner, executes vector search, and applies reranking.

L38: The Validator Agent: Factual Consistency

• Learning Objective: Design the Validator Agent: a critical compliance check for assessing retrieved content for factual consistency and redundancy.

• Hands-On Coding Focus: Create a ValidatorAgent that uses an LLM to cross-reference retrieved documents for contradictions.

L39: The Validator Agent: Risk & Compliance

• Learning Objective: Implement the Validator Agent’s logic for risk alignment against domain-specific rules and policy adherence.

• Hands-On Coding Focus: Define a set of “compliance rules”; the Validator Agent checks retrieved content against these rules.

L40: Fallback Logic & Self-Healing

• Learning Objective: Configure Validator $\leftrightarrow$ Planner feedback loops to trigger query refinement and system adaptation upon retrieval or consistency failure.

• Hands-On Coding Focus: Implement retry logic: if Validator fails, instruct Planner to reformulate the query and re-execute RAG.

L41: The Synthesizer Agent & XAI

• Learning Objective: Build the Synthesizer Agent to generate the final, coherent response, enforcing CoT reasoning and embedding explicit source citations.

• Hands-On Coding Focus: Implement a SynthesizerAgent that takes validated context and generates a response with citations.

L42: Traceability Layer Implementation

• Learning Objective: Implement the full audit trail for Agentic RAG, logging every step from Planner intent to Validator’s risk assessment.

• Hands-On Coding Focus: Integrate logging into each Agentic RAG component, capturing inputs, outputs, and decision points.

L43: Hands-On: Agentic RAG End-to-End

• Learning Objective: Deploy a full Agentic RAG system, demonstrating internal validation, self-correction, and traceable output generation.

• Hands-On Coding Focus: Integrate Planner, Retriever, Validator, and Synthesizer agents into a single, executable pipeline.

L44: Evaluating Agentic RAG Reliability

• Learning Objective: Apply advanced evaluation metrics (e.g., faithfulness, answer relevance, context recall) to Agentic RAG systems.

• Hands-On Coding Focus: Use Ragas or similar tools for a comprehensive evaluation of the Agentic RAG system built in L43.

L45: Project 3: Autonomous Research Agent

• Learning Objective: Develop a fully implemented Agentic RAG system demonstrating internal validation, self-correction, and traceable output generation for a specific domain.

• Hands-On Coding Focus: Develop an Agentic RAG system for a mini-research task; provide evaluation results.

Module 4: Multi-Agent Systems and Orchestration Frameworks (15 Lessons)

L46: MAS Theory & Specialization

• Learning Objective: Design agent teams with clear role definition, specialization, and coordination mechanisms for complex problems.

• Hands-On Coding Focus: Diagram a multi-agent system for a complex task (e.g., event planning); define roles and communication flows.

L47: Introduction to Microsoft AutoGen

• Learning Objective: Explore AutoGen as a framework for building multi-agent conversational systems through peer-to-peer collaboration.

• Hands-On Coding Focus: Set up an AutoGen environment; run a basic two-agent conversation (e.g., User Proxy and Assistant Agent).

L48: AutoGen: Collaborative Problem Solving

• Learning Objective: Implement multi-agent systems in AutoGen for collaborative problem-solving, incorporating self-reflection loops.

• Hands-On Coding Focus: Build an AutoGen GroupChat with multiple agents working on a shared problem; observe agent interactions.

L49: CrewAI: Role-Based Execution

• Learning Objective: Utilize the role/task abstraction model of CrewAI for rapid design and synchronous execution of agent teams.

• Hands-On Coding Focus: Develop a simple CrewAI “crew” with defined agents, tasks, and process to achieve a specific goal.

L50: Hands-On: CrewAI Agent Team Build

• Learning Objective: Construct a collaborative agent team using CrewAI to automate a multi-step project (e.g., content generation, market analysis).

• Hands-On Coding Focus: Build a more complex CrewAI application; include tool use and sequential/hierarchical task execution.

L51: Coordination Strategy I: Hierarchy & Allocation

• Learning Objective: Implement hierarchical decomposition structures and deterministic task allocation, utilizing GFM orchestrators for sub-agent management.

• Hands-On Coding Focus: Architect a master OrchestratorAgent (using a GFM) that delegates tasks to WorkerAgents via API calls.

L52: Coordination Strategy II: Consensus & Fallbacks

• Learning Objective: Implement consensus voting for decision harmonization across agents and utilize workflow checkpoints for fail-safe recovery.

• Hands-On Coding Focus: Design a ConsensusAgent that collects opinions from other agents and makes a final decision; add rollback points.

L53: Coordination Economics & Resource Limits

• Learning Objective: Master Coordination Economics: setting strict token, step, and time boundaries to control runaway compute costs in MAS.

• Hands-On Coding Focus: Implement max_rounds or max_tokens limits in AutoGen/CrewAI; monitor token usage per agent.

L54: Advanced Agent Personalization

• Learning Objective: Implement sophisticated personalization techniques for agents, ensuring they adapt to individual user preferences and historical data.

• Hands-On Coding Focus: Integrate user profiles and preference stores into agent memory; agents adapt responses based on profile.

L55: Enterprise Orchestration: Google ADK

• Learning Objective: Explore Google ADK for enterprise-grade orchestration, focusing on seamless integration with corporate cloud services.

• Hands-On Coding Focus: Explore Google Cloud Agent Builder console; understand components for enterprise agent deployment.

L56: Enterprise Orchestration: Microsoft Semantic Kernel

• Learning Objective: Utilize Microsoft Semantic Kernel to bridge AI capabilities with existing legacy tech stacks and Microsoft ecosystems.

• Hands-On Coding Focus: Integrate a simple Semantic Kernel plugin with an existing C# or Python application.

L57: Sub-Agent Architectures

• Learning Objective: Design nested agent systems where specialized sub-agents handle modular micro-tasks, optimizing overall efficiency.

• Hands-On Coding Focus: Implement an agent whose “tool” is actually another sub-agent, demonstrating nested calls.

L58: Hands-On: Designing a Multi-Modal Agent

• Learning Objective: Develop frameworks capable of unifying processing across text, vision, and voice for comprehensive, unified workflows.

• Hands-On Coding Focus: Integrate an image analysis tool (e.g., Pillow + LLM vision API) into an agent for multi-modal reasoning.

L59: MAS Evaluation & Debugging

• Learning Objective: Strategies for evaluating and debugging complex multi-agent systems, including interaction logging and performance profiling.

• Hands-On Coding Focus: Use a logging framework (e.g., loguru) to trace inter-agent communication; profile MAS execution.

L60: Project 4: Collaborative Agent Team

• Learning Objective: Develop a fully orchestrated multi-agent system solving a complex, decomposed task using a modern MAS framework (AutoGen/CrewAI).

• Hands-On Coding Focus: Develop a MAS that performs a multi-step business process (e.g., market research, competitive analysis).

Module 5: Enterprise MLOps and Productionizing VAIAs (15 Lessons)

L61: Introduction to MLOps for Agentic AI

• Learning Objective: Establish the integrated lifecycle framework required for continuous development, deployment, and adaptive maintenance of VAIAs.

• Hands-On Coding Focus: Map a theoretical MLOps pipeline onto the agent development lifecycle.

L62: Achieving MLOps Level 1 Maturity

• Learning Objective: Automate the CI/CD/CT/CM pipeline for recurrent training and deployment in production environments.

• Hands-On Coding Focus: Set up a basic CI/CD pipeline (e.g., GitHub Actions) for code linting and unit testing of agent components.

L63: Continuous Integration (CI) for Agent Logic

• Learning Objective: Implement pipeline checks for code quality, tool validation, schema adherence, and security compliance.

• Hands-On Coding Focus: Add automated tests for tool function schemas, LLM prompt robustness, and agent logic.

L64: Continuous Delivery (CD) & Containerization

• Learning Objective: Master containerization (Docker) for consistent agent deployment across environments; understand basic Kubernetes concepts.

• Hands-On Coding Focus: Dockerize a simple agent application; build and run the Docker image locally.

L65: High-Throughput Serving Strategies

• Learning Objective: Understand high-throughput serving, managing latency, and scalability for agent inference in production environments.

• Hands-On Coding Focus: Deploy a FastAPI application for agent inference; use locust for basic load testing.

L66: Continuous Training (CT) & Adaptive Agents

• Learning Objective: Automate retraining pipelines to ensure agents adapt rapidly to changing market data, regulations, and user behavior.

• Hands-On Coding Focus: Implement a script that simulates data drift detection and triggers a model retraining workflow.

L67: Data Versioning & Feature Stores for Agents

• Learning Objective: Manage data versions (DVC) and utilize feature stores for consistent and reproducible training and inference.

• Hands-On Coding Focus: Use DVC to version data used for agent fine-tuning or RAG knowledge bases.

L68: Continuous Monitoring (CM) & Drift Detection

• Learning Objective: Implement real-time monitoring to detect concept drift, data drift, and agent performance degradation; configure automated response triggers.

• Hands-On Coding Focus: Integrate evidently or a similar library to monitor RAG context relevance over time.

L69: Agent Observability & Logging

• Learning Objective: Integrate dedicated observability tools (e.g., Langfuse, OpenTelemetry) to track agent execution paths, measure latency, and monitor operational costs.

• Hands-On Coding Focus: Instrument agent code with Langfuse calls to trace agent steps and observe internal states.

L70: Alerting & Incident Response

• Learning Objective: Configure alerting systems for critical agent failures, performance degradation, or security incidents; establish incident response protocols.

• Hands-On Coding Focus: Set up a simple alert (e.g., slack_sdk notification) when an MLOps metric threshold is breached.

L71: Runtime Guardrails & Security

• Learning Objective: Implement third-party guardrails (e.g., Guardrails AI) and watchdog mechanisms to monitor and enforce policy adherence at the execution level.

• Hands-On Coding Focus: Integrate Guardrails AI to validate LLM outputs (e.g., for PII, unwanted topics) at runtime.

L72: Cost Optimization Strategies in MLOps

• Learning Objective: Design deployment routing for a Hybrid Compute Strategy, balancing large GFM costs with efficient SLM execution for specific tasks.

• Hands-On Coding Focus: Implement a routing logic in Python: send simple queries to a local SLM, complex ones to a cloud GFM.

L73: A/B Testing & Canary Deployments for Agents

• Learning Objective: Implement A/B testing and canary deployments to safely roll out new agent versions and evaluate their impact.

• Hands-On Coding Focus: Outline the logic for traffic splitting and metric collection for A/B testing agent versions.

L74: Hands-On: MLOps Pipeline Deployment

• Learning Objective: Practical deployment of a minimal MLOps pipeline on a cloud service (e.g., Google Vertex AI, Azure ML), demonstrating automated drift response.

• Hands-On Coding Focus: Deploy a Dockerized agent to a cloud platform; configure basic monitoring and alerts.

L75: Project 5: MLOps Pipeline Automation

• Learning Objective: Develop a containerized and monitored MLOps pipeline demonstrating automated continuous integration and continuous training readiness for an agent.

• Hands-On Coding Focus: Provide a Dockerfile, CI/CD YAML, and monitoring configuration for a previously built agent.

Module 6: Specialized Vertical Case Studies and Capstone Projects (15 Lessons)

L76: Vertical Adaptation Strategies: Data Sourcing

• Learning Objective: Master proprietary data sourcing, preparation, and cleaning techniques for deep domain specialization.

• Hands-On Coding Focus: Develop a script to ingest and preprocess unstructured domain-specific data (e.g., financial reports, medical journals).

L77: Vertical Adaptation Strategies: Fine-Tuning LLMs

• Learning Objective: Understand and apply fine-tuning techniques (LoRA, QLoRA) for adapting open-source LLMs to specific vertical tasks.

• Hands-On Coding Focus: Use Hugging Face PEFT library to fine-tune a small LLM (e.g., Llama 3 8B) on a custom dataset.

L78: Vertical Adaptation Strategies: RAG Optimization

• Learning Objective: Optimize RAG implementations with specialized chunking, retrieval, and validation strategies for high-stakes vertical data.

• Hands-On Coding Focus: Adapt the Agentic RAG pipeline (from L43) with domain-specific chunking and validation rules.

L79: Governance & Compliance by Design

• Learning Objective: Implement “Security by Design” and “Privacy by Design,” focusing on GDPR, HIPAA, and financial regulatory mandates.

• Hands-On Coding Focus: Implement data anonymization/pseudonymization techniques; integrate access control for sensitive data within an agent.

L80: XAI & Traceability for VAIAs

• Learning Objective: Design audit logs that explicitly capture Validator Agent output and Synthesizer CoT, fulfilling the Right to Explanation.

• Hands-On Coding Focus: Enhance the logging (L73) to include detailed Thought and Decision traces from the Agentic RAG Validator.

L81: Bias Mitigation & Ethical AI

• Learning Objective: Implement strategies for continuous bias detection, ethical guidelines, and monitoring for systemic fairness in vertical agents.

• Hands-On Coding Focus: Use fairness metrics (e.g., from aif360) to evaluate agent outputs for potential biases.

L82: Risk Management: Reward Alignment

• Learning Objective: Design robust reward systems to counter optimization failure (where agents exploit system weaknesses to achieve unintended outcomes).

• Hands-On Coding Focus: Outline how a reward function for an agent might be designed to prevent undesirable behaviors.

L83: Risk Management: Auditing & Red Teaming

• Learning Objective: Establish ongoing governance frameworks that review reward alignment and audit trails; conduct red-teaming exercises for agents.

• Hands-On Coding Focus: Conduct a guided red-teaming exercise against a deployed agent to discover vulnerabilities.

L84: Vertical Case Study: Financial Agents

• Learning Objective: Design and build traceable agents for AML compliance, risk assessment, fraud detection, and maintaining required audit trails.

• Hands-On Coding Focus: Implement a simplified “AML transaction monitoring” agent using Agentic RAG and a FraudDetector tool.

L85: Vertical Case Study: Healthcare Agents

• Learning Objective: Architect agents for administrative tasks (coding, scheduling) and clinical summaries, emphasizing data privacy (HIPAA) and firewalled deployment.

• Hands-On Coding Focus: Develop a prototype “medical coding assistant” agent, focusing on secure data handling and privacy.

L86: Vertical Case Study: Legal Agents

• Learning Objective: Develop highly accurate agents for contract analysis and regulatory research, ensuring alignment with professional standards.

• Hands-On Coding Focus: Build a “contract clause extractor” agent that uses Agentic RAG to identify and validate specific legal terms.

L87: Team Collaboration & Version Control

• Learning Objective: Master advanced Git/GitHub workflows for collaborative agent development, including branching strategies and pull request reviews.

• Hands-On Coding Focus: Participate in a simulated collaborative project using advanced Git features (forks, PRs, code reviews).

L88: Deployment to Production Environments

• Learning Objective: Understand the final steps for deploying VAIAs to production, including security hardening, scaling, and operational handover.

• Hands-On Coding Focus: Finalize Docker images and deployment configurations for a full production deployment.

L89: Presenting & Defending Agent Architectures

• Learning Objective: Develop strong communication skills for presenting complex agent architectures to both technical and non-technical stakeholders.

• Hands-On Coding Focus: Prepare and deliver a mock presentation of a VAIA architecture.

L90: Capstone Project: VAIA Scoping

• Learning Objective: Define the scope, requirements, and architectural design of the final Capstone Project.

• Hands-On Coding Focus: Detailed Capstone Project proposal, including architecture diagram and technical plan.

L91-L93: Capstone Project: VAIA Development

• Learning Objective: Intensive hands-on development of the Capstone Project, integrating all learned MLOps, compliance, and architectural components.

• Hands-On Coding Focus: Build out the full Capstone Project, iterative coding with mentorship.

L94-L95: Capstone Project: VAIA Submission & Defense

• Learning Objective: Final submission and presentation of a production-grade Vertical AI Agent Based System demonstrating enterprise readiness and full auditability.

• Hands-On Coding Focus: Functional VAIA system, detailed documentation, and live defense/demo.