Meta found that a single AI agent loaded with all the domain knowledge hallucinated in production, so they split the system into a deliberately dumb router that only sequences work and runs reliability checks, plus 14 specialist agents that each handle one domain. The result cut column configuration prep from about a day to under an hour, with roughly two-thirds of changes landing without significant human edits.

• A single "know everything" agent degraded past a certain context size, cross-contaminating naming conventions and code patterns across data sources; the fix was giving the central agent less to do, not better prompts or a bigger context window.
• The orchestrator is 1,300 lines and does no domain work: it follows a fixed seven-step protocol where only one step involves LLM reasoning, and that step runs inside an isolated subagent.
• New use cases are added by writing new workflow templates that sequence the existing 14 subagents differently, so pipeline migration needed zero new agent code.

Paper2Agent is a system that automatically transforms research papers into functional AI agents by extracting code and methods into MCP servers. You can use it through a skill in Claude Code or Codex to convert any paper's codebase into interactive tools.

• The system coordinates parallel specialist agents to extract scientific papers into reliable MCP servers with minimal manual setup
• You install the skill, point it at a paper URL and code repository, and it generates tested MCP tools ready to connect to your coding agent
• Three working examples (AlphaGenome, TISSUE, Scanpy) show agents handling genomic analysis, spatial transcriptomics, and single-cell preprocessing tasks with specific scientific queries

Jeff Dean's lecture breaks down the full AI engineering pipeline from building LLMs to orchestrating multi-agent systems, targeting developers who skip prompt engineering courses and need to understand how production AI actually works.

• Most developers learn AI wrong by focusing on prompt engineering instead of production systems fundamentals
• The lecture covers the complete stack: orchestration, RAG, evals, context engineering, and inference
• One person can coordinate 100 agents using proper engineering practices

A former Meta React compiler engineer and Netflix EM discusses how to build and manage multiple AI agents to automate routine work. The post highlights a SpaceXAI engineer running 10-20 agents coordinated by a "Chief of Staff" agent, framing this practical approach as more valuable than paid courses on agentic systems.

• A former SpaceX AI engineer (ex-Cursor) runs 10-20 Grok agents to automate 90% of routine work, coordinated by a "Chief of Staff" agent
• The podcast guest, an ex-Meta React compiler engineer and former Netflix EM, approaches agent tech with skepticism, detailing specific problems before showing how they're solved
• The poster frames this 50-minute conversation as more valuable than paid courses on agentic engineering

QM gives each employee and channel an isolated AI agent workspace with its own memory, files, permissions, and cron jobs while supporting shared scopes for collaboration in Slack or a web app. It’s model-agnostic—swap between Pi, OpenCode, Codex, or Claude Code—and offers admin controls for security postures, org-wide configs, and custom plugins. Deploy with the qm CLI by layering your org’s config and skills over the headless core, all under an MIT license.

• Every user and channel gets an isolated sandbox (memory, files, permissions, cron jobs) while still allowing shared skills across scopes, so teammates don't step on each other
• It's model-agnostic—swap between Pi, OpenCode, Codex, or Claude Code without changing the deployment
• Deployment cleanly separates org-specific config/skills from the upstream core, with helper skills (update-qm, upstream-pr) to sync a private fork in both directions
• Admins get org-wide controls over allowed harnesses/models, security postures (Strict, Auto, Dangerous), and skill/app access

Paperclip is an open-source platform that turns separate AI agents into a structured organization with roles, budgets, mission context, and audit logs. It solves coordination issues like task overlap, hidden API costs, and lost state through scheduled “heartbeats,” human approval gates, and a mission-driven context chain—all via a self-hosted CLI tool.

• Paperclip wraps AI agents in an org structure (roles, budgets, mission context, audit logs) to stop duplicated work, cost overruns, and lost state across restarts.
• It hit 43,000 GitHub stars since its March 2026 launch, signaling strong demand for agent coordination tools, not just smarter individual agents.
• Its "heartbeat" system addresses agents' lack of persistent memory by having them recheck a mission-to-task context chain each cycle rather than relying on retraining.
• Unlike LangChain or CrewAI, which build individual agents or pipelines, Paperclip manages agents collectively as a company, self-hosted under MIT license with no platform fee beyond LLM costs.

This article shows how solving complex problems benefits from a team of AI agents with roles like planner, doer, tool operator, critic, supervisor, and presenter. It breaks down each subagent’s function and gives tips on prompting, model choice, tuning, and context setup. The CDN-Folk case illustrates how a team of agents designed, validated, and deployed a content delivery network faster than traditional methods.

• Splitting AI work across specialized agent roles (planner, doer, tool operator, critic, supervisor, presenter) beats using one monolithic LLM for complex tasks
• The CDN-Folk case cut build time to 3.5 days using this multi-agent approach and produced a cleaner architecture than legacy methods
• Matching model size to task complexity (small/fast for simple generation, larger chain-of-thought models for planning) and fine-tuning on domain data improves accuracy
• Giving agents narrowly scoped context (relevant APIs, logs, databases only) keeps them focused and reduces errors

This article introduces PaperOrchestra, a multi-agent system that transforms raw idea summaries and experimental logs into submission-ready AI research papers using agents for outlining, plotting, literature review, writing, and refinement. It outperforms single-agent and state-of-the-art baselines on PaperWritingBench, a new benchmark of 200 CVPR and ICLR papers, in both literature review and overall manuscript quality.

• PaperOrchestra uses a multi-agent pipeline (outline, plots, citation graph via Semantic Scholar, LaTeX writing, peer-review-style refinement) to turn raw idea summaries and experiment logs into full draft papers.
• Tested on a new 200-paper benchmark (100 CVPR 2025, 100 ICLR 2025), it beat a monolithic LLM pipeline and AI Scientist-v2 by 50–68 points on literature-review quality and 14–38 points on overall manuscript quality in blind human evaluations by 11 AI researchers.
• Grounding citations in real API data and using iterative self-reflection agents reduced hallucinated references and shallow reviews compared to baselines.
• The tool is positioned as a drafting accelerator, not a replacement for authors—humans must still verify facts, originality, and ethics despite built-in flags for dubious content.

Paperclip is a platform that manages AI agents to streamline business operations. It allows users to set goals, hire agents, and monitor their performance from a centralized dashboard. Unlike traditional task managers, Paperclip integrates organizational structures and budget controls for efficient agent coordination.

• Paperclip orchestrates multiple AI agents as a unified company rather than just managing individual tasks, adding org charts, budget controls, and goal alignment on top.
• It runs locally via Node.js/pnpm with an embedded, self-managed PostgreSQL database, and can run multiple isolated companies from one deployment.
• Agents run on schedules or event triggers with built-in cost tracking to prevent budget overruns and maintain persistent state.
• The platform supports plugins, allowing extension beyond its core orchestration and governance features.