Advanced Rag & Ai Agents 100 Production– Grade Labs

Architect production-grade RAG systems using hybrid search, reranking, GraphRAG, CRAG, Self-RAG, HyDE, and contextual retrieval.
Build asynchronous Python AI applications with Pydantic validation, streaming, retries, backpressure, state management, and resilient API clients.
Engineer autonomous AI agents using ReAct, state machines, memory systems, reflection, tool calling, and human-in-the-loop controls.
Design scalable multi-agent architectures including supervisor-worker systems, agent swarms, delegation, negotiation, shared state, and conflict resolution.
Integrate Model Context Protocol (MCP) with databases, filesystems, APIs, browsers, webhooks, and sandboxed execution environments.
Operate production AI systems with OpenTelemetry, Prometheus, Grafana, automated evaluations, regression testing, cost tracking, and retrieval-drift monitoring.
Harden RAG and agentic applications against prompt injection, data leakage, excessive tool permissions, PII exposure, and adversarial attacks.
Optimize AI infrastructure for latency, throughput, reliability, and cost using semantic caching, model routing, quantization, concurrency controls, circuit bre
Deploy enterprise AI platforms using Docker, Kubernetes, CI/CD, canary releases, disaster recovery, zero-trust networking, and air-gapped infrastructure.
Architect and defend the Lab 100 sovereign enterprise AI ecosystem—integrating RAG, GraphRAG, multi-agent orchestration, MCP, security, observability, complianc

Requirements

Recommended prerequisites
1. Basic Python programming knowledge
2. Familiarity with functions, classes, modules, and virtual environments
3. Basic command-line/Linux knowledge is helpful but not mandatory
4. Basic understanding of HTTP APIs and JSON is recommended
5. No previous LangGraph, Qdrant, MCP, Kubernetes, or RAG expertise is required
Recommended Software
1. Git
2. Python 3.12+
3. Docker / Docker Compose
4. Ollama for local open-weight LLM execution
5. Linux, macOS, or Windows with WSL2
Recommended Hardware
1. 16 GB RAM: Recommended minimum
2. 32 GB+ RAM: Strongly recommended for local LLM and multi-service workloads
3. Modern multi-core CPU
4. Approximately 50–100 GB free storage for development environments, containers, models, and datasets
5. NVIDIA GPU with 8 GB+ VRAM is beneficial for local model experimentation but is not mandatory

Description

This course contains the use of artificial intelligence.I only charge a fee solely for the time invested in building this comprehensive curriculum.Stop Vibe Coding. Start Engineering AI Systems.There is a huge difference between making an LLM answer a question and engineering an AI system that a real organization can trust.Vibe coding can produce a fascinating demo in an afternoon.Production AI requires something very different :1. Architecture.2. Validation.3. Observability.4. Security.5. Reliability.6. Evaluation.7. Cost control.8. Compliance.9. Deployment discipline.That is the gap this course is designed to close.This is not a collection of "copy this LangChain snippet" tutorials.It is a 100-lab engineering progression designed to take you from foundational AI application logic to a sophisticated autonomous, production-grade enterprise AI platform.You will build the system layer by layer.From Your First Local Model to Enterprise AI InfrastructureThe journey begins at the engineering foundation.You will configure an isolated asynchronous Python environment, run open-weight models locally, build validated API clients, implement retries and connection pooling, stream tokens, enforce structured outputs, manage conversation state, and establish your first local vector database.Then you will build your first complete semantic question-answering pipeline.That is your first major milestone.But it is only the beginning.Build RAG That Works Beyond the DemoThe next stage moves deep into modern retrieval engineering.You will process complex PDFs and HTML, implement semantic and token-aware chunking, extract hierarchical metadata, control concurrent embedding workloads, work with multimodal content, and combine sparse and dense retrieval.Then things become substantially more sophisticated.You will implement:Reciprocal Rank FusionCross-encoder rerankingIncremental knowledge synchronizationCorrective RAGSelf-RAGGraphRAGParent-child retrievalHyDEContextual retrievalSemantic deduplicationDistributed cachingRAG evaluationThe objective isn't simply to make retrieval work.It is to understand why retrieval succeeds, why it fails, and how to measure the difference.Engineer Autonomous AgentsOnce your retrieval foundation is solid, you move into agent engineering.You will build perception → reasoning → action → observation loops.You will implement ReAct architectures, typed tools, failure recovery, state machines, working memory, long-term episodic memory, reflection, self-correction, and human approval gates.Then you move from one agent to teams of agents.You will construct:Supervisor-worker architecturesPeer-to-peer communicationSpecialized agent swarmsConsensus mechanismsShared state systemsConflict resolutionDynamic delegationSub-agent spawningMulti-agent negotiationToken and latency optimizationThis is where the curriculum transitions from AI application development to AI systems engineering.Master MCP and Tool-Enabled AIModern agents become dramatically more useful when they can interact with the world.You will work with Model Context Protocol (MCP) and build standardized connections to filesystems, databases, APIs, browsers, webhooks, and sandboxed execution environments.You will also address the engineering realities that demos often ignore:Authentication.Rate limiting.Network isolation.Permission boundaries.Safe execution.Failure handling.The goal is to build agents that can use tools without turning your infrastructure into an uncontrolled security boundary.Make AI ObservableA production AI system cannot be operated by guessing what happened.You will implement distributed tracing with OpenTelemetry and capture:Token usageLatencyCostRetrieval behaviorAgent execution pathsFailuresRegression patternsAudit eventsYou will build monitoring dashboards with Prometheus and Grafana and create evaluation harnesses using golden datasets.You will also measure retrieval drift and automate regression testing for changing agent behavior.In other words:You will learn to observe AI systems instead of merely watching their outputs.Secure the SystemThen comes one of the most important sections of the entire course:AI security engineering.You will actively defend systems against direct and indirect prompt injection.You will implement PII masking, data-loss prevention, semantic filtering, least-privilege tool access, confidential-data redaction, vector database access controls, moderation pipelines, cryptographic audit trails, and adversarial red-team simulations.Security is not presented as a final checklist.It is engineered into the architecture.Scale Without Losing ControlA prototype can tolerate inefficiency.A production platform cannot.You will learn to route requests intelligently across model providers, implement semantic caching, tune vector indexes, manage high-concurrency asynchronous workloads, use backpressure and circuit breakers, compress context, conduct load tests, and design multi-region failover.You will also explore task-specific open-weight model fine-tuning as a strategy for controlling inference costs.The objective is simple:More throughput. Less latency. Lower cost. Greater resilience.Deploy a Sovereign AI PlatformThe final modules take everything you have built into production infrastructure.You will package your systems with Docker, deploy AI microservices on Kubernetes, configure ingress and TLS, establish zero-trust communication policies, automate CI/CD, perform canary model deployments, and design disaster-recovery procedures.Then the curriculum moves into a much more demanding environment:Data sovereignty.You will explore air-gapped environments and architectures designed for organizations that cannot simply send sensitive enterprise information to external AI providers.
1. The Aspiring AI Engineer,You know Python and want to move beyond calling an LLM API.,2. The Business Automator / AI Builder,You want AI agents that can actually retrieve enterprise knowledge, call tools, interact with APIs, execute workflows, maintain state, and operate under controlled permissions.,3. The Senior Developer Seeking AI Sovereignty,You already understand software engineering and want to master the architecture required for private, observable, secure, scalable AI infrastructure.

Homepage

https://www.udemy.com/course/advanced-rag-ai-agents-100-production-grade-labs/

Rapidgator
blstm.Advanced.Rag..Ai.Agents.100.ProductionGrade.Labs.part1.rar.html
blstm.Advanced.Rag..Ai.Agents.100.ProductionGrade.Labs.part2.rar.html
AlfaFile
blstm.Advanced.Rag..Ai.Agents.100.ProductionGrade.Labs.part1.rar
blstm.Advanced.Rag..Ai.Agents.100.ProductionGrade.Labs.part2.rar
DDownload
blstm.Advanced.Rag..Ai.Agents.100.ProductionGrade.Labs.part1.rar
blstm.Advanced.Rag..Ai.Agents.100.ProductionGrade.Labs.part2.rar
FreeDL
blstm.Advanced.Rag..Ai.Agents.100.ProductionGrade.Labs.part1.rar.html
blstm.Advanced.Rag..Ai.Agents.100.ProductionGrade.Labs.part2.rar.html