donbr

🚀 Executive Summary

A Spec-Driven Operating Model for Human + AI Engineering Teams Using GitHub

AI Engineering in 2025 requires more than prompting or code generation—it requires a repeatable, spec-driven system that aligns humans and AI agents on what to build and why before any code is written.

GitHub’s Spec Kit provides a lightweight, practical foundation for this: a standardized workflow that uses structured specifications to guide AI agents, reduce rework, and eliminate “vibe coding.”

This bootcamp framework extends that foundation into a three-phase operating model, helping future AI architecture & engineering leaders create teams where humans and AI work together effectively, predictably, and safely across a GitHub Organization.

donbr

Here’s a concise analysis of the markdown‑confluence GitHub organisation (and its tooling), why it appears to have waned in activity, and alternative tools/approaches you might evaluate.

---

1. What the project was

• The mono-repo at [markdown-confluence/markdown-confluence] described itself as “a collection of tools to convert and publish your Markdown files to Confluence (using Atlassian Document Format – ADF)”. ([GitHub][1])

• It included components like:

• an npm CLI (@markdown-confluence/lib) for converting Markdown → ADF. ([GitHub][1])

donbr

🎨 Breakout Room Visual Guide

Frontend Development & Deployment Workflow

---

📋 Complete Workflow Overview

graph TB
    Start([👋 Start: Breakout Room Session]) --> Phase1[💡 Phase 1: Planning & Design]

donbr

Santosh - Assignment 15 - A2A

🧭 TL;DR Feedback – Simplifying & Aligning with A2A Client Patterns

Great job — your code works and shows deep understanding of streaming, chunk parsing, and A2A context handling. To simplify and align with the official A2A client patterns (like test_client.py), focus on these refinements:

---

🔹 1. Separate Concerns

donbr

The LangChain Provider Switching Pattern

How to Switch Between LLM Providers in 20 Lines of Code

---

🎯 The Core Pattern

The essential truth about switching LLM providers in LangChain:

donbr

2025 agentic papers

1. Agentic Retrieval-Augmented Generation: A Survey — arXiv, Jan 2025 Why it matters: formalizes “agentic RAG” patterns (reflection, planning, tool use, multi-agent) and maps implementation choices you already teach. Great for framing why orchestration beats “just a better model.” ([summarizepaper.com][1])

2. Reasoning↔RAG Synergy (Survey): Toward Deeper RAG-Reasoning Systems — arXiv, Jul 2025 Why it matters: unifies “reasoning-enhanced RAG” and “RAG-enhanced reasoning,” then spotlights agentic interleaving (search ↔ think loops). Solid taxonomy + dataset links you can fold into eval curricula. ([summarizepaper.com][2])

3. LLM-based Agents in Medicine (Survey) — ACL Findings 2025 Why it matters: a rigorous vertical survey (healthcare) with evaluation tables, safety constraints, and workflow patterns (routing, oversight, audit). Use it as a model for domain-specific agent governance sections in your posts. ([ACL Anthology][3])

donbr

donbr

How to discover A2A agents (code)

1) Direct discovery via “well-known” AgentCard

Most A2A servers expose a public card at a well-known path. (Spec recommends a well-known URL and describes card contents/capabilities.) ([a2a-protocol.org][1])

import httpx, asyncio

WELL_KNOWN = "/.well-known/agent-card.json"  # (spec names vary slightly by version)

donbr

Open Deep Research Observability prompt

Role: You are a senior AI systems observability engineer specializing in multi-agent pipelines and trace analytics. Your task is to help us define what visibility truly means in our LangGraph “Open Deep Research” project, and what we must monitor to make it reliable and explainable at scale.

---

Context:

• We run long-form, multi-agent research graphs composed of supervisor, researcher, compression, and tool nodes.

donbr