Multi-Agent Articles
Browse 584 articles about Multi-Agent.
Human-in-the-Loop Checkpoints for AI Agents: Why Full Autonomy Is the Wrong Goal
The best AI agent workflows aren't fully autonomous—they have human checkpoints at the right moments. Here's how to design them into your systems.
What Is Claude Sonnet 5? Anthropic's Most Agentic Sonnet Model Explained
Claude Sonnet 5 is Anthropic's most agentic Sonnet yet—faster and cheaper than Opus 4.8 while matching it on most tasks. Here's what changed.
What Is GLM 5.2? The Open-Weight Model With 1M Token Context and Frontier-Level Coding
GLM 5.2 is ZAI's 753B open-weight model with 1M token context, MCP support, and agentic coding at 1/5th the cost of frontier models.
Multi-Perspective AI Research: How Sub-Agents Beat Single-Prompt Deep Research
Using 5 expert sub-agents for research produces better results than 100+ parallel agents. Here's the architecture and why it works for AI workflows.
Human-in-the-Loop Checkpoints for AI Agents: Why Full Autonomy Is the Wrong Goal
The best AI workflows aren't fully autonomous. Learn how to identify the two or three checkpoints where human review prevents costly mistakes and AI slop.
How to Build a Multi-Perspective AI Research Workflow Using the STORM Method
Stanford's STORM method uses five expert agent personas to produce research 25% more organized than single-prompt approaches. Here's how to build it.
Sub-Agents vs Agent Teams in Claude Code: What's the Difference and When to Use Each
Sub-agents report to one session but can't talk to each other. Agent teams can debate and collaborate. Learn which architecture fits your workflow.
How to Coordinate Multiple AI Agents Without Copying and Pasting Between Tools
Most AI users manually carry context between tools. Learn how ticket-based queues and shared state let agents hand off work without human intervention.
Self-Scaffolding AI Models: How Ornith 1.0 Writes Its Own Agent Harness
Ornith 1.0 generates custom harnesses for each task instead of relying on human-written scaffolds. Learn how self-scaffolding works and why it matters.
What Is Sakana Fugu? The Multi-Model Orchestrator That Routes Prompts Automatically
Sakana Fugu is an orchestrator model that routes prompts to the best AI model automatically. Learn how it works and when to use Fugu vs Fugu Ultra.
What Is the Agent Harness? Why It Matters More Than the Model You Choose
Google says the LLM is only 10% of an agentic system. The harness—rules, tools, context, and guardrails—drives the other 90%. Here's what that means.
What Is Sakana Fugu? The Multi-Model Orchestrator Explained
Sakana Fugu is an AI orchestrator that routes prompts to the best model automatically. Learn how it works, its two tiers, and real benchmark results.
What Is a Loop of Loops? How to Build AI Agents That Coordinate Recurring Work
A loop of loops lets multiple recurring AI jobs notice each other, share context, and hand off tasks. Here's the concept and how to build one for your business.
What Is Sakana Fugu Ultra? The Multi-Model Orchestrator That Beats Frontier AI
Sakana Fugu Ultra is an LLM pool that coordinates multiple models to outperform GPT and Claude on coding benchmarks. Here's how it works.
What Is Cursor's Composer Model? How a Coding Tool Became a Frontier AI Lab
Cursor trained Composer 2.5 on Qwen K2.5 with novel RL techniques, competing with GPT 5.5 and Opus. Learn how the SpaceX acquisition changes everything.
Sakana Fugu vs Claude Opus 4.8: Is Multi-Model Orchestration Worth the Cost?
Fugu is 5x more expensive and 4.5x slower than Opus 4.8 with similar results. Here's when multi-model orchestration actually makes sense for your workflows.
What Is Cursor's Composer Model? How the AI Coding Tool Became a Frontier Lab
Cursor is training a 1.5T parameter model from scratch using SpaceX compute. Here's what it means for AI coding agents and the future of agentic development.
What Is Sakana Fugu? The Multi-Agent AI System That Beats Frontier Models
Sakana Fugu orchestrates Claude, GPT, and Gemini through one API to outperform single models on benchmarks. Here's how it works and when to use it.
AI Agent Ownership: Why Every Agent Needs a Single Responsible Owner
Unowned AI agents cause silent failures. Learn the four pillars of agent ownership—job, diet, boundaries, and review loop—to keep agents reliable.
How to Build an AI Agent Roster for Your Team: The Agent Ownership Card
Every agent your team uses needs a name, owner, job, sources, permissions, and known failure modes. Here's how to build and maintain an agent registry.