Multi-Agent Articles
Browse 597 articles about Multi-Agent.
What Is the Access-Meaning-Authority Framework for AI Agents?
Access gets agents into your systems. Meaning tells them what actions do. Authority determines who can do what. Learn why all three layers matter.
How to Design AI Agent Permissions That Users Actually Trust: The 5-Rung Ladder
Read, Suggest, Draft, Act with confirmation, Autonomous — the five permission levels that move users from skeptical to trusting AI agents. Build this in a day.
Alex Finn Built a Full Video Game in 1 Hour Using Codex's /goal Command — Here's How
Alex Finn used Codex's /goal agentic loop to build a complete extraction shooter with auto-generated assets in under an hour. The exact workflow.
DeepMind's Eve Online AI Agents Get Their Own Server — What the Sandbox Separation Actually Means
DeepMind's Eve agents won't touch the main Tranquility server. Here's what the sandboxed pocket environment means for agent training validity.
Demis Hassabis Personally Pushed the Eve Online Deal — What It Reveals About DeepMind's Agent Roadmap
Hassabis drove DeepMind's Eve Online equity deal himself. The progression from Atari to Chess to Eve Online reveals exactly where agent research is heading.
Google DeepMind Buys Into Eve Online: 5 Reasons It's the Perfect AI Agent Training Ground
DeepMind just took an equity stake in Eve Online's developer. Here's why a 20-year-old space MMO is the ideal environment to train frontier AI agents.
How to Build an Agentic Context Grounding System for Any Vertical
Agentic context grounding prevents generic AI outputs by reading a source of truth before generating. Learn how to build this pattern for any industry.
Meta's 'Hatch' Consumer Agent Runs on Claude — Not Llama. Here's What That Means.
Meta is training its new consumer agent 'Hatch' on Claude models, not Llama — paying Anthropic to build the agent that will eventually compete with Anthropic.
Perplexity's Comet Browser Is a Semantic Work Graph Play — Not Just a Search Replacement
Perplexity's Comet isn't competing with Chrome on features — it's trying to own the semantic meaning layer across all your work. Here's the strategic logic.
SAP Is Blocking AI Agents. Salesforce Is Welcoming Them. One of These Strategies Will Win.
SAP is actively blocking agents from its platform. Salesforce is going headless and MCP-first. Here's why one of these enterprise strategies will dominate.
What Are Semantic Work Primitives? The Missing Layer in AI Agent Design
Semantic work primitives give AI agents the meaning behind actions—not just access. Learn why this layer matters more than computer use or MCP servers.
What Is Multi-Variation Generation in AI Agents? How to Surface Better Decisions
Multi-variation generation has AI agents produce multiple options upfront instead of forcing users to ask for alternatives. Here's how to implement it.
Why Most AI Agents Fail in Production: The 3-Layer Framework Every Builder Needs to Know
Access, Meaning, Authority — the three layers that separate demo-worthy agents from production-ready ones. Here's the framework and where most agents break.
Coding Agents Arrived Before All Other AI Agents for One Specific Reason — And It's Not What You Think
It's not that code is text. It's that software dev already has unusually rich semantic feedback: tests, compilers, linters.
Your AI Agent Is Underperforming: Run This 4-Question Harness Audit Before Switching Models
Before you upgrade your model, run this 4-question audit on your orchestration layer. Most performance problems live there, not in the weights.
AI Burnout Isn't From Typing More — It's Judgment Drain: Why Agent Users Hit a Wall at 4 Hours
Managing agent fleets depletes a different cognitive resource than normal work. Judgment drain caps productive hours at 4-5 — not 8-10. Here's the mechanism.
How Alex Finn Built a Complete Game in 1 Hour Using Codex's /goal Command
Alex Finn ran a single /goal prompt and let Codex build an extraction shooter game — assets included — over one autonomous hour. Here's how it worked.
What Is the Anticipation Gap? Why Consumer AI Agents Are Still Reactive
Most AI agents wait to be asked. The anticipation gap explains why truly proactive agents don't exist yet and what it will take to build them.
Better Model vs. Better Harness — Which One Actually Moves Your Agent's Benchmark Score?
The same model shows up to 6x performance variation based solely on harness design. Here's the data on where to invest first.
Why Consumer AI Agents Still Feel Disappointing: 5 Rungs They Haven't Climbed Yet
The ladder of trust — from read-only to fully autonomous — explains exactly where every consumer agent product is stuck and what it would take to move up.