Context engineering

Backlog.md: Terminal Kanban Board for Managing Tasks with AI Agents — Alex Gavrilescu, Funstage

Backlog.md: Terminal Kanban Board for Managing Tasks with AI Agents — Alex Gavrilescu, Funstage

Alex Gavrilescu introduces Backlog.md, a Git-based project management tool designed to structure AI-driven development. By breaking down features into Markdown tasks and using a multi-step review process, it helps prevent AI agents from running out of context or deviating from requirements, enabling a more predictable and efficient workflow.

The Unbearable Lightness of Agent Optimization — Alberto Romero, Jointly

The Unbearable Lightness of Agent Optimization — Alberto Romero, Jointly

This talk introduces Meta-ACE, a learned meta-optimization framework that dynamically orchestrates multiple strategies (context evolution, adaptive compute, hierarchical verification, and more) to maximize AI agent performance. The framework profiles each task to select an optimal strategy bundle, overcoming the single-dimension limitations of previous methods.

Context Engineering & Agentic Search with the CEO of Chroma

Context Engineering & Agentic Search with the CEO of Chroma

Jeff Huber, CEO of Chroma, discusses "context rot," the degradation of AI performance in large context windows, and outlines a new vision for retrieval infrastructure. He covers the evolution of search, the importance of a two-stage recall-then-precision pipeline, and the challenges of agentic memory, advocating for a shift from AI "alchemy" to reliable engineering.

The End of Ad-Hoc BI Dashboards

The End of Ad-Hoc BI Dashboards

Nick Schrock, CTO of Dagster, introduces Compass, a Slack-native tool for collaborative, exploratory data analysis, and discusses the rising importance of 'context engineering' as the new data pipeline in the AI era.

Overcoming Agentic Memory Management Challenges

Overcoming Agentic Memory Management Challenges

Biswaroop Bhattacharjee from Prem AI discusses Cortex, a novel AI memory system inspired by human cognition. The conversation explores moving beyond traditional flat memory structures to hierarchical, context-aware systems that enable more sophisticated and less noisy retrieval for AI agents.

Beyond Prompting: The Emerging Discipline of Context Engineering Reading Group

Beyond Prompting: The Emerging Discipline of Context Engineering Reading Group

This summary covers a deep dive into the paper "A Survey of Context Engineering for Large Language Models". The discussion reframes the conversation from simple prompt engineering to a more systematic approach of building information environments for LLMs. It explores the foundational components of context engineering—generation, processing, and management—and their application in advanced systems like Retrieval-Augmented Generation (RAG), memory, tool use, and multi-agent systems.