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ai-agent-architecture
by Drobiazkin
Engineering guide to building, serving, and understanding LLM systems
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Updated Aug 9, 2026
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Overview
Explains how to design and operate LLM systems end-to-end, from tokenization through model serving and self-hosting. Presents mechanisms, trade-offs, and quantitative guidance rather than prompt recipes, using examples and engineering reasoning. Emphasizes practical architecture patterns, performance numbers, and deployment considerations for real-world LLM stacks.
Why It Matters
As multi-agent and autonomous systems scale, engineers need to understand the mechanics behind model behavior and failure modes to assess agent reliability. That grounding makes it easier to build reproducible evaluation pipelines and reason about agent track records instead of relying on ad-hoc prompts. This book gives practitioners the missing engineering context — latency, tokenization, model choices, and serving trade-offs — that underpins trustworthy agent design and reproducible evaluation pipelines.
Ideal For
Engineers and teams building self-hosted or production LLM stacks who need a principled, systems-level understanding of trade-offs and failure modes.
Applications
- When you need to choose model and serving architectures to meet latency and cost targets
- When you want to understand tokenization and context effects that impact agent behavior
- When designing reproducible evaluation pipelines or pre-production testing for agents
- When planning self-hosted deployments and trade-offs between performance, privacy, and cost
Works With
transformershuggingfaceopenai
Topics
a2a-protocolaiai-agentscontext-engineeringdistributed-systemsharness-engineeringllmloop-engineeringmachine-learningmcp+5 more
Similar Tools
huggingface courseopenai cookbook
Keywords
agent reliabilityagent failure modesmulti-agent orchestrationllm engineeringself-hosted models