Skip to main content

Vinay Jayanna

Sizing LLM Inference for Production
Agentic Systems in Production

GitHub LinkedIn

Overview & Download
Part I · The Production Landscape
→ Why RAG and Agentic Systems Break at Scale
1 · LLM Gateway and Multi-Provider Routing
Part II · High-Throughput Retrieval
2 · Embedding Models and Retrieval Quality
3 · Vector Database Architecture, Scaling, and Real-Time Indexing
4 · Chunking, Context Construction, and Document Pipelines
5 · Context Engineering: Budget, Assembly, and Governance
6 · Hybrid Search, Query Routing, and Semantic Caching
Part III · RAG System Design
7 · RAG Architectures: Production Failure Modes and Design Patterns
8 · RAG Evaluation: Metrics That Survive Production
9 · RAG Cost, Latency, and Security
Part IV · Agentic System Design
10 · Agentic RAG: When Retrieval and Agency Interleave
11 · Agent Loop Design: State Machines, Re-planning, and Failure Isolation
12 · Tool Design, MCP, and the Agentic Protocol Layer
13 · Memory, State, and Context Across Agent Turns
14 · Multi-Agent Orchestration and Failure Isolation
15 · Latency Budgets, Cost Control, and Agentic Security
Part V · Reliability and Governance
16 · DAG-Based Observability for Non-Deterministic Systems
17 · Deterministic Testing and Agentic CI/CD
18 · Continuous Improvement: Feedback Loops and Online Learning
19 · Production Guardrails, Compliance, and Sizing

3 · Vector Database Architecture, Scaling, and Real-Time Indexing

Vector Database Architecture, Scaling, and Real-Time Indexing

This chapter is coming soon.

Last updated on Apr 30, 2026

2 · Embedding Models and Retrieval Quality

4 · Chunking, Context Construction, and Document Pipelines

Field Guides

Sizing LLM Inference for Production
Agentic Systems in Production

Writing

LinkedIn Articles
Substack

Connect

GitHub
LinkedIn

© 2026 Vinay Jayanna. All rights reserved.