Agentic

agent-sandbox enables easy management of isolated, stateful, singleton workloads, ideal for use cases like AI agent runtimes. - kubernetes-sigs/agent-sandbox

Steering hooks achieved a 100% accuracy pass rate across 600 evaluation runs, compared to 82.5% for simple prompt-based instructions and 80.8% for graph-based workflows.

Meet Fabro (https://t.co/9iJbhJbMiw), the dark software factory for small teams of expert engineers. Fabro gets you out of the REPL (read-eval-prompt-loop) by layering deterministic workflow graphs over agent

An overview of four durable agentic patterns, from deterministic workflows to autonomous multi-agent orchestration, and the trade-offs each architecture introduces.

Learn how Databricks Agent Bricks, Unity Catalog Functions, and Vega‑Lite let multi‑agent systems return governed, portable visualizations instead of just raw tables.

By Ryan Lopopolo, Member of the Technical Staff

A visual guide to the Tape mechanism: append-only facts, anchors, handoff, fork/merge — a unified model for context management.

So far I only have one: Inflicting unreviewed code on collaborators, aka dumping a thousand line PR without even making sure it works first https://t.co/rg6LVi9zkk

TLDR: Our coding agent went from Top 30 to Top 5 on Terminal Bench 2.0. We only changed the harness. Here’s our approach to harness engineering (teaser: self-verification & tracing help a lot). The Goal of Harness Engineering The goal of a harness is to mold the inherently spiky intelligence of a model for tasks we care about. Harness Engineering is about systems, you’re building tooling around the model to optimize goals like task performance, token efficiency, latency, etc. Design decisions

Skills work via progressive disclosure: specialized prompts are loaded only when needed based on the context. 🪶 You can easily develop and share skills across teams because they're super lightweight (a

Master AI engineering with 30 essential AI systems engineering patterns. From RAG and LLM gateways to multi-agent orchestration and flow engineering, discover the definitive guide to robust LLM design patterns that bridge the gap between traditional software architecture and modern AI development.

Discover and install skills for AI agents.