![agentic-ai-cert-hero.webp](agentic-ai-cert-hero.webp)--DIVIDER--Welcome to the **Agentic AI Developer Certification Program** by Ready Tensor! This is a 12-week, hands-on learning journey where you'll design, build, and deploy intelligent, goal-driven AI systems. This page provides all essential information about the program, including: - Program Overview - How to Enroll - How the Program Works - Program Schedule - Certification Process - Project Details - Team-Based Learning - What's Not Covered--DIVIDER-- # What You'll Learn This program is structured into three comprehensive modules, each culminating in a practical, portfolio-worthy project: - **Module 1 (Weeks 1–4): Foundations of Agentic AI** Explore core concepts including agent architectures, retrieval-augmented generation (RAG), and tool use. You'll build your first project—a question-answering assistant. - **Module 2 (Weeks 5–8): Architecting Agentic Workflows** Learn to implement complex workflows, multi-agent collaboration, and Model Context Protocol (MCP)-aligned systems. Your second project involves creating a sophisticated multi-agent system. - **Module 3 (Weeks 9–12): Real-World Readiness** Master guardrails, evaluation strategies, logging, documentation, and deployment using FastAPI or Streamlit. In your final project, you'll deliver a production-quality agentic AI system. --DIVIDER-- # How to Enroll Follow these steps to enroll in the program: 1. **Register for a free account** on Ready Tensor (if you don't have one already). [Sign-up here](https://app.readytensor.ai/signup). 2. **Enroll in the program**. Navigate to the [`Ready Tensor Certifications` hub](https://app.readytensor.ai/hubs/ready_tensor_certifications). 3. Near the top of the page, click the **"Request to Join"** button to request access. Note the button will not be visible unless you are logged into the platform (see Step 1 above). The program is free to join, and the Program Manager will approve your request within 1-2 business days. 4. Once approved, you are officially enrolled! You'll have immediate access to program materials, including weekly lectures, reading materials, and project guidelines. The program officially begins on May 19th, 2025. You can enroll up until June 6th, the due date for the first project.--DIVIDER--# Certification is Free — Expert Feedback is Optional The certification program is completely free. All participants who complete the requirements will receive an official **Agentic AI Developer Certificate**. However, if you wish to receive **expert feedback and guidance** on your projects, you can subscribe to the **Pro** or **Team** plan on Ready Tensor. These plans include structured project reviews, personalized feedback, and direct support from AI experts to help you refine your work and grow faster.--DIVIDER--# How the Program Works Each week follows a consistent and engaging format: - **Weekly Lectures**: A pre-recorded session (30–60 minutes) covering essential concepts. Lectures include Q\&A sessions based on participant questions from the previous week's content, hosted on Discord. - **Reading Materials**: Essential readings, publications, and templates provided weekly to deepen your understanding and guide your projects. - **Weekly Assignments**: Practical, task-driven assignments designed to build progressively toward each module's project. These are **not graded**, but serve as guided practice to prepare you for your project work. - **Community Engagement**: Use Discord for ongoing discussions, community Q\&A, and team collaboration. - **Project Submissions**: You’ll submit three main projects, one per module, individually or as part of a team (max 5 people).--DIVIDER--# Program Schedule The program schedule is as follows (also see attachment titled **Agentic_AI_Developer_Certification_Schedule.pdf**).

Module 1: Foundations of Agentic AI (Weeks 1–4)

Lays the groundwork for understanding and constructing agentic systems.

Week 1: Introduction to Agentic AI Systems

- What is Agentic AI? Definitions, terminology, and motivations - Comparison with traditional AI pipelines - Core elements: agents, tools, environments, memory, planning - Real-world use cases and emerging trends - Toolchain overview (LangChain, LangGraph, vector stores, etc.)

Week 2: Chain-of-Thought Workflows & Retrieval-Augmented Generation (RAG)

- Basic prompting and LLM function chaining - Reasoning frameworks: ReAct, CoT, Self-Ask - Introduction to RAG systems: why they matter, how they work - Vector databases and embedding models (FAISS, Chroma, etc.) - Simple LangChain pipelines with RAG-style retrieval

Week 3: Hands-On Exercises with RAG Systems

- Guided practical exercises with LangChain and vector DBs - Building more robust retrieval pipelines - Incorporating memory or intermediate steps in retrieval - Debugging and evaluation of RAG agents

Week 4: Project 1 - Build a RAG-Powered AI App

- **Project-focused week with no new video lectures or required readings** - Participants work on building a question-answering or document-assistant app - Chain design: Prompt + Retrieval + Response - Integration with a vector store and basic evaluation loop - Optional: Add memory, tool usage, or intermediate reasoning - Deliverable: A simple RAG-based agent system with working retrieval and output - _Note: Participants may begin project work earlier during Weeks 2–3 if desired_

Module 2: Architecting Agentic AI Systems (Weeks 4–8)

Focuses on building autonomous and collaborative agents using modular and extensible systems.

Week 5: Agent Architectures & Planning Techniques

- Agent execution models: tool-using agents, reactive vs. deliberative - Planning mechanisms: zero-shot, few-shot, and learned planning - Introducing MCP: Model Context Protocol - Tool abstractions, APIs, and self-reflection - Introduction to LangGraph and directed workflow graphs - Building your first agentic workflow in LangGraph

Week 6: Multi-Agent Systems & Collaboration

- Design patterns for multi-agent coordination - Communication protocols and messaging (e.g., broadcast, direct, shared memory) - Role assignment and inter-agent task delegation - Coordinated tool use and shared context - Use cases: decentralized planning, team-of-agents models - Best practices for evaluating multi-agent performance

Week 7: Advanced Agent Evaluation Techniques

- Evaluating agent autonomy and reasoning quality - Measuring collaboration effectiveness in multi-agent systems - Human-in-the-loop testing and intervention - Benchmarking against baselines and predefined goals - Dataset creation for agent evaluation

Week 8: Project 2 - Build a Multi-Agent System

- **Project-focused week with no new video lectures or required readings** - Participants design a system of modular, composable agents - Implement inter-agent communication and memory sharing - Apply LangGraph to orchestrate role-based agent workflows - Optionally incorporate persistence via memory layers or vector DBs - Deliverable: A functional, MCP-aligned multi-agent system capable of collaborative problem solving - _Note: Participants may begin project work earlier during Weeks 6–7 if desired_

Module 3: Preparing Agentic AI for Real-World Use (Weeks 9–12)

Equips participants with essential skills for building safe, evaluable, and deployable systems.

Week 9: Guardrails, Evaluation, and Safety

- Prompt protection and safety frameworks (Guardrails.ai, Rebuff, etc.) - Input/output validation and structured output constraints - Defining evaluation metrics: success, efficiency, alignment - Instrumentation and logging (LangSmith, OpenTelemetry basics) - Case studies: agent failure modes and mitigation

Week 10: Deployment & Scalability Considerations

- When and how to deploy agentic systems - Lightweight deployment: FastAPI + containers - Hosting options: Hugging Face Spaces, Render, Streamlit, Gradio - Vector DB hosting, rate-limits, and cost considerations - Monitoring basics: tracing, usage tracking, user feedback

Week 11: Advanced Deployment Case Studies & Troubleshooting

- Scaling agents in production settings - Troubleshooting common deployment issues - Advanced observability and performance profiling - Security, reliability, and failover considerations - Real-world case studies and deployment architectures

Week 12: Final Project - Production-Aware Agentic AI System

- **Project-focused week with no new video lectures or required readings** - Capstone project: Productionize your Week 8 multi-agent system - Add guardrails, logging, and simple deployment wrapper - Document limitations, assumptions, and intended use - Deliverable: A portfolio-ready, production-aware agentic AI application - _Note: Participants may begin final project work earlier during Weeks 10–11 if desired_ --DIVIDER--# Certification Process To earn your **Agentic AI Developer Certificate**, you must: - Complete all three hands-on projects by their due dates. Publish each completed project publicly on the Ready Tensor platform, including comprehensive documentation and a repository link. - Achieve at least a 70% score per project based on the evaluation criteria provided in the **AAIDC Project Evaluation Criteria.pdf** attachment (**to be uploaded soon**).--DIVIDER--# Project Details Each project is designed to be a portfolio piece, showcasing your skills and understanding of agentic AI systems. The projects are described in detail in the **Agentic_AI_Developer_Certification_Projects.pdf** attachment. --DIVIDER-- # Team-Based Learning We strongly encourage participants to complete projects in teams (3–4 members recommended). This mirrors real-world professional workflows and maximizes skill diversity: - **AI/ML Theory Expert**: Knowledge of embeddings, transformers, and applied AI concepts. - **Programming Expert**: Skilled in Python, clean coding, and version control. - **Documentation Expert**: Adept at creating polished documentation and visuals. - **UI Expert**: Experienced in building professional-quality apps using Streamlit or Gradio. Solo projects are permitted but strongly discouraged. Team formation and collaboration are facilitated via our Discord community.--DIVIDER--# What's Not Covered This certification focuses specifically on agentic system development with existing models and APIs. It does **not** include: - Model training or fine-tuning - Self-hosting of foundation models - Full-scale ML-Ops or CI/CD pipelines - Enterprise-level security frameworks - Advanced front-end development --DIVIDER--