OpenAI’s recent study shows that more than 10% of Java developers now manage three or more Codex agents at once, prompting a shift from single‑agent coding to a multithreaded, task‑splitting workflow that demands clear boundaries, isolated workspaces, and disciplined coordination.
The article explains how the Gate First principle in Routa separates task completion, evidence verification, and session boundaries, ensuring that each agent receives a structured, consumable state instead of just a final output, thereby reducing rework and preserving failure reasons for future iterations.
The Scale‑SWE project combines a massive 100k‑sample software engineering dataset with a high‑concurrency sandbox infrastructure and a multi‑agent workflow to dramatically improve code‑agent training, evaluation, and real‑world performance, surpassing existing models on SWE‑bench benchmarks.
The Scale‑SWE project releases a 100k‑task real software‑engineering dataset built with a sandboxed multi‑agent workflow, demonstrating that models fine‑tuned on this data achieve 64% on SWE‑bench‑Verified and surpass leading industrial baselines, highlighting the critical value of authentic SWE data.
The article reviews Anthropic's Claude Cowork launch, then introduces three notable open‑source replacements—OpenWork, Claude‑Cowork, and Eigent—detailing their features, multi‑agent workflows, and technology stacks, and provides repository links for further exploration.
This guide walks you through the fundamentals of Claude Code agents—explaining why multiple agents are useful, how to configure them with YAML templates, manage them via slash commands, install third‑party agents, and orchestrate real‑world multi‑agent workflows—all with practical screenshots and code examples.
