{ "schema_version": "1.1", "id": "atlas-multi-agent", "slug": "multi-agent", "title": "Multi-Agent", "url": "https://feed7.dev/atlas/multi-agent", "current_answer": null, "implementation_consequence": null, "agent_context": null, "confidence": "auto_collected", "last_verified": null, "last_updated": null, "evidence": [ { "schema_version": "1.1", "id": "p12", "slug": "conductor-mac-app", "url": "https://feed7.dev/p/conductor-mac-app", "title": "Conductor: a Mac app that runs local agent fleets", "why_included": "Runs multiple Claude Code sessions in parallel worktrees with a review queue — on your machine.", "summary": "Native Mac app: each agent gets a git worktree, results land in one review queue. Free tier covers 3 parallel agents.", "practical_implication": "Parallel agents stop stepping on each other. Worth adopting if you run more than one coding session a day.", "agent_context": "Conductor runs parallel Claude Code sessions in isolated git worktrees, merged via a review queue. Tested: 3 agents on one repo, no conflicts. Limitation: worktree cleanup is manual.", "source": { "name": "Mac app", "url": "https://conductor.build", "published_at": "2026-06-20T00:00:00.000Z" }, "source_class": "tool", "content_type": "Mac App", "layer": "tools", "domains": [ "coding" ], "topics": [ "multi-agent", "coding-agents" ], "verification": { "status": "founder_tested", "label": "Founder Tested", "method": "founder_test", "verified_at": "2026-06-22T00:00:00.000Z" }, "uncertainty": [ "Worktree cleanup is manual; disk usage grows fast." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-06-20T00:00:00.000Z", "modified_at": "2026-06-20T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/conductor-mac-app", "json": "https://feed7.dev/p/conductor-mac-app.json", "markdown": "https://feed7.dev/p/conductor-mac-app.md" } }, { "schema_version": "1.1", "id": "auto-32d427eb72", "slug": "agent-swarms-and-the-new-model-economics-32d427eb72", "url": "https://feed7.dev/p/agent-swarms-and-the-new-model-economics-32d427eb72", "title": "Agent swarms and the new model economics", "why_included": "Use a strong planner with bounded cheaper workers, separate contexts, shared decisions, and independent review lenses.", "summary": "Cursor’s SQLite experiment suggests swarm harness design can matter more than model mix: separate planner and worker contexts reduced coordination churn while cheaper workers handled most tokens.", "practical_implication": "For large agent runs, reserve frontier models for decomposition and decisions, then give bounded implementation work to cheaper workers. Separate their contexts, record shared design decisions, delegate conflict resolution, limit oversized files, and stack independent review lenses.", "agent_context": "Cursor’s new planner-worker swarm reached **73%–85%** of a held-out SQLite test suite within four hours, and every new configuration later reached **100%**. Similar quality across model mixes cost from **$1,339 to $10,565**.\n\nFor large agent runs, reserve frontier models for decomposition and decisions, then give bounded implementation work to cheaper workers. Separate their contexts, record shared design decisions, delegate conflict resolution, limit oversized files, and stack independent review lenses.\n\nThis was a controlled but unusual SQLite reimplementation with no source, binary, tests, or internet available to agents. Cursor tested four configurations rather than the full planner-worker matrix, and workers consumed at least **69% of tokens**, so workload shape will strongly affect the economics.", "source": { "name": "Cursor", "url": "https://cursor.com/blog/agent-swarm-model-economics", "published_at": "2026-07-20T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "agent", "domains": [ "coding" ], "topics": [ "multi-agent", "harness-engineering", "subagents" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-20T00:00:00.000Z", "modified_at": "2026-07-20T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/agent-swarms-and-the-new-model-economics-32d427eb72", "json": "https://feed7.dev/p/agent-swarms-and-the-new-model-economics-32d427eb72.json", "markdown": "https://feed7.dev/p/agent-swarms-and-the-new-model-economics-32d427eb72.md" } }, { "schema_version": "1.1", "id": "auto-fb423aadb9", "slug": "run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9", "url": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9", "title": "Run multiple isolated agents in a single Sandbox", "why_included": "Give each agent a private Linux user and expose only selected collaboration files through a shared group directory.", "summary": "Vercel Sandbox can now isolate agents as Linux users while exposing selected files through a shared group directory. This gives multi-agent harnesses a simpler permission boundary.", "practical_implication": "Create one user per agent and expose only the workspace they need through a group. This makes coder-reviewer or other multi-agent roles easier to separate without provisioning a sandbox for each role.", "agent_context": "The **@vercel/sandbox SDK** now supports multiple Linux users and groups. Each agent gets a **private home directory**; users cannot read, write, or list one another’s files, while a **shared group directory** supports collaboration.\n\nCreate one user per agent and expose only the workspace they need through a group. This makes coder-reviewer or other multi-agent roles easier to separate without provisioning a sandbox for each role.\n\nThe boundary is user and group permissions inside one Sandbox. The material does not claim separate kernels, resource quotas, or protection from vulnerabilities that cross operating-system user boundaries.", "source": { "name": "Vercel", "url": "https://vercel.com/changelog/run-multiple-isolated-agents-in-a-single-sandbox", "published_at": "2026-07-30T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "infra", "domains": [ "coding", "security" ], "topics": [ "sandboxing", "multi-agent", "agent-reliability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-30T00:00:00.000Z", "modified_at": "2026-07-30T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9", "json": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9.json", "markdown": "https://feed7.dev/p/run-multiple-isolated-agents-in-a-single-sandbox-fb423aadb9.md" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks", "url": "https://arxiv.org/abs/2607.28587v1", "source_name": "arXiv", "published_at": "2026-07-30T17:42:44+00:00", "summary": "PAIChecker finds PR–issue mismatch in 13.6% of SWE-bench Verified instances. Benchmark maintainers should validate that the issue, patch, and tests describe the same task before trusting agent scores." }, { "title": "First Steps Toward Automated AI Research — Richard Socher, CEO Recursive AI", "url": "https://www.youtube.com/watch?v=pWXUkLP9uWM", "source_name": "AI Engineer", "published_at": "2026-07-30T16:59:37+00:00", "summary": "Socher’s automated-research design combines prior knowledge, measurement data, simulation, physical experiments, and agent orchestration, with early demonstrations in training and CUDA optimization." }, { "title": "Let's integrate AI Agents in Event-Sourced Systems — Divakar Kumar, FlyersSoft", "url": "https://www.youtube.com/watch?v=o6U_2vd967Y", "source_name": "AI Engineer", "published_at": "2026-07-30T00:00:06+00:00", "summary": "Add agents beside deterministic fraud systems, not in place of them: project cross-service events into a semantic layer, send only uncertain cases to specialist agents, then emit the verdict back into the saga." }, { "title": "The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation", "url": "https://arxiv.org/abs/2607.24720v1", "source_name": "arXiv", "published_at": "2026-07-27T17:55:03+00:00", "summary": "Controlled experiments suggest long-horizon agent planning depends on explicit state transitions, some compositional trajectories, and compatible teacher patterns—not atomic skills alone." }, { "title": "The Future of Evals: From LLM as a Judge to Agent as a Judge — Aparna Dhinakaran, Arize AI", "url": "https://www.youtube.com/watch?v=q2JrUKBMf0w", "source_name": "AI Engineer", "published_at": "2026-07-24T20:00:06+00:00", "summary": "Fixed-rubric judges miss failures that emerge across long, variable agent trajectories. Arize argues for adding agent-based analysis while retaining deterministic and LLM-judge evals." }, { "title": "Why We Killed Our Multi-Agent Pipeline — Subbiah Sethuraman and Abhilash Asokan, ZS Associates", "url": "https://www.youtube.com/watch?v=u6jJcIFDLE4", "source_name": "AI Engineer", "published_at": "2026-07-23T05:00:02+00:00", "summary": "A fixed chain of specialist agents lost context and produced incoherent recommendations. The replacement separates deterministic detection, gives one agent end-to-end ownership, and uses subagents only for bounded investigations." }, { "title": "FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation", "url": "https://arxiv.org/abs/2607.12982v1", "source_name": "arXiv", "published_at": "2026-07-14T17:24:57+00:00", "summary": "FormalAnalyticGeo shows a reusable synthetic-data pipeline: agents generate problems, compile them into a formal representation, render exact diagrams, measure answers, and retry failed checks." }, { "title": "MadsLorentzen/ai-job-search", "url": "https://github.com/MadsLorentzen/ai-job-search", "source_name": null, "published_at": null, "summary": "A trending Claude Code framework (8.4k stars) that runs a job hunt end to end: /scrape ranks postings, /apply tailors LaTeX CVs, and a second reviewer agent plus a PDF-compile loop verifies the output." }, { "title": "Government of Alberta uses Claude to find and fix cybersecurity vulnerabilities across government systems", "url": "https://www.anthropic.com/news/alberta-government-claude-cybersecurity", "source_name": null, "published_at": null, "summary": "Alberta's government ran 50 parallel Claude Code agents over 466M lines of code, compressing a security review estimated at 6.5 years into 20 hours — with every patch still gated on human review." }, { "title": "What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates", "url": "https://arxiv.org/abs/2607.02507v1", "source_name": null, "published_at": null, "summary": "Dual-channel debates show social structure alone makes LLM agents diverge: public statements split from private ones, with decision divergence jumping from ~3% to ~40% across 10 models — no deceptive prompt needed." }, { "title": "Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation", "url": "https://arxiv.org/abs/2607.09600v1", "source_name": null, "published_at": null, "summary": "Agora routes reasoning steps through an auction among expert models and tools, adding a single control for cost versus quality and outperforming matched baselines on five benchmarks." }, { "title": "New research shows how AMIE, our medical AI, could help manage health conditions.", "url": "https://blog.google/innovation-and-ai/models-and-research/google-research/amie-for-disease-management-in-nature/", "source_name": null, "published_at": null, "summary": "Google's AMIE matched 21 primary-care physicians on longitudinal disease management in a blinded Nature study, scoring higher on plan preciseness and guideline alignment. Research-stage, not deployed." }, { "title": "The Factory That Dreams: 39 AI Agents, No Framework - Rushabh Doshi, Machinecraft", "url": "https://www.youtube.com/watch?v=jtzh-GBXBWc", "source_name": null, "published_at": null, "summary": "A 100-person factory built a multi-agent sales system around organized company memory, specialist roles, and human approval. The useful pattern is retrieval and governance, not custom model training." }, { "title": "Shubhamsaboo/awesome-llm-apps", "url": "https://github.com/Shubhamsaboo/awesome-llm-apps", "source_name": null, "published_at": null, "summary": "This Apache-2.0 collection provides runnable agent, skill, MCP, memory, multi-agent, and RAG examples across major model providers, useful for borrowing patterns before choosing a stack." }, { "title": "Don't Build Agents You Can't Answer For — Addy Osmani", "url": "https://www.youtube.com/watch?v=n97BCfyFIvw", "source_name": null, "published_at": null, "summary": "Osmani argues that agent throughput makes evidence-backed judgment the engineering bottleneck: builders should require explainable diffs, tests, logs, and explicit ownership before shipping." }, { "title": "The Agentic Web and the Bazaar Era of AI - Ramesh Raskar, MIT Media Lab", "url": "https://www.youtube.com/watch?v=sum9DgexFRQ", "source_name": null, "published_at": null, "summary": "Project Nanda proposes open discovery, identity and coordination layers for agents across vendors. Its local simulator lets builders test one protocol layer without building the entire network." }, { "title": "What Does Done Even Mean? Agents and Paperclip's Liveness Model - Dotta, Paperclip", "url": "https://www.youtube.com/watch?v=7P0elyLIxXo", "source_name": null, "published_at": null, "summary": "Model “done” as evidence, verification, authority, residual risk and next ownership—not an agent-set Boolean. This separates forward progress from approval in multi-agent workflows." }, { "title": "Claude Science, an AI workbench for scientists, is now available", "url": "https://www.anthropic.com/news/claude-science-ai-workbench", "source_name": null, "published_at": null, "summary": "Claude Science (beta, June 30) packages 60+ domain skills, a coordinator/specialist/reviewer agent stack, and HPC/Modal compute into a research workbench with reproducible, auditable outputs." }, { "title": "openai/codex-plugin-cc", "url": "https://github.com/openai/codex-plugin-cc", "source_name": null, "published_at": null, "summary": "OpenAI's plugin lets you drive Codex from inside Claude Code—slash commands for code review, adversarial critique, and delegating or handing off tasks to Codex background jobs. ~629 stars today." }, { "title": "Building a C compiler with a team of parallel Claudes", "url": "https://www.anthropic.com/engineering/building-c-compiler", "source_name": null, "published_at": null, "summary": "Sixteen parallel Opus 4.6 agents wrote a 100k-line Rust C compiler in two weeks (~$20k) that builds Linux 6.9 — the writeup credits test quality and context hygiene, not raw model capability." }, { "title": "Harness design for long-running application development", "url": "https://www.anthropic.com/engineering/harness-design-long-running-apps", "source_name": null, "published_at": null, "summary": "An Anthropic harness for multi-hour app builds pairs a generator agent with a Playwright-driven evaluator to counter self-grading bias — a $200, 6-hour run versus $9 solo, and it got simpler on Opus 4.6." }, { "title": "ogulcancelik/herdr", "url": "https://github.com/ogulcancelik/herdr", "source_name": null, "published_at": null, "summary": "Terminal multiplexer for coding agents: a real terminal per agent across Claude Code, Codex, Copilot and 15+ others, with blocked/working/done state in a sidebar, persistent sessions, and a socket API." } ] }