Multi-Agent Articles
Browse 431 articles about Multi-Agent.
What Is the Agent Discovery Problem? Why AI Agents Need an App Store to Find Each Other
As every business deploys AI agents, agent discovery becomes a massive unsolved problem. Learn what an agent-native app store would look like.
What Is the AI Trading Bot Challenge? How OpenClaw Performed With $10,000 Over 30 Days
Two creators gave OpenClaw $10,000 each to trade stocks for 30 days. Both bots outperformed the S&P 500 during a volatile market. Here's what happened.
What Is the Anthropic OpenClaw Ban? Why Third-Party Harnesses Were Blocked
Anthropic blocked Claude subscriptions from powering third-party tools like OpenClaw. Learn what changed, why it happened, and what to do instead.
How to Build an AI Stock Trading Bot With OpenClaw: Strategy, Setup, and Lessons Learned
Learn how to build an autonomous stock trading agent with OpenClaw, including strategy design, cron job scheduling, and what not to do with options.
Inside Claude Code's Shared Task List: How Agents Avoid Conflicts
Claude Code's shared task list uses git worktrees and status flags to prevent file conflicts when multiple agents edit a codebase. Here's the mechanic in detail.
What Is the Claude Code Builder-Validator Chain? How to Build Quality Checks Into AI Workflows
The builder-validator chain uses one sub-agent to build and another to review, giving you automated quality checks without manual code review.
Run Multiple Claude Code Sessions in Parallel With Git Worktrees
Use the Claude Code -w flag to spin up isolated git worktrees and run several AI coding sessions in parallel — one per branch, with zero context bleed.
What Is the Claude Code Operator Pattern? How to Run Multiple AI Agents in Parallel Terminals
The operator pattern lets you run multiple Claude Code sessions in isolated Git worktrees simultaneously, each with its own clean context window.
What Is the Claude Managed Agents Dashboard? How to Monitor Sessions, Environments, and Costs
Anthropic's Managed Agents dashboard gives you full visibility into agent sessions, token usage, environment permissions, and credential vaults.
Archon Explained: The Meta-Agent Framework for AI Coding Workflows
Archon is a meta-agent framework that orchestrates AI coding agents through YAML pipelines. Here's what a harness builder is and why teams are adopting one.
Harness Engineering: Orchestrating AI Coding Agents
Harness engineering is the discipline of orchestrating multi-agent AI coding sessions into reliable pipelines. Here's how the practice is taking shape.
What Is the AI Model Tipping Point? How Claude Opus 4.5 Made Agentic Tools Actually Work
Agentic tools failed with GPT-3.5 but work with Claude Opus 4.5 and 4.6. Learn why model quality—not tooling—is the real driver of the agentic AI revolution.
How Claude Code Parallel Agents Coordinate Through an Orchestrator
Claude Code's parallel agents use an orchestrator-subagent model to split work across instances. Learn how to enable it and what the token tradeoff looks like.
Beyond One-Shot Prompts: 5 Claude Code Workflow Patterns Explained
Anthropic's five Claude Code workflow patterns explained with real engineering tasks — schema migrations, test loops, and review chains where each pattern fits.
When to Use Split-and-Merge in Claude Code (vs. Sequential Chains)
A practical guide to choosing split-and-merge over sequential chains or agent teams in Claude Code, with orchestration patterns and merge strategies.
Anthropic Managed Agents: A Hosted Runtime for Claude + MCP
Managed Agents pairs Claude's agentic API with MCP and hosted compute. Here's what the platform includes and how it changes Claude-based agent deployment.
Wrap Claude Code and Codex With Archon for Determinism
Use Archon to wrap Claude Code and OpenAI Codex CLI in YAML workflows that are version-controlled, reviewable, and reproducible across runs.
Autonomy vs. Control in Claude Code: 5 Agentic Workflow Patterns
Five Claude Code patterns ranked by autonomy: sequential, operator, split-and-merge, agent teams, headless. Pick the right tradeoff between control and speed.
From 80% to 93.9%: Why the Claude Mythos SWE-Bench Jump Matters
Claude Mythos jumped from 80% (Opus 4.6) to 93.9% on SWE-Bench Verified. A look at the gain and what it changes for agentic coding workflows in practice.
Claude Code Split-and-Merge: A Deep Dive Into Sub-Agent Parallelism
How Claude Code's split-and-merge pattern uses isolated context windows across 10 sub-agents to handle codebases too large for a single agent to process.