Master modern GenAI, LLMs, MCP, Agent2Agent (A2A), ACP, LangGraph, and multi-agent orchestration in a 12-week, 96+ hour instructor-led program.
DigiDARA's 12-week (96+ instructor-led hours) program provides practical training in Large Language Models (LLMs), prompt engineering, LangGraph, MCP, Agent2Agent (A2A), ACP protocol, RAG systems, vector databases, and AI application deployment with open-source tools.
Master the 2026 agent standards: MCP for tool access, A2A (Linux Foundation) for agent-to-agent coordination, and ACP for lightweight messaging.
Comprehensive week-by-week curriculum covering foundations to multi-agent deployment:
AI, Generative AI and Agentic AI; transformers, tokens, embeddings and context; multimodal and open-source models, model selection and responsible use. Build: Multi-Model AI Playground.
Python essentials for AI applications, model APIs/SDKs, authentication, streaming, retries, rate limits, and model routing based on quality, cost and latency. Build: Multi-Provider AI Application.
Prompt anatomy, reusable templates, prompt chaining, zero-shot/few-shot/iterative refinement, long-context strategies, compression and token optimization. Build: Prompt & Context Engineering Toolkit.
Structured JSON output and schema validation, function calling and external tools, document/image/audio processing, Streamlit/Gradio interfaces. Build: Multimodal Document Intelligence Application.
Embedding models and semantic similarity, vector stores and production vector databases, document loading, cleaning, metadata and chunking strategies. Build: Semantic Document Search System.
Keyword, semantic and hybrid retrieval, query transformation, metadata filters, reranking, citation-supported, multimodal and agentic RAG. Build: Enterprise Knowledge RAG Assistant.
The agent loop (observe, plan, act, verify), tool design/selection/task decomposition, reasoning-and-action patterns, failure recovery and appropriate autonomy. Build: Tool-Using Business Assistant.
Deterministic workflows vs dynamic agents, session state and persistent memory, human-in-the-loop approvals, interruptions and durable execution. Build: Human-Supervised Stateful Agent.
OpenAI Agents SDK, Claude Agent SDK, LangGraph, CrewAI and Google ADK; supervisor/router/specialist-agent patterns, subagents, native multi-agent orchestration. Build: Multi-Agent Business Operations Team.
MCP clients, servers and tools; building/consuming custom MCP servers; A2A (Linux Foundation) for agent-to-agent coordination; ACP for lightweight messaging. Build: Custom MCP-Powered Agent.
Evaluation datasets, LLM-as-judge grading, RAG/agent-trajectory evaluation with RAGAS and DeepEval, tracing with Langfuse/LangSmith/Braintrust. Build: Agent Evaluation & Security Report.
FastAPI, interfaces and authentication, state and background tasks, Docker and cloud/VPS deployment, model routing/optimization/scaling. Build: Production-Ready Capstone Application.
OpenAI (GPT-5.6), Anthropic Claude (Sonnet 5 / Opus 4.8), Google Gemini (3.x), Hugging Face & Ollama
Python & FastAPI, Streamlit or Gradio, Git & GitHub, Pydantic & JSON Schema
LangChain & LlamaIndex, Chroma & FAISS, Qdrant/Weaviate/Pinecone, hybrid retrieval & reranking
OpenAI Agents SDK & Claude Agent SDK, LangGraph/CrewAI/Google ADK, Model Context Protocol (MCP), Agent2Agent (A2A) & ACP
Design, build, and optimize autonomous agent workflows and multi-agent systems
Implement conversational and task-oriented agents using modern frameworks
Develop model interfaces, tools, and custom protocol bridges for AI systems
Manage agent monitoring, evaluation pipelines, testing, and guardrail enforcement
Design optimal prompts, context structures, and templates for reasoning models
Build full-stack AI-enabled web applications, RAG pipelines, and model API integrations
| Assessment Component | Weightage |
|---|---|
| Weekly Quizzes | 10% |
| Hands-on Labs | 25% |
| Three Mini-Projects | 20% |
| Evaluation & Security Assignment | 15% |
| Final Capstone | 25% |
| Presentation & Viva | 5% |
Basic understanding of Python programming and AI concepts.
Agentic AI Engineer, AI Agent Developer, MCP/Protocol Engineer, GenAI App Developer