Multi-Agent on Bruce on AI Engineering

OpenClaw Multi-Agent Setup: Stop Letting LLMs Orchestrate

Thu, 02 Apr 2026 14:00:00 +0800

The biggest threat to your OpenClaw multi-agent system is not misconfiguration. It is letting LLMs handle orchestration.

The typical setup looks elegant on paper: a Supervisor agent receives all messages, uses sessions_send to delegate to specialist agents, collects results, and delivers a unified response. In practice, every routing decision is a full API call, every specialist reply-back is another, and a simple “write me a blog post” might trigger 6-8 API requests. Worse, without anti-recursion rules, agents can enter circular delegation loops. Cogent’s failure playbook documents teams that burned thousands of dollars in minutes from exactly this pattern.

GitHub Agent HQ: Multi-Agent Development Guide for VS Code

Fri, 13 Mar 2026 15:00:00 +0800

Running one AI coding agent is productive. Running three at the same time — comparing their approaches, playing to each one’s strengths, and picking the best output — changes how you build software entirely.

GitHub Agent HQ makes this possible without leaving VS Code. Announced at GitHub Universe 2025 and launched in public preview in February 2026, Agent HQ lets you run Anthropic Claude, OpenAI Codex, and GitHub Copilot from a single interface. Think of it as a mission control for AI agents: you assign tasks, monitor progress, compare results, and merge the best solution — all without switching tools.

OpenClaw Multi-Agent Setup: Build AI Teams That Work

Wed, 04 Mar 2026 19:00:00 +0800

You built an OpenClaw personal assistant. It handles your Telegram messages, searches the web, writes drafts, reviews code. Life is good.

Then reality hits. You ask it to research a competitor, and it responds with coding suggestions from yesterday’s conversation. You ask it to write an article, and it pulls in debugging context from a completely unrelated project. Your single agent is drowning in accumulated memory, and every response gets slower and more confused.

Claude Code Teams Guide 2026: Multi-Agent Setup & Collaboration

Fri, 27 Feb 2026 10:00:00 +0800

One agent is good. A coordinated team of agents is transformative.

Since Anthropic released Agent Teams on February 5, 2026, alongside the Opus 4.6 model, Claude Code has crossed a fundamental threshold: you’re no longer limited to a single agent working sequentially through your codebase. You can now spin up multiple Claude agents that communicate with each other, divide work, and execute tasks in parallel — like a small engineering team that happens to operate at machine speed.

Multi-Agent Orchestration: 4 Patterns That Actually Work

Thu, 26 Feb 2026 10:00:00 +0800

A single AI agent can do impressive things — until it cannot. Give one agent a codebase with 200 files, a conversation history spanning 50 exchanges, and instructions that mix refactoring with testing with documentation, and you will watch it slowly fall apart. Responses drift. Costs spike. The agent starts “hallucinating” connections between unrelated parts of your project.

OpenClaw Multi-Agent Guide: Architecture, Configuration, and Collaboration Patterns

Mon, 23 Feb 2026 17:00:00 +0800

Have you ever had this experience: your AI assistant starts “losing its mind” mid-conversation – you ask it to write a blog post, and it suddenly spits out code logic; you ask it to research competitors, and it starts correcting typos from last week?

The AI didn’t get dumber. You’re just making one “brain” do too many jobs. It’s like asking the front desk receptionist to also handle accounting, HR, and engineering – nobody ends up doing anything well.

Claude Code Agent Teams 2026: Parallel Multi-Agent Development (With Cost Optimization)

Sun, 22 Feb 2026 08:00:00 +0800

On February 5, Anthropic shipped Claude Code Agent Teams alongside Claude Opus 4.6 — an experimental feature that lets multiple Claude Code instances form a team and work in parallel. If Subagents are errand runners you send out on focused tasks, Agent Teams are an engineering squad whose members can discuss, coordinate, and challenge each other in real time. For complex scenarios involving cross-module development, multi-perspective code review, or parallel debugging, Agent Teams can compress hours of serial work into minutes.