AI Agents

AI AGENTS

106 SRC

106 sources Updated May 24, 2026

AI Agents

The AI agent ecosystem has crossed from infrastructure-building into platform, and the infrastructure layer itself has consolidated from a sprawl of point tools into a recognizable production stack of named primitives — Pipecat (voice), browser-use (web), Mem0 (memory), Composio (1,000+-app OAuth), RAGFlow/Dify (retrieval/workflows), with Mastra as the TypeScript-first framework (1.77M monthly npm downloads, YC). Cost architecture is foundational: 80% of agent tasks are janitorial, so hierarchical model routing (the 80/15/5 distribution) yields ~10x cost reduction, and configuration is converging on three files — SOUL.md (brutally specific constitution), USER.md (~4000-word user model), AGENTS.md (operational playbook). MCP is becoming the survival-level integration protocol: vendors without MCPs become unusable in agent-driven workflows. The infrastructure layer now includes reusable web-agent skill catalogs, anti-detection browsers, local self-improvement plugins, and remote Mac/Tailscale command networks, showing that agent capability depends as much on operating substrate as model intelligence.

The orchestration consensus has shifted decisively from a single powerful agent to one coordinator managing teams of sub-agents — swarms fail from coordination, not intelligence (task assignment, deduplication, handoff, human-in-the-loop are the unsolved UX problems). Concrete surfaces have shipped: Hermes Agent v0.12.0's Kanban board, the Discord-intake / Kanban-execution bridge, gstack's 30-second 6-specialist Claude Code team, @conductor_build's Opus-planning / GPT-5.5-review workflow, NovaStation's unified command center ("AI stops being a tab and becomes the OS"), remote-device networks where mobile devices command one always-on dev machine, and finance as the proving ground for multi-agent debate as the dominant high-stakes reference pattern (see Ai Trading). On the automation side the org-design implications are concrete and increasingly counterintuitive: Marcus Moretti runs Spiral at Every as a one-person team with strategy.md plus a /ce:product-pulse cron replacing 60% of a PM's old week, Every's broader automation push grew headcount from 4 to 30 after GPT-3, the unit of automation has shifted from job to cross-functional process, and new roles (the "agent engineer" internal-FDE, the matching "agent PM") follow the agent's affordances.

Products have multiplied across verticals as the field moves to role-specific harnesses: finance (Dexter, AutoHedge, Vibe-Trading, Fincept Terminal), outbound sales platforms, tax/estate-planning skills encoding judgment ($1k–$20K saved per user), document-KB tools (Cabinet, Tolaria, ByteRover), design/motion engines ("to draw" as an agent primitive), local-first agentic job search, self-hosted model/workspace wrappers like LibreChat, and AI-native workspaces rebuilding Slack + Linear + Notion as one surface. The defining lesson of skills & distribution is that domain-knowledge distribution is a documentation problem, not a code problem — "skills as markdown," GitHub-backed Claude Teams marketplaces, LLM-maintained knowledge bases, Browserbase's researched web-agent skill catalog, and Single Brain's "compounding data IS the moat," sitting alongside Anthropic's managed-agent platform (Claude Managed Agents PaaS at $2.58 fulfillment cost on $1k of service, Advisor Strategy, Monitor tool) and operational self-improving skill/eval loops (hook-writer 32/50 → 47/50 overnight; ml-intern beating Claude Code on GPQA). Finally, interaction is maturing into its own discipline — conversation-native UX, cognitive debt, the AI Interaction Atlas, leverage on managerial ritual (12-minute Friday reviews, 5-minute 1:1 prep), and the deepest framing of all: intelligence as a social process, where frontier models spontaneously generate "societies of thought" and the path forward runs through composing richer human-AI social systems, not building a single colossal oracle. The canonical Cross-Cutting Patterns synthesis index (Anthropic-as-Platform, three-file configuration, self-improving loops, multi-agent debate, four-layer ecosystem template, MCP-as-survival, job→process, DevOps-mirroring maturity, compounding-data moat, write-path value capture, local-first) is preserved at the foot of Infrastructure.

Sub-Topics

AI AGENTS:

SKILLS &

DISTRIBUTION

32 SRC

KE

AI Agents: Skills & Distribution

32 sources

“The "skills as markdown" pattern is becoming a standard for AI agent extensibility — Corey Haines' marketingskills repo (17.4K stars) packages 36 marketing skills as .md files…”

AI Agents: Infrastructure

AI AGENTS: INFRASTRUCTURE

23 SRC

AI Agents: Infrastructure

23 sources

“Vercel Labs' agent-browser Electron skill lets AI agents control any Electron-based desktop app (Discord, Figma, Notion, VS Code), extending automation from browsers to the full…”

AI Agents: Products

AI AGENTS: PRODUCTS

22 SRC

AI Agents: Products

22 sources

“Dexter is an open-source AI agent that reached 10K GitHub stars, combining OpenClaw and Claude Code to automate financial research workflows: stock screening, financial breakdown,…”

AI Agents: Orchestration

AI AGENTS: ORCHESTRATION

16 SRC

AI Agents: Orchestration

16 sources

“The industry consensus is shifting from wanting a single powerful agent to wanting one coordinator agent that manages teams of sub-agents -- the "1 agent who runs teams of agents"…”

AI Agents: Automation

AI AGENTS: AUTOMATION

9 SRC

AI Agents: Automation

9 sources

“Claude Code subagents running in parallel with scoped tool access is the key capability enabling complex personal automation -- six independent workers holding different contexts…”

AI AGENTS:

INTERACTION

9 SRC

KE

AI Agents: Interaction

9 sources

“Tool UI renders JSON tool outputs as inline, narrated, referenceable surfaces within chat messages -- solving the problem of agent results being dumped as raw text or hidden…”

Guides

Designing a Second Brain for AI Agents: The Vault-as-Database Pattern

How to architect a local knowledge base that AI agents can reason over — starting from Karpathy's three-folder reference architecture, through the three-layer memory stack, MCP bridges, quality maintenance, and the scaling path from flat files to SQLite.

The Social Nature of AI Intelligence: From Societies of Thought to Agent Governance

Why intelligence is inherently social — how reasoning models spontaneously generate internal debates, why the singularity vision is wrong, the human-AI centaur era, and the constitutional design principles for governing billions of interacting agents.

Building Multi-Agent Orchestration Systems: From Single Agents to Coordinated Teams

A practical guide to designing agent coordination systems — covering orchestrator patterns, sub-agent delegation, cost optimization strategies, MCP tool integration, and the memory/self-improvement loops that make agent teams compound over time.

What Is Autoresearch and How to Use It

Autoresearch is Karpathy's hill-climbing optimization loop — make one change, test against a binary checklist, keep or revert, repeat. This guide covers the core method, practical implementation with Claude skills, scaling to parallel GPU clusters and distributed agent swarms, and the emerging pattern of full-stack AI research agents.

Claude & Claude Code for Marketing Agencies: A Detailed Guide

A comprehensive guide covering agency workspace setup, content strategy, competitive intelligence, outbound automation, brand design workflows, and agency-specific Claude Code skills — grounded in 77+ sources.

Voices

107 contributors
Andrej Karpathy

Andrej Karpathy

@karpathy

I like to train large deep neural nets. Previously Director of AI @ Tesla, founding team @ OpenAI, PhD @ Stanford.

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Garry Tan

Garry Tan

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President & CEO @ycombinator —Founder https://t.co/7aoJjp1iIK—designer/engineer who helps founders—SF Dem accelerating the boom loop—haters not allowed in my sauna

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Siqi Chen

Siqi Chen

@blader

🏗️ Love to build (@runwayco @sandboxvr @zynga) people love 💸 Investor @amplitude_hq @mercury @owner @elevenlabsio @meetgamma @sfcompute @turingcom++

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Claude

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Claude is an AI assistant built by @anthropicai to be safe, accurate, and secure. Talk to Claude on https://t.co/ZhTwG8d1e5 or download the app.

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Aakash Gupta

Aakash Gupta

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Ole Lehmann

Ole Lehmann

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I help non-technical people make more money with AI agents. AI connoisseur, robotics maxi, eu/acc supporter, dad, techno optimist

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Charly Wargnier

Charly Wargnier

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Sukh Sroay

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Chris Tate

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Morgan

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Peter Steinberger 🦞

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そう|Claude Codeで始めるAI自動化

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@so_ainsight

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@solirvine

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@soumitrashukla9

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@spiritbuun

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@Teknium

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Rohan Varma

@TheRohanVarma

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