Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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Agentic Systems Engineering
A principal-grade technical reference for building production agentic AI systems
<p> <a href="https://generalaimodels.github.io/Agentic-Systems-Engineering/"><strong>Read Online</strong></a> · <a href="https://generalaimodels.github.io/Agentic-Systems-Engineering/chapter1-theagentic-paradigm/"><strong>Start With Chapter 1</strong></a> · <a href="https://generalaimodels.github.io/Agentic-Systems-Engineering/future/"><strong>See Future Roadmap</strong></a> </p>
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A systems-first technical reference for agentic AI: architecture, protocols, context, retrieval, memory, orchestration, observability, hallucination control, and production reliability.
Agentic systems are not “prompt + tool” toys. They are control architectures with typed interfaces, bounded reasoning loops, retrieval engines, memory layers, verification paths, observability surfaces, and failure domains.
This repository is a long-form engineering book for that reality.
It is written for engineers who want to design agents as real systems:
- with explicit architecture, not vague orchestration
- with typed contracts, not hidden conventions
- with retrieval and memory as infrastructure, not marketing terms
- with observability, drift detection, and fault tolerance designed in from the start
Why This Repository Matters
Most public agent material focuses on demos, libraries, or short-form patterns. This project instead asks harder questions:
- What makes an agent different from a predictive model?
- How should JSON-RPC, gRPC, Protobuf, and MCP fit into one system?
- How do you compile context under strict token budgets without losing provenance?
- What makes retrieval deterministic and auditable?
- How should memory be layered, validated, and governed?
- How do you keep multi-agent systems observable, bounded, and repairable in production?
This repository is designed to answer those questions end to end.
At a Glance
| What this repo is | Why it is useful |
|---|---|
| A long-form engineering book on agentic systems | It connects agent behavior to real systems design decisions |
| A GitHub Pages knowledge base with chapterized navigation | You can read it online like a technical handbook |
| A production-oriented reference | It focuses on verification, reliability, observability, and failure handling |
| A systems architecture lens on agents | It treats context, retrieval, memory, and orchestration as infrastructure |
Visual Map
flowchart LR
A[Foundations] --> B[Typed Protocol Stack]
B --> C[Context Engineering]
C --> D[Retrieval Infrastructure]
D --> E[Memory Architecture]
E --> F[Tools and Agent Loops]
F --> G[Multi-Agent Orchestration]
G --> H[Observability and Reliability]
H --> I[Fault Tolerance and Production Operations]What You’ll Find Here
- 21 chapters covering the architecture of modern agentic systems
- formal models, typed interfaces, pseudocode, tables, diagrams, and equations
- production-grade treatment of context, retrieval, memory, tool use, orchestration, and resilience
- a coherent technical reference that connects agent reasoning to real software and infrastructure decisions
Signature Themes
- agentic behavior as closed-loop control rather than next-token fluency
- typed protocol layers across JSON-RPC, gRPC, Protobuf, and MCP
- context engineering as a compiler problem with budgets and invariants
- retrieval as a deterministic evidence pipeline with provenance
- memory as a layered, validated, governance-aware substrate
- production safety through monitoring, abstention, drift detection, and fault tolerance
Coverage by Part
| Part | Focus | Representative Topics |
|---|---|---|
| Part I | Foundations | agent definitions, autonomy levels, bounded rationality, control loops |
| Part II | Typed Protocol Stack | JSON-RPC, gRPC, Protobuf, MCP, gateways, auth propagation, contract testing |
| Part III | Context Engineering | prefill compilation, token economics, instruction hierarchy, query understanding |
| Part IV | Retrieval Infrastructure | hybrid retrieval, chunking, provenance, ranking, embeddings, indexing |
| Part V | Memory Architecture | layered memory, write policies, validation, governance, retrieval alignment |
| Part VI | Tools and Orchestration | tool architecture, agent loops, multi-agent coordination, session design |
| Part VII | Reliability | environment legibility, hallucination monitoring, resilience, fault tolerance |
Reading Tracks
If You’re Building Your First Serious Agent Platform
- Chapter 1: The Agentic Paradigm
- Chapter 4: Protocol Architecture
- Chapter 6: Context Engineering
- Chapter 8: Retrieval Architecture
- Chapter 13: Tool Architecture
- Chapter 15: Agent Loop
If You Care Most About Production Safety and Reliability
If You Care Most About Platform and Systems Design
- Chapter 5: SDK Architecture
- Chapter 10: Embedding
- Chapter 16: Multi-Agent Orchestration
- Chapter 18: Session Architecture
Chapter Entry Points
- Homepage
- Chapter 1: The Agentic Paradigm
- Chapter 2: LLM as Cognitive Substrate
- Chapter 3: Formal Agent Architectures
- Chapter 4: Protocol Architecture
- Chapter 5: SDK Architecture
- Chapter 6: Context Engineering
- Chapter 7: Query Understanding
- Chapter 8: Retrieval Architecture
- Chapter 9: Chunking
- Future Roadmap
Who This Is For
- engineers building internal agent platforms
- applied AI teams shipping tool-using or retrieval-heavy systems
- research engineers studying agent reliability and architecture
- architects designing typed, observable, production-safe AI systems
- advanced practitioners who want a coherent systems view instead of fragmented blog posts
Design Principles
- rigor over hype
- architecture over demos
- typed contracts over hidden glue
- provenance over retrieval theater
- verification over unchecked autonomy
- repairability over brittle “smart agent” narratives
Repository Structure
This repository is the published static site:
index.htmlfor the main landing pagechapter*/index.htmlfor chapter pagesfuture/index.htmlfor the roadmapassets/for shared CSS, JavaScript, KaTeX, and fonts.github/workflows/for GitHub Pages deployment automation
The content is exported from a source notes repository into this GitHub Pages repository for publication.
Current Status
This is an actively expanding reference. The existing chapters already cover the major system layers needed to build production agentic AI, while the future roadmap extends into deeper multi-agent, operational, and long-horizon autonomy work.
If you want to study agentic systems as engineering, not branding, this repository is built for you.