Multi-Agent Articles
Browse 584 articles about Multi-Agent.
AI Industry Shift: Why the Model Race Is No Longer the Only Race That Matters
Meta is monetizing infrastructure, OpenAI is buying regulatory headroom, and the AI scoreboard has changed. Here's what it means for builders.
AI Agent Evaluators and Verifiers: How to Stop Agents from Grading Their Own Work
Agents that evaluate their own output produce biased results. Learn how to build separate evaluator and verifier components that catch errors before they ship.
AI Agent Observability: How to Monitor Agents Running for Hours Without Babysitting
Learn how to set up observability for long-running AI agents—tracing costs, latency, failures, and decisions—so you can intervene before things go wrong.
AI Model Routing: When to Use Frontier Models vs Cheap Models in Your Agent Stack
Frontier models excel at imagining new tasks; cheap models execute known ones. Learn how to route intelligently and where each model tier creates real value.
How to Build a Long-Running AI Agent That Doesn't Go Off the Rails
Long-running agents need goals, evaluators, verifiers, loops, orchestration, observability, and memory. Here's how to design each component correctly.
What Is the Dark Factory Approach to AI Coding? How to Ship Code Without Human Bottlenecks
The dark factory is a fully autonomous AI coding pipeline: spec goes in, shipped code comes out. Learn what it takes to build one and when it makes sense.
AI Agent Evaluators and Verifiers: How to Stop Agents from Grading Their Own Work
Agents that evaluate their own output produce biased results. Learn how to build separate evaluator and verifier components that catch errors before they ship.
AI Agent Observability: How to Monitor Agents Running for Hours Without Babysitting
You can't watch a 6-hour agent session. Learn how to set up dashboards, traces, and monitors so you know exactly when to step in and when to let it run.
How to Build a Long-Running AI Agent That Doesn't Go Off the Rails
Long-running agents need 7 components to stay reliable: goal, evaluator, verifiers, loops, orchestration, observability, and memory. Here's how to build them.
What Is the Dark Factory Approach to AI Coding? How to Ship Code Without Human Bottlenecks
The dark factory takes a spec and ships production code with no human in the loop. Learn the architecture, required agents, and why most teams aren't ready yet.
AI Agent Evaluators and Verifiers: How to Stop Agents from Grading Their Own Work
Learn why AI agents shouldn't evaluate their own output and how to build separate evaluator and verifier components that catch errors before they ship.
AI Agent Observability: How to Monitor Agents Running for Hours Without Babysitting
Discover how to add observability to long-running AI agents so you can catch failures, track costs, and fix issues before users notice.
What Is the Gate Pattern for AI Agents? Why Agents Should Prepare, Not Submit
The gate pattern stops AI agents before they submit, pay, or sign. Learn why this design principle is essential for high-trust agentic workflows.
How to Build a Long-Running AI Agent: 7 Components You Need
Learn the 7 essential components for building autonomous AI agents that run for hours without drifting, stopping early, or going off the rails.
What Is the Outer Loop Pattern for AI Agents? How to Keep Agents Running Until Done
The outer loop pattern wraps AI agents in a control mechanism that checks progress, compares against goals, and restarts agents that stop too early.
What Is the Dark Factory Approach to AI Coding? How to Ship Code Without Human Bottlenecks
The dark factory is a fully autonomous AI coding pipeline that takes a spec and ships production code. Learn what it takes to build one reliably.
How to Build an OKF Knowledge Bundle and Share It with Any AI Agent
OKF bundles let you package structured knowledge and share it across agents. Here's how to build one, add metadata, and deploy it to your second brain.
How to Use GLM 5.2 in Agent Harnesses: Cursor, OpenCode, and Claude Code
GLM 5.2 integrates with Cursor, OpenCode, and Claude Code for agentic coding tasks at roughly one-fifth the cost of frontier models.
What Is GLM 5.2? The Open-Weight Model With 1M Token Context for Agentic Workflows
GLM 5.2 is ZAI's flagship open-weight model with 1M token context, MCP support, and frontier-level coding at a fraction of the cost.
Claude Sonnet 5 Token Efficiency Problem: Why It Can Cost More Than Opus 4.8 in Agents
Claude Sonnet 5 uses 30% more tokens than other models due to its agentic design. Learn when it costs more than Opus and how to manage usage.