Show HN: 서로의 실수로부터 배우는 AI 에이전트

hackernews | | 📦 오픈소스
#ai 딜 #ai agent #ai api #anthropic #claude #에러 복구 #에이전트 진화 #자율 학습
원문 출처: hackernews · Genesis Park에서 요약 및 분석

요약

AI 에이전트들이 서로의 실수와 성공 경험을 공유하여 학습할 수 있도록 돕는 'Prismer' 인프라 플랫폼이 소개되었습니다. 이 시스템은 계층적 베이지안 모델과 톰슨 샘플링 알고리즘을 활용해 에러 발생 시 최적의 해결 전략을 추천하며, 한 에이전트의 문제 해결 경험이 267ms 만에 전체 네트워크로 전파되어 모든 에이전트의 정확도를 높이는 네트워크 효과를 제공합니다. 사용자는 클로드, 커서 등 다양한 개발 환경에 MCP 서버나 플러그인 형태로 이를 설치할 수 있으며, 문맥 유지, 메모리 동기화, 에이전트 간 메시징 등 장기 구동에 필수적인 통합 기능을 활용할 수 있습니다.

본문

The Harness for AI Agent Evolution Your agent learns from every session. Errors become strategies, fixes become recommendations — shared across all agents. Get API Key · Docs · Live Evolution Map · Discord Long-running agents fail without infrastructure. Anthropic's research identifies the core requirements: reliable context, error recovery, persistent memory, and cross-session learning. Most teams build these ad hoc. Prismer provides them as a single, integrated layer. | Evolution | Context | Memory | Tasks | Messaging | Security | The future agent & model should be plugin , agent workspace info & data should follow human not agent. Read & reference: https://prismer.cloud/docs/Skill.md — install it as a skill for your. # Install CLI npm i -g @prismer/sdk # recommended prismer setup # Browser sign-in → key auto-saved with 1000 free credits prismer setup --agent # No browser, agent auto-register it'self with 100 free credits prismer setup # Or provide a key directly prismer status # Verify: shows key, credits, connection This saves your key to ~/.prismer/config.toml — all plugins and SDKs read from it automatically. # In Claude Code console: /plugin marketplace add Prismer-AI/PrismerCloud /plugin install prismer@prismer-cloud /reload-plugins On first session, the plugin will guide you through setup: /prismer:prismer-setup # Auto opens browser for one-click sign-in (zero copy-paste, 30 seconds) 9 hooks run automatically — errors detected, strategies matched, outcomes recorded, web content cached, memory synced. # Claude Code — one command claude mcp add prismer -- npx -y @prismer/mcp-server For Cursor / Windsurf, add to .cursor/mcp.json (or .windsurf/mcp.json ): { "mcpServers": { "prismer": { "command": "npx", "args": ["-y", "@prismer/mcp-server"], "env": { "PRISMER_API_KEY": "sk-prismer-xxx" } } } } Reload the IDE — 29 tools available immediately (evolve_* , memory_* , context_* , skill_* ). No API key yet? Run npx @prismer/sdk setup first. | Agent Integrations | Install | What it does | | Claude Code Plugin | /plugin marketplace add Prismer-AI/PrismerCloud | 9-hook auto-evolution, context cache, memory sync, skill sync | | MCP Server | npx -y @prismer/mcp-server | 33 tools for Claude Code / Cursor / Windsurf | | OpenCode Plugin | opencode plugins install @prismer/opencode-plugin | Evolution hooks for OpenCode | | OpenClaw Channel | openclaw plugins install @prismer/openclaw-channel | IM channel + 14 agent tools | | SDKs | Install | | TypeScript / JavaScript | npm i @prismer/sdk | | Python | pip install prismer | | Go | go get github.com/Prismer-AI/PrismerCloud/sdk/prismer-cloud/golang | | Rust | cargo add prismer-sdk | The evolution layer uses Thompson Sampling with Hierarchical Bayesian priors to select the best strategy for any error signal. Each outcome feeds back into the model — the more agents use it, the smarter every recommendation becomes. Agent A hits error:timeout → Prismer suggests "exponential backoff" (confidence: 0.85) Agent A applies fix, succeeds → outcome recorded, gene score bumped Agent B hits error:timeout → same fix, now confidence: 0.91 Network effect: every agent's success improves every other agent's accuracy How it works: - Signal detection — 13 error patterns classified from tool output (build failures, TypeScript errors, timeouts, etc.) - Gene matching — Three-layer scoring: exact tag match → category prefix match → semantic similarity - Thompson Sampling — Beta posterior sampling with hierarchical pooling (local agent data + global cross-agent prior) - Outcome recording — Success/failure updates edge counts, quality-gated to prevent spam - Person-Level Sync — All agent instances of the same user share genes (digital twin foundation) Key properties: - Sub-millisecond local — cached genes require no network - 267ms propagation — one agent learns, all agents benefit - Cold-start covered — 50 seed genes for common error patterns - Convergence — ranking stability (Kendall tau) reaches 0.917 in benchmarks | Capability | API | What it does | |---|---|---| | Evolution | Evolution API | Gene CRUD, analyze, record, distill, cross-agent sync, skill export | | Context | Context API | Load, search, and cache web content — compressed for LLM context windows (HQCC) | | Parsing | Parse API | Extract structured markdown from PDFs and images (fast + hires OCR modes) | | Messaging | IM Server | Agent-to-agent messaging, groups, conversations, WebSocket + SSE real-time delivery | | Memory | Memory Layer | Working memory (compaction) + episodic memory (persistent files) | | Orchestration | Task API | Cloud task store with cron/interval scheduling, retry, exponential backoff | | Security | E2E Encryption | Ed25519 identity keys, ECDH key exchange, per-conversation signing policies | | Skills | Skill Catalog | Browse, install, and sync reusable agent skills from the evolution network | More in sdk page Step-by-step tutorials with TypeScript, Python, and curl examples. | # | Tutorial | Time | What you'

Genesis Park 편집팀이 AI를 활용하여 작성한 분석입니다. 원문은 출처 링크를 통해 확인할 수 있습니다.

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