'autonomous' 검색 결과

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth. Use when "autonomous agent, autogpt, babyagi, self-prompting, goal decomposition, react pattern, agent loop, self-correcting agent, reflection agent, langgraph, agentic ai, agent planning, autonomous, agents, langgraph, react, planning, reflection, guardrails, reliability, checkpointing" mentioned.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

🕹️ Autonomous Agent Patterns workflow skill. Use this skill when the user needs Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Autonomous Agents workflow skill. Use this skill when the user needs Autonomous agents are AI systems that can independently decompose and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

🕹️ Autonomous Agent Patterns workflow skill. Use this skill when the user needs Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Autonomous Agents workflow skill. Use this skill when the user needs Autonomous agents are AI systems that can independently decompose and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Claude Code에서 개발 작업을 반복하는 자율 루프 설계를 안내합니다. 순차 실행부터 병렬 작업까지 진행 상황 인계, 품질 확인, 변경 사항 통합과 중단 조건을 정리합니다.

• 순차적으로 실행할 개발 절차를 설계할 때
• 반복 실행 사이에 진행 상황을 인계할 때
• 병렬 작업과 변경 사항 통합을 조율할 때

Design autonomous coding agents with patterns inspired by Cline and OpenAI Codex. Explore tool calling interfaces, permission and approval systems, browser automation, and workflows where people review or guide the agent's actions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning an autonomous coding agent
• Designing tool and function calling interfaces
• Defining permissions and approval steps

Improve code through repeated experiments against a measurable target, such as execution time or memory use. After defining scope and constraints, the agent tests changes, keeps improvements, reverts unsuccessful attempts, and logs results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reduce execution time through measured code experiments
• Optimize memory use or bundle size
• Compare changes using benchmark results

Design autonomous AI agents that break goals into tasks, use tools, and review their own work. Covers reasoning and action loops, separate planning and execution, and reliability measures such as cost limits, validation, and logging.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing how an agent plans and executes tasks
• Breaking goals into constrained steps
• Adding self-review and correction to an agent

Design autonomous coding agents using implementation patterns for tool calls, permission checks, browser interaction, and saved task state. Code examples and checklists help structure agent behavior and workflows that include human approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a coding agent's execution loop
• Defining tool interfaces for files and commands
• Adding permissions and human approval steps

自动驾驶 (Autonomous Driving) Master OS — automated mastery of Autonomous Driving: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on Autonomous Driving problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「自动驾驶」「智能驾驶」「智驾」「辅助驾驶」「领航辅助」

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

AI 에이전트의 위험한 명령을 실행 전에 확인하고 파일 편집을 지정 디렉터리로 제한합니다. 보호 모드를 따로 쓰거나 결합해 운영 작업과 자율 실행을 관리합니다.

• 운영 작업 중 위험한 명령 확인
• 특정 디렉터리로 편집 범위 제한
• 자율 실행 시 보호 모드 결합

Claude Code의 기억, 정기 실행, 작업 대기열을 결합해 지속적인 작업을 설계합니다. 외부 실행 환경과 화면 조작 연동 설정도 안내합니다.

• 풀 리퀘스트를 정기적으로 확인할 때
• 매일 조사 결과를 모아 요약할 때
• 기억한 맥락으로 회의를 준비할 때

Turns supplied AI agent evaluation results into reports that distinguish autonomous success, human-assisted success, failure, timeout, and invalid runs. Makes metric denominators, retries, timing, costs, and comparison conditions explicit to support evidence-based decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Separating autonomous and human-assisted success rates
• Reporting evaluations with retries and invalid runs
• Comparing agents or releases under matched conditions

Build and operate AutoGPT agents that carry out multi-step workflows. Covers a visual editor for connecting processing blocks, scheduled and webhook triggers, deployment, execution monitoring, and benchmarks for evaluating agent performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an AI workflow in a visual editor
• Run agents on a schedule or external event
• Add custom agent abilities with Forge

Design the controls around autonomous agents: what they can edit, how results are evaluated, how progress is logged, and when human approval is required. Build research and experiment loops with clear recovery and review rules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing autonomous research and experiment loops
• Separating editable work from fixed evaluation rules
• Preserving progress and failed attempts across long runs

Write and review launch prompts for agents working autonomously for hours or days. Define successful deliverables, excluded partial results, stopping rules, worker handoffs, and evidence-based review requirements so completion can be assessed against explicit criteria.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning vague requests into precise task briefs
• Defining stopping rules for long autonomous runs
• Specifying deliverables for parallel workers

Build AI agents by connecting workflow blocks in AutoGPT's visual editor. This skill covers multi-step automation, scheduled runs, webhooks that start work after external events, execution monitoring, and custom agent development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an AI workflow in a visual editor
• Start an agent when an external event occurs
• Run a multi-step workflow on a schedule

Coordinate AI research from literature review and hypothesis testing to findings and paper drafts. The skill alternates focused experiments with broader reflection, maintains project records, and directs specialized skills to carry out research tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a research question into testable hypotheses
• Managing repeated experiments across multiple hypotheses
• Resuming research from recorded findings and project state

Drafts a token budget extension request when an autonomous loop approaches its daily limit and high priority work remains. Uses logged results and remaining issues to justify the request, then pauses until a human increases the budget.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Requesting extra budget for remaining critical work
• Summarizing daily token spending and successful actions
• Preparing a budget request for a human maintainer

Guide an AI agent through research, implementation, and verification with fewer interruptions. The skill emphasizes evidence, concise reporting, and saved progress notes, with three working modes that adjust how closely the user follows the work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Delegating engineering tasks through verification
• Reducing interruptions during focused work
• Resuming long tasks from saved progress notes

Build AI agents that perform Solana operations such as transfers, token creation, NFT minting, and asset swaps. Provides plugin setup, LangChain and Vercel AI SDK integration, Claude Desktop connectivity through MCP, and autonomous execution patterns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an agent for Solana transfers and balance checks
• Add token creation and NFT minting tools
• Connect Solana operations to Claude Desktop through MCP

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.