Agentic AI System DesignReleased 7/2026
By Keerti Purswani
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning |
Language: English |
Duration: 11 Lessons ( 27h 33m ) |
Size: 4.5 GB
Design production-grade AI systems with RAG, agents, memory, evaluation, and LLMOpsA comprehensive breakdown of the course content, designed to take you from fundamentals to advanced mastery.
1
Foundation + Knowledge Systems
Retrieval fundamentals - embeddings, chunking, vector search
Lexical, semantic, and hybrid retrieval
Vector databases, ANN, FAISS, HNSW
Production RAG architecture
Advanced RAG patterns
Metadata filtering and access control
Freshness, incremental indexing, multi-tenant retrieval
RAG debugging and optimization
2
Context Engineering
Context architecture and context assembly
Short-term and long-term memory
Semantic and user preference memory
Token budgeting and context optimization
Context compression and summarization
Selective context loading
Managing stale or conflicting context
3
Agent Systems & Harness Engineering
Agent architectures and execution patterns
Harness engineering - skills and execution environments
Tool calling and function orchestration
MCP and tool interoperability
Tool reliability and fallback handling
Multi-agent architectures
Long-running agentic workflows
Human approvals and state persistence
4
Production AI Engineering / LLMOps
Observability, tracing, and debugging
Evaluation frameworks and testing
Automated eval pipelines
LLMOps and release workflows
Cost optimization and token economics
Prompt caching and semantic caching
Model routing and model ensembles
Reliability, security, and guardrails
5
End-to-End System Design Capstones
Design AI coding agent
Implement AI coding agent
Production architecture discussions across real-world AI applications
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