42-learn-foundry -- EVA Learning & Reference Repository

EVA Ecosystem Integration

Tool	Purpose	How to Use
37-data-model	Single source of truth for all project entities	GET http://localhost:8010/model/projects/42-learn-foundry
29-foundry	Agentic capabilities (search, RAG, eval, observability)	C:\eva-foundry\eva-foundation\29-foundry
48-eva-veritas	Trust score and coverage audit	MCP tool: audit_repo / get_trust_score
07-foundation-layer	Copilot instructions primer + governance templates	MCP tool: apply_primer / audit_project

Agent rule: Query the data model API before reading source files.

Invoke-RestMethod "http://localhost:8010/model/agent-guide"   # complete protocol
Invoke-RestMethod "http://localhost:8010/model/agent-summary" # all layer counts

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Purpose: Learning sandbox, external reference repos, and exploratory spikes. Patterns learned here are implemented in 29-foundry (production library).

Relation: 29-foundry is the production EVA Foundry Library. 42-learn-foundry is study and exploration only. Nothing in this folder is imported by production EVA code.

Created: 2026-02-21

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Contents

Directory	Contents	Source
cloned-repos/	9 external Microsoft reference repos	Moved from 29-foundry 2026-02-21
notebooks/	4 tutorial Jupyter notebooks (quickstart, RAG, evaluation)	Moved from 29-foundry 2026-02-21
spikes/	One-off exploratory scripts and notebooks	Naming: YYYYMMDD-<topic>.py or .ipynb

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cloned-repos ? Reference Repositories

Repo	Source	Key patterns for EVA
agent-framework/	microsoft/agent-framework	Core agent patterns, graph orchestration
Agent-Framework-Samples/	microsoft/Agent-Framework-Samples	Progressive tutorials (chapters 1-9)
azure-search-openai-demo/	Azure-Samples/azure-search-openai-demo	RAG + hybrid search patterns
contoso-chat/	Azure-Samples/contoso-chat	Retail RAG reference
contoso-creative-writer/	Azure-Samples/contoso-creative-writer	Multi-agent writer pattern
mcp/	microsoft/mcp	MCP protocol specs
mcp-for-beginners/	microsoft/mcp-for-beginners	MCP tutorial
multi-agent-accelerator/	Azure-Samples/multi-agent-accelerator	Multi-agent patterns
prompty/	microsoft/prompty	Prompty format and tooling
spec-to-agents/	microsoft/spec-to-agents	Spec-driven agent generation

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spikes/

Naming convention: YYYYMMDD-<topic>.py or YYYYMMDD-<topic>.ipynb

Each spike should include a comment header:

# Spike: <topic>
# Date: YYYY-MM-DD
# Informs: <29-foundry path or "none">
# Conclusion: <one line>