Mergeable by default: Building the context engine to save time and tokens — Peter Werry, Unblocked
Agents can generate code. The hard part is generating code that's right for your system, team conventions, and past decisions. That's a context problem that naive RAG, MCP servers, and bigger context windows don't solve. Without the right context, that code costs you twice: once in tokens, again in long review cycles. This session is a practitioner's guide to building a context engine: the reasoning layer that brings together your organizational context and delivers only what the agent needs for the task at hand. I'll walk through the challenges that matter: reasoning across conflicting sources, maintaining permissions, and personalizing results based on who's asking and what they're working on. Along the way, we'll go deep on specific components with live demos and technical breakdowns. Drawn from real lessons building this in production, including what we got wrong.

RL for Agents Workshop - Deep Dive on Training Agents with RL and Open Source

TLMs: Tiny LLMs and Agents on Edge Devices with LiteRT-LM — Cormac Brick, Google

MCP = Mega Context Problem - Matt Carey

Zig 2026: No-AI Policy, $670K Foundation, Left GitHub & Why Zig Isn’t 1.0 - Andrew Kelley Explains

Control Plane for Agent Memory -- Vas (cognee.ai)

Demand-Driven Context: A Methodology for Coherent Knowledge Bases Through Agent Failure

Full Walkthrough: Writing & Using Skills — Nick Nisi and Zack Proser

Everything We Got Wrong About Research-Plan-Implement - Dexter Horthy

Anthropic Workshop: Build Agents That Run for Hours — Ash Prabaker & Andrew Wilson

The Friction is Your Judgment — Armin Ronacher & Cristina Poncela Cubeiro, Earendil

How I deleted 95% of my agent skills and got better results — Nick Nisi, WorkOS

Agentic Engineering: Working With AI, Not Just Using It — Brendan O'Leary

Python Interview Questions and Answers | Top Python Interview Questions | Intellipaat

Agentic Search for Context Engineering — Leonie Monigatti, Elastic

Context Is the New Code — Patrick Debois, Tessl

Agent Optimization with Pydantic AI: GEPA, Evals, Feedback Loops — Samuel Colvin, Pydantic

The $1T SaaS Sell-Off: Why Agents Are Winning!

Andrej Karpathy: From Vibe Coding to Agentic Engineering w/ Stephanie Zhan

Build a Complete Medical Chatbot with LLMs, LangChain, Pinecone, Flask & AWS 🔥
![Microsoft Fabric and Power BI - Developer of the Future⚡ [Full Course]](https://i.ytimg.com/vi/ohKpl80obzU/hqdefault.jpg?sqp=-oaymwEjCNACELwBSFryq4qpAxUIARUAAAAAGAElAADIQj0AgKJDeAE=&rs=AOn4CLC7OUcS43Tjw7PcWR1n6T-ncrgsdA)
