Multi-Agent Orchestration
When and how to run multiple AI agents as a team — Paperclip vs OpenClaw architecture, multi-agent companies, agent role design, when single-agent loops are better.
How to Prevent AI Sycophancy: Why Your Agent Agrees With Everything and How to Fix It
AI models agree with users 88% of the time. Learn how to use adversarial councils, devil's advocate prompts, and structured critique to get honest AI feedback.
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 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 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.
How to Build a Multi-Model LLM Council for Better AI Decisions
Run multiple AI models in parallel, have them rank each other's answers, and synthesize a final response. Learn when LLM councils beat single-model outputs.
How to Build an LLM Council: Ensemble AI Agents with Blind Ranking and Synthesis
Learn how to build a multi-model AI council where agents answer independently, rank each other anonymously, and a chairman synthesizes the final answer.
Multi-Model AI Agent Councils: Do Multiple LLMs Give Better Answers Than One?
Running GPT, Claude, and Gemini in parallel with blind peer review and a chairman synthesizer can beat any single model—but only for the right tasks.
What Is the OmniAgent Meta Harness? How to Orchestrate Claude, Codex, and Local Models Together
OmniAgent is Databricks' open-source meta harness that runs Claude, Codex, and local models under one roof with shared sessions and cross-vendor review.
How to Use OmniAgent to Orchestrate Claude and Codex in One Workflow
Learn how to use OmniAgent's Polly orchestrator to delegate implementation to Claude and code review to Codex in a single automated pipeline.
What Is OmniAgent? The Open-Source Meta Harness for AI Agent Orchestration
OmniAgent is Databricks' open-source meta harness that lets you run Claude, Codex, and custom agents under one roof with shared sessions and policies.
What Is a Meta Harness for AI Agents? How OmniAgent Orchestrates Claude, Codex, and More
A meta harness sits above individual AI coding agents to orchestrate them together. Learn how OmniAgent unifies Claude Code, Codex, and Pi in one session.
How to Use Claude Fable 5 Dynamic Workflows for Parallel Sub-Agent Execution
Claude Fable 5 paired with dynamic workflows can spawn hundreds of parallel sub-agents. Learn how to use this combination for massive agentic coding tasks.
How to Build Custom Sub-Agents in Claude Code: YAML, Tools, and Triggers
Custom sub-agents in Claude Code are markdown files with YAML front matter. Learn how to write descriptions, set tools, and trigger agents automatically.
How to Use AI for Ad Creative Variation at Scale: The Marketing Sub-Agent Pattern
Anthropic's growth team uses two specialized sub-agents—one for headlines, one for descriptions—to generate hundreds of ad variations in minutes.
How to Use Claude Code Agent Teams for Multi-Perspective Brainstorming
Claude Code agent teams let multiple AI personas debate and reach consensus. Here's how to enable the feature and use it for strategy and analysis.
What Is the /workflows Command in Claude Code? Dynamic Multi-Agent Workflows Explained
The /workflows command in Claude Code lets you compose multi-agent workflows dynamically with full transparency. Here's how it works and when to use it.
What Is the Piling Problem in AI Agent Workflows? How to Prevent Output Bottlenecks
When agents generate work faster than humans can review it, output piles up. Here's how to design agentic pipelines that prevent unsustainable backlogs.
How to Use AI Agents to Build and Test LLM Benchmarks: Lessons from Claude Opus 4.8
Claude Opus 4.8 built an entire economic simulation benchmark autonomously. Learn how to use AI agents to design and run your own LLM evals.
What Is the Implement-Verify-Fix Loop in Multi-Agent AI Systems?
Dynamic workflows use an implement-verify-fix loop where independent agents adversarially review each other's work. Here's how it works and when to use it.
How to Use Parallel Agent Execution to Build and Compare Multiple Product Strategies at Once
Run three agents on three isolated databases to test different product strategies simultaneously. Learn the parallel exploration pattern for agentic work.
How to Orchestrate Multiple Claude Code Sessions for Large-Scale Automation
Learn how to chain multiple Claude Code sessions using the RALF loop pattern to handle large tasks without overwhelming a single agent context window.
What Is the RALF Loop? How to Chain AI Coding Sessions for Autonomous Task Completion
The RALF loop automates multiple Claude Code or Codex sessions to complete large tasks without babysitting. Learn how it works and when to use it.
How to Use AI Agents for Long-Running Tasks: Lessons from the Emergence AI Town Experiment
A 15-day multi-agent simulation revealed how different models behave over time. Learn the key lessons for designing production AI agent systems.
AI Agent Infrastructure: The 5 Control Layers That Decide If Your Agent Ships
Runtime, identity, data, payments, and observability—these five infrastructure layers determine whether your AI agent reaches production. Here's what each does.