'autonomous' 검색 결과

Author and run unattended autonomous workflows — "workflow", "unattended", "run on a schedule", "automate this overnight", "set up an autonomous", "dry-run". Mandatory budget guards, a separate verification gate, idempotency, and dry-run on by default.

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Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool APIs, implementing permission systems, or creating autonomous coding assistants.

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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% b

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지갑이나 거래 실행 권한을 가진 AI 에이전트의 안전 대책을 설계하고 점검합니다. 외부 입력 방어, 지출 한도, 전송 전 검증, 중단 조건, 개인 키 관리를 다룹니다.

• 거래 권한을 가진 봇을 감사할 때
• 거래별·일별 지출 한도를 설계할 때
• 전송 전 검증과 자동 중단을 설계할 때

AI 에이전트의 반복 작업을 설계·점검합니다. 기계로 판정할 완료 조건, 변경 금지 경계, 독립 검증과 재시도 상한을 정해 헛돌거나 통제를 벗어나는 일을 방지합니다.

• 반복 작업의 완료 조건 정의
• 헛도는 반복과 검증 우회 점검
• 계획·구현·검증의 역할 분담

앱 개발을 계획·구현·평가 에이전트로 나누고, 실행 중인 앱의 화면과 기능을 검증하며 설정한 품질 기준이나 반복 횟수 상한까지 수정을 이어 갑니다.

• 짧은 아이디어로 앱 명세를 만듭니다
• 별도 평가를 통해 화면을 개선합니다
• 풀스택 앱의 동작을 검증하고 수정합니다

Design and build AI agents for business, research, operations, or creative work. Define essential actions and knowledge, start with a simple execution loop, and add progress tracking or separate child agents as needs emerge.

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• Designing an assistant for business tasks
• Building a research agent
• Choosing essential actions and knowledge

Run one cycle of autonomous task work in Claude Code: check progress, select and execute the next action, log the work, and schedule another cycle. Stop when all tasks are complete or the iteration limit is reached.

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• Continuing work from a Claude Code task list
• Working through Markdown checklist items
• Running tasks managed through MCP

Design the operating layer around a coding agent so it can read and edit code, run checks, and correct errors. Define completion criteria, permission boundaries, feedback, and recovery methods, and assess which tasks suit autonomous execution.

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• Designing a coding agent's tools and workflow
• Adding acceptance checks and permission boundaries
• Assessing tasks for autonomous agent execution

Design AI agents that break down goals, plan actions, use tools, and correct their own mistakes. The guidance focuses on reliable execution, clear operating boundaries, and managing conversation context as tasks grow longer.

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• Designing an agent's planning and execution loop
• Breaking a complex goal into manageable tasks
• Reviewing self-correction and reliability patterns

Plan and build AI agents through selected stages of design, implementation, tool integration, memory, and evaluation. The workflow points to specialized skills for single agents, coordinated agent teams, and stateful workflows using CrewAI or LangGraph.

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• Designing an autonomous AI agent
• Building a team of agents with distinct roles
• Creating stateful workflows with LangGraph

Find autonomous AI agents, frameworks, and tools through AgentFolio. Compare their capabilities, intended users, integrations, and deployment options to shortlist existing products or gather examples and ideas before designing an agent of your own.

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• Finding existing agents before building your own
• Shortlisting agent vendors for evaluation
• Researching competitors and market gaps

Design autonomous agents for Claude Code plugins, including when they should run, what they should do, and which tools they may use. Provides file templates and guidance for validating configuration and testing behavior.

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• Create a code review agent
• Clarify when an agent should run
• Limit the tools an agent can access

Design autonomous agents for Claude Code plugins, including when they should run, what tools they may use, and how they should respond. Provides configuration templates, prompt guidance, and validation steps for creating agent files.

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• Create a code review agent
• Refine examples that trigger an agent
• Limit the tools an agent can use

Build collaborating AI teams with CrewAI by defining roles, tasks, and shared context. Configure ordered execution or manager-led delegation, add memory and tools, and create workflows that branch based on events or results.

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• Build a research-to-writing agent workflow
• Coordinate specialists through a manager agent
• Create workflows with conditional branches

Design AI agents that plan tasks, call tools, and retain useful information. Covers coordination between agents, evaluation, and debugging, with practical patterns for execution limits, error handling, and tracing what an agent does.

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• Designing an agent that plans and executes tasks
• Organizing tool definitions and selection
• Deciding what an agent should remember

Plan and improve AI agent systems by managing the information models receive. This collection covers memory, agent coordination, tool design, context compression, evaluation, and the operating controls needed for reliable autonomous workflows.

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• Planning information management for a new AI agent
• Investigating failures in long agent conversations
• Designing agent memory and multi-agent coordination

Improve research code, results, and paper drafts through repeated review by a second Codex agent. The workflow implements fixes, checks them again, and records assessments and unresolved issues across up to four rounds.

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• Improve research before submitting a paper
• Repeat fixes and reviews of experimental results
• Track unresolved concerns across review rounds

Iteratively improve research by having Claude Code review code, results, and drafts while Codex implements fixes. Repeat until the assessment meets the stopping criteria or the round limit is reached, preserving feedback and unresolved issues.

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• Repeating research reviews and fixes before submission
• Checking whether claims match code and experiment results
• Tracking how reviewer concerns are addressed across rounds

Improve research through repeated Gemini reviews, code changes, experiments, and analysis. The workflow records full feedback and actions, then stops when both the score and readiness criteria are met or the round limit is reached.

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• Improving research through repeated external reviews
• Implementing and checking fixes for research weaknesses
• Keeping a complete record of reviews and experiments

Populate a running CARLA simulation with autonomous traffic, evenly spaced vehicles in one lane, or a single vehicle for control and sensors. Filter vehicle models, keep automated driving active, and remove vehicles when the scene is finished.

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• Fill a map with autonomous traffic
• Place vehicles at fixed intervals in one lane
• Create an ego vehicle for control and sensors

Creates a Python agent skeleton for CARLA Leaderboard and checks its sensor configuration offline against the installed version’s rules. Covers version-specific initialization, driving logic, and ROS1 or ROS2 agent integration.

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• Creating an agent with a front camera and lidar
• Checking sensor limits and configuration errors
• Porting a Leaderboard 1.0 agent to 2.1

Add pedestrians to a CARLA map and let them walk autonomously. Adjust crowd size, walking speed, road-crossing frequency, and placement seeds, create stationary crowds, and remove pedestrians together with their movement controllers in the correct order.

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• Adding pedestrians who walk around a map
• Reusing pedestrian placement with a fixed seed
• Adjusting walking speeds and road crossings

Build teams of AI agents with CrewAI, assigning roles such as researcher, writer, and analyst. Guidance covers passing results between tasks, running work in sequence or under a manager, adding memory, and creating workflows with conditional branches.

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• Connecting research, writing, and editing agents
• Coordinating specialist agents through a manager
• Branching workflows based on analysis results