A 7×6 framework for agent architecture. 28 patterns, each placed at a coordinate, each with runnable code and a verified engineering slice from real production codebases.

The model spends. The harness budgets. This repo is the vocabulary you can put in your project tomorrow.

简体中文 README · Manning · Designing AI Agents · Paper · arXiv:2605.13850 · 极客时间专栏 · Newsletter · Author site

Looking for the full Argus running example as one evolving codebase, organized by book chapter? See the companion repo huangjia2019/designing-ai-agents — Argus grows module by module from Ch2 to Ch10, with each chapter's patterns/ + argus/ side by side. That repo follows the book's narrative; this repo is the standalone pattern catalog.

Designing AI Agents — the design-pattern catalogue for production AI agents. (Manning)

The two-axis framework, the 27 named patterns, and the five pattern-selection laws are introduced in A Two-Dimensional Framework for AI Agent Design Patterns: Cognitive Function × Execution Topology (Huang & Zhou, arXiv:2605.13850). This repository is the runnable companion to the paper.

Most "agent architecture" guides give you a flat list — Reflection, ReAct, Multi-Agent, Tree of Thoughts, Reflexive Metacognitive, and so on. A flat list answers what patterns exist. It does not answer where my problem sits, and which pattern lives at that coordinate.

A loan-evaluation agent crashes not because Reflection is missing but because Perception-stage budget allocation dropped the disqualifying document. A multi-agent code reviewer drifts not because ReAct is wrong but because two Reflection critics contradict each other and there is no governance gate to resolve it. These are not different patterns — they are patterns sitting at specific coordinates in a structured design space. Without the coordinates you can't see them.

This repo gives you the coordinates.

Every agent pattern sits at the intersection of two orthogonal axes.

• Cognitive function — what the agent is doing ↳ perceive · remember · reason · act · reflect · collaborate · govern
• Execution topology — how the work is laid out at runtime ↳ single-step · sequential · parallel · loop · router · hierarchy

Seven × six = 42 cells. The 28 cells where interesting patterns live are the chapters of Designing AI Agents (Manning) and the lectures of the 极客时间 column.

The framework's claim is not that everything fits the matrix. The claim is that giving a pattern a coordinate forces an answer to "why is this pattern here and not somewhere else". A flat list lets you skip the question. A matrix does not.

Every pattern below lives at one coordinate. Click any pattern name to enter that folder's code and README. Cells marked ✅ have runnable code; cells marked 🟡 are scaffolded.

Composition (putting patterns together): Pattern Selection Card · Six-Step Methodology · Argus Full Case Study · Checklist Benchmark Case

The 14 empty cells mark either industry gaps no production harness has filled yet, or topology-function combinations whose patterns haven't crystallized.

Each pattern folder follows the same shape: pattern.py (the minimal honest reference, 50–250 lines), example.py (a real-scenario case that runs without API keys), test_pattern.py (the invariants the pattern must hold), and bilingual README.md / README.zh-CN.md.

Every pattern's README cites real production code. Citations are file-and-line in upstream open-source projects, verified at the time of writing. If you find a citation that no longer matches the upstream code, open an issue — that's a bug, not a documentation choice.

The eight production harnesses the framework tracks: Claude Code, Codex CLI, Aider, OpenCode, OpenClaw, Hermes Agent, DeepAgents, DeerFlow, OpenHands. Each pattern's README pulls from at least one of these to show the pattern in real production form, not toy form.

• Not a framework. Use LangGraph, agno, DeerFlow, or OpenHands for a production runtime. This repo is the design vocabulary you apply on top of any of them. Switching frameworks does not change the matrix.
• Not a flat catalog. A list answers what patterns exist. The matrix answers where a problem sits and which patterns are wrong for that position.
• Not toy code. Every pattern.py is small (50–250 lines) on purpose, but it is honest code with real invariants and tests. Each example.py runs on data shaped like production. Engineering slices in the READMEs cite verified upstream files.

git clone https://github.com/huangjia2019/agent-design-patterns.git
cd agent-design-patterns
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Run a pattern's case
python perception/a-context-triage/example.py
python perception/b-semantic-compaction/example.py

# Run all invariant tests
pytest

Each pattern folder is self-contained. No central framework, no plugin system to learn. Read the folder's README, look at pattern.py, run example.py, read the tests.

<pattern-folder>/
  README.md                # The why — what failure mode this pattern catches
  README.zh-CN.md          # 中文版
  pattern.py               # The minimal honest implementation
  example.py               # A runnable case on production-shaped data
  test_pattern.py          # The invariants the pattern must hold

Read the README first — it's the why and the upstream slice. Then read pattern.py to see the smallest amount of code that solves it. Run example.py to see it work on data with shape. Tests pin the invariants you must not break when adapting.

Three lines from the book:

• Designing an agent is solving a constrained allocation problem.
• A fixed token budget must be distributed across competing cognitive demands under non-deterministic execution paths.
• The model is the spender. The harness is the budgeter. The patterns are the strategies.

Every pattern in the matrix is a strategy for one of those three roles — how the harness budgets, how the patterns allocate, how the model is positioned to spend. The matrix is what makes the strategies discussable as a system rather than as a flat list.

The book gives you the theory. The column gives you the lectures. This repo gives you runnable code.

Jia Huang (黄佳) — Lead Research Engineer at A*STAR Singapore, formerly senior consultant at Accenture Singapore. Twenty years across NLP, LLMs, and AI applications in MedTech and FinTech. Author of two forthcoming English-language books (Designing AI Agents with Manning, RAG from First Principles with Packt) and six Chinese-language books on machine learning, GPT, AI agents, RAG, and data analysis with cumulative readers in the hundreds of thousands.

The two-axis framework is the author's original contribution; the constituent elements (seven cognitive functions, six execution topologies) are not new — the contribution is the orthogonal organization.

kage-ai.com · LinkedIn · Substack · tohuangjia@gmail.com

Issues welcome. Particularly useful:

• Citation drift — a verified engineering slice in a README points at a file or line that no longer matches upstream
• Invariant violations — a test misses a case that you've seen the pattern fail in production
• New language ports — TypeScript / Go ports of any pattern, opened as a separate top-level folder
• New engineering slices — a production harness you've worked with shows the pattern in a form not yet documented in the README

Pull requests for new patterns: please open an issue first to discuss where the pattern sits in the matrix.

If this framework is useful in your work, please cite the paper:

@misc{huang_zhou_2026_dual_axis,
  author        = {Huang, Jia and Zhou, Joey Tianyi},
  title         = {A Two-Dimensional Framework for AI Agent Design Patterns:
                   Cognitive Function and Execution Topology},
  year          = {2026},
  eprint        = {2605.13850},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  doi           = {10.5281/zenodo.19036557},
  url           = {https://arxiv.org/abs/2605.13850}
}

MIT. See LICENSE.