Repository Analysis

microsoft/ai-agents-for-beginners

18 Lessons to Get Started Building AI Agents

12.8 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of microsoft/ai-agents-for-beginners, a Jupyter Notebook project with 70,904 GitHub stars. SynthScan v2.0 examined 813,598 lines of code across 3664 source files, recording 3117 pattern matches distributed across 16 syntactic categories. The overall adjusted score of 12.8 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

12.8
Adjusted Score
12.8
Raw Score
100%
Time Factor
2026-07-29
Last Push
70.9K
Stars
Jupyter Notebook
Language
813.6K
Lines of Code
3.7K
Files
3.1K
Pattern Hits
2026-08-02
Scan Date
0.26
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 960MEDIUM 1571LOW 586

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 3117 distinct pattern matches across 16 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Modern AI Meta-Vocabulary1549 hits · 4970 pts
SeverityFileLineSnippetContext
MEDIUMSTUDY_GUIDE.md137### RAG and KnowledgeCOMMENT
MEDIUM14-microsoft-agent-framework/README.md29[Microsoft Agent Framework (MAF)](https://aka.ms/ai-agents-beginners/agent-framework) is Microsoft's unified framework fCODE
MEDIUM…ramework/code-samples/hotel_booking_workflow_sample.py193 # MiniMax: OpenAI-compatible API with large context window (up to 204K tokens).COMMENT
MEDIUM05-agentic-rag/README.md5# Agentic RAGCOMMENT
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MEDIUM05-agentic-rag/README.md117### Got More Questions about Agentic RAG?COMMENT
MEDIUM05-agentic-rag/README.md125- <a href="https://weaviate.io/blog/what-is-agentic-rag" target="_blank">What is Agentic RAG | Weaviate</a>CODE
MEDIUM05-agentic-rag/README.md126- <a href="https://ragaboutit.com/agentic-rag-a-complete-guide-to-agent-based-retrieval-augmented-generation/" target="_CODE
MEDIUM…-agentic-rag/code_samples/05-dotnet-agent-framework.md1# 🔍 Enterprise RAG with Microsoft Foundry (.NET)COMMENT
MEDIUM…-agentic-rag/code_samples/05-dotnet-agent-framework.md13## 🎯 Enterprise RAG ArchitectureCOMMENT
MEDIUM…-agentic-rag/code_samples/05-dotnet-agent-framework.md29### Enterprise RAG PipelineCOMMENT
MEDIUM…-agentic-rag/code_samples/05-dotnet-agent-framework.md89## 📊 Enterprise RAG PatternsCOMMENT
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MEDIUM…rotocols/code_samples/github-mcp/event-descriptions.md180We will walk you through a multi-agent application in C# that is built on top of the Semantic Kernel framework. You willCODE
MEDIUM11-agentic-protocols/code_samples/github-mcp/app.py102# RAG tool for event searchCOMMENT
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MEDIUM09-metacognition/README.md247### Retrieval-Augmented Generation (RAG)COMMENT
MEDIUM09-metacognition/README.md253### Corrective RAG ApproachCOMMENT
MEDIUM09-metacognition/README.md261#### Example: Corrective RAG in a Search AgentCOMMENT
MEDIUM09-metacognition/README.md269### Corrective RAG in Travel AgentCOMMENT
MEDIUM09-metacognition/README.md279#### Steps for Implementing Corrective RAG in Travel AgentCOMMENT
MEDIUM09-metacognition/README.md638### RAG: Prompting Technique vs ToolCOMMENT
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MEDIUM09-metacognition/README.md661#### RAG as a ToolCOMMENT
MEDIUM09-metacognition/README.md1196### Using SQL as a Retrieval-Augmented Generation (RAG) TechniqueSTRING
MEDIUM04-tool-use/README.md243<a href="https://learn.microsoft.com/azure/ai-services/agents/overview" target="_blank">Microsoft Foundry Agent Service<CODE
MEDIUM13-agent-memory/README.md109#### Structured RAG (Retrieval Augmented Generation)COMMENT
MEDIUM13-agent-memory/README.md137### Storing Memory with RAGCOMMENT
MEDIUM00-course-setup/README.md230## Additional Setup for Lesson 5 (Agentic RAG)COMMENT
MEDIUM08-multi-agent/README.md191- <a href="https://www.analyticsvidhya.com/blog/2024/10/agentic-design-patterns/" target="_blank">Agentic design patternCODE
MEDIUMtranslations/sl/STUDY_GUIDE.md137### RAG in znanjeCOMMENT
MEDIUMtranslations/sl/05-agentic-rag/README.md5# Agentic RAGCOMMENT
MEDIUMtranslations/sl/05-agentic-rag/README.md31## Kaj je Agentic RAG?COMMENT
MEDIUMtranslations/sl/05-agentic-rag/README.md37## Določanje Agentic Retrieval-Augmented Generation (Agentic RAG)COMMENT
MEDIUMtranslations/sl/05-agentic-rag/README.md117### Imate več vprašanj o Agentic RAG?COMMENT
MEDIUMtranslations/sl/05-agentic-rag/README.md125- <a href="https://weaviate.io/blog/what-is-agentic-rag" target="_blank">Kaj je Agentic RAG | Weaviate</a>CODE
MEDIUMtranslations/sl/05-agentic-rag/README.md126- <a href="https://ragaboutit.com/agentic-rag-a-complete-guide-to-agent-based-retrieval-augmented-generation/" target="_CODE
MEDIUM…-agentic-rag/code_samples/05-dotnet-agent-framework.md1# 🔍 Enterprise RAG z Microsoft Foundry (.NET)COMMENT
MEDIUM…-agentic-rag/code_samples/05-dotnet-agent-framework.md13## 🎯 Arhitektura Enterprise RAGCOMMENT
MEDIUM…-agentic-rag/code_samples/05-dotnet-agent-framework.md29### Pipeline za Enterprise RAGCOMMENT
MEDIUM…-agentic-rag/code_samples/05-dotnet-agent-framework.md89## 📊 Vzorce Enterprise RAGCOMMENT
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MEDIUMtranslations/sl/09-metacognition/README.md247### Retrieval-Augmented Generation (RAG)COMMENT
MEDIUMtranslations/sl/09-metacognition/README.md253### Korektivni pristop RAGCOMMENT
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MEDIUMtranslations/sl/09-metacognition/README.md269### Korektivni RAG v Potovalnem agentuCOMMENT
MEDIUMtranslations/sl/09-metacognition/README.md279#### Koraki za implementacijo korektivnega RAG v Potovalnem agentuCOMMENT
MEDIUMtranslations/sl/09-metacognition/README.md639### RAG: tehnika pozivanja vs. orodjeCOMMENT
MEDIUMtranslations/sl/09-metacognition/README.md643#### RAG kot tehnika pozivanjaCOMMENT
MEDIUMtranslations/sl/09-metacognition/README.md662#### RAG kot orodjeCOMMENT
MEDIUMtranslations/sl/09-metacognition/README.md1198### Uporaba SQL kot tehnike Retrieval-Augmented Generation (RAG)STRING
MEDIUMtranslations/sl/13-agent-memory/README.md109#### Strukturirani RAG (Retrieval Augmented Generation)COMMENT
MEDIUMtranslations/sl/13-agent-memory/README.md137### Shranjevanje pomnilnika z RAGCOMMENT
MEDIUMtranslations/sl/00-course-setup/README.md230## Dodatna nastaviti za Lekcijo 5 (Agentic RAG)COMMENT
MEDIUMtranslations/sl/08-multi-agent/README.md192- <a href="https://www.analyticsvidhya.com/blog/2024/10/agentic-design-patterns/" target="_blank">Agentni vzorci načrtovCODE
MEDIUM…-multi-agent/code_samples/08-dotnet-agent-framework.md36Celotna delujoča koda za to lekcijo je na voljo v pripadajoči C# datoteki: [`08-dotnet-agent-framework.cs`](../../../../CODE
MEDIUMtranslations/sk/STUDY_GUIDE.md137### RAG a vedomostiCOMMENT
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Cross-File Repetition951 hits · 4755 pts
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Hyper-Verbose Identifiers343 hits · 274 pts
SeverityFileLineSnippetContext
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Excessive Try-Catch Wrapping156 hits · 158 pts
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Structural Annotation Overuse66 hits · 101 pts
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Decorative Section Separators14 hits · 48 pts
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MEDIUM…ramework/code-samples/hotel_booking_workflow_sample.py47# ============================================================================COMMENT
MEDIUM…ramework/code-samples/hotel_booking_workflow_sample.py49# ============================================================================COMMENT
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Magic Placeholder Names7 hits · 30 pts
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Self-Referential Comments3 hits · 10 pts
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AI Response Leakage2 hits · 10 pts
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HIGH07-planning-design/README.md267Magentic One - A Generalist multi-agent system for solving complex tasks and has achieved impressive results on multipleCODE
HIGHtranslations/en/07-planning-design/README.md267Magentic One - A Generalist multi-agent system for solving complex tasks and has achieved impressive results on multipleCODE
Deep Nesting9 hits · 9 pts
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Modern Structural Boilerplate5 hits · 5 pts
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Unused Imports5 hits · 5 pts
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Over-Commented Block3 hits · 3 pts
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LOWrequirements.txt1# Azure AI & IdentityCOMMENT
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Fake / Example Data2 hits · 2 pts
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Redundant / Tautological Comments1 hit · 2 pts
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AI Structural Patterns1 hit · 1 pts
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