Results for “human-in-the-loop”

Designs human-in-the-loop review points for DAG workflows. Determines what to present to the human, how to collect feedback, and how to route approve/reject/modify decisions back into the DAG. Use when adding approval gates, designing review UX, or handling human feedback in agent workflows. Activate on "human review", "approval gate", "human-in-the-loop", "human gate", "approval workflow", "user review step". NOT for executing human gates at runtime (use dag-runtime with Temporal signals), general UX design, or chatbot conversation design.

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INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...), approval/validation workflows, and the 4-tier error handling strategy.

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You are an expert in Cline, the autonomous AI coding agent for VS Code that can read files, write code, run terminal commands, and use the browser — with human-in-the-loop approval at each step. You help developers use Cline for complex multi-file refactoring, feature implementation, debugging, and codebase exploration where the AI plans and executes while the developer reviews and approves.

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Expert guide for AI-assisted coding workflows — agentic code generation, multi-agent code swarms, self-healing CI/CD, automated PR review, spec-to-code pipelines, codebase knowledge graphs, and human-in-the-loop approval gates / Panduan ahli untuk workflow pengkodean berbasis AI — generasi kode agentic, code swarm multi-agen, CI/CD self-healing, review PR otomatis, pipeline spec-to-code, knowledge graph codebase, dan gate persetujuan human-in-the-loop.

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Use when designing human approval gates for high-stakes agent actions. Keywords: human-in-the-loop, HITL, approval gate, checkpoint, confirmation, risk-tiered, action review.

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Find professionals with relevant experience and request a written or voice answer, or a short call, through Instant Expert. Use it for customer discovery and practical advice, with a free simulated test mode to try the process.

• Gathering firsthand experience from practitioners
• Interviewing potential customers
• Requesting a short call with a named professional

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.

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

Set up human approval for Claude Code actions such as PR reviews, merges, and CI changes. Configure Cedar authorization rules and signed records of allowed actions that can be checked offline with a public key.

• Requiring approval before PR reviews and merges
• Gating issue actions and release publishing
• Requiring approval for CI changes and protected branch pushes

Build web apps connecting a CopilotKit React interface to agents hosted in Microsoft Foundry. Covers AG-UI, the communication protocol between them, plus tool approvals, conversation history, shared state, deployment, debugging, and dependency upgrades.

• Build a chat interface for a Foundry hosted agent
• Add approval screens for agent tool execution
• Investigate failures across the agent and web interface

Helps integrate CopilotKit into an app by consulting current documentation and source code. Covers chat interfaces, agent connections, shared state, and troubleshooting, with guidance on checking configuration and avoiding incompatible API versions.

• Adding a CopilotKit chat interface to an app
• Connecting an agent framework
• Diagnosing runtime connection problems

Build AI agents with LangGraph by connecting processing steps into a graph. Define shared state, conditional paths, tool calls, saved checkpoints, and human review points to create workflows that retain information as they run.

• Building an agent that calls tools in a loop
• Sharing accumulated findings between multiple agents
• Routing requests to different processing paths

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.

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

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Classify agent work as ready to continue, needing human judgment, or intentionally paused. Structure decision requests with evidence, a recommended choice, and clear outcomes, then connect the recorded decision to the next responsible agent.

• Deciding whether agent work needs human review
• Preparing decision requests with evidence and recommended choices
• Designing a dashboard for pending human decisions

Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.

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Darwin Skill 2.0 (达尔文.skill 2.0): autonomous skill optimizer, v2.0 integrates Microsoft Research SkillLens (arXiv 2605.23899) 9-dim rubric + SkillOpt (arXiv 2605.23904) validation-gated design + human-in-the-loop checkpoints. Evaluates SKILL.md files using a 9-dimension rubric (structure + effectiveness + meta-skill blacklists), runs hill-climbing with git version control, spawns independent judge agents for blind evaluation, validates improvements through test prompts with auto-break on diminishing returns, and generates visual result cards. Use when user mentions "优化skill", "skill评分", "自动优化", "auto optimize", "skill质量检查", "达尔文", "darwin", "帮我改改skill", "skill怎么样", "提升skill质量", "skill review", "skill打分".

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Orchestrates the complete talk preparation pipeline from raw material to revision sheets, running 6 stages in sequence with human-in-the-loop checkpoints for REX or Concept mode talks. Use when starting a new talk pipeline, resuming a pipeline from a specific stage, or running the full end-to-end preparation workflow.

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Diverga Dashboard - Live configuration status and feature overview. 24 specialized agents across 9 categories for social science research. VS methodology prevents mode collapse. Human checkpoints enforce human-in-the-loop decisions. Triggers: /diverga, diverga dashboard, diverga status

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Design and generate .envelope.json AI agent team definitions — the open standard for multi-agent teams with hierarchy, access policies, human-in-the-loop gates, and cron schedules.

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Builds resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like lambda durable functions, durable execution, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, reliable serverless applications, context.step, context.wait, context.invoke, context.runInChildContext, withDurableExecution, DurableContext, UnrecoverableInvocationError, durable-execution-sdk, qualified ARN invocation, and durable handler replay.

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Human-in-the-loop integration for LangGraph workflows with approval and intervention points

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Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where the agent can fail (bad input, hallucination, wrong tool call, edge cases, silent errors), the checks that catch each (validation, evals on real cases, human-in-the-loop gates, monitoring), a right-sized reliability plan scaled to the stakes, and a rollout that earns trust incrementally.

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INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of dangerous tool calls, creating custom middleware with hooks, Command resume patterns, and structured output with Pydantic/Zod.

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Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.

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