# human review Human review is the process of having people assess AI system outputs or content-moderation decisions, including checking automated decisions and overriding them when appropriate. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Use My /Human-Review Skill to Edit HTML and Markdown Files Visually DevFeed: [Use My /Human-Review Skill to Edit HTML and Markdown Files Visually]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-08-05T14:03:23Z Content type: tutorial Language: en Sources: [Behind the Craft]() Topics: [human review](), [HTML](), [Markdown](), [ai-coding](), [interface]() Tags: [ai-coding](), [cost](), [how-to](), [html](), [human-review](), [interface](), [markdown]() ### AI overview The article introduces /human-review, a free AI skill that opens HTML and Markdown files in a visual editor. Users can edit text and basic formatting, resize images, select content or components for comments, and send grouped feedback to an AI coding tool. ### Source excerpt Tired of giving AI feedback in chat? My new skill lets you edit files, leave comments, and send feedback directly to your agent using a beautiful visual interface ## LangGraph in production: Temporal's LangGraph Plugin adds Durable Execution DevFeed: [LangGraph in production: Temporal's LangGraph Plugin adds Durable Execution]() Original publisher: [Read original article]() Author: Brian Strauch Published: 2026-07-16T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Langgraph](), [Orchestration](), [Agent Framework](), [human review](), [Python](), [telemetry]() Tags: [agent-framework](), [crash-recovery](), [human-review](), [langgraph](), [orchestration](), [product-news](), [python]() ### AI overview Temporal announces a public-preview LangGraph integration for Python, allowing LangGraph agents to run on Temporal with automatic failure recovery, durable execution, human-in-the-loop waits, and support for long-running work. The announcement also covers LangSmith integrations for Python and TypeScript. ### Source excerpt LangGraph agents now run on Temporal: automatic crash recovery, free human-in-the-loop waits, and full observability via new LangSmith integration. ## How Coverwatch uses Temporal to orchestrate AI-powered insurance workflows DevFeed: [How Coverwatch uses Temporal to orchestrate AI-powered insurance workflows]() Original publisher: [Read original article]() Author: Wilmer Yan Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [Artificial Intelligence](), [Automation](), [human review](), [API]() Tags: [agents](), [ai](), [automation](), [community](), [human-review](), [outages](), [temporal](), [workflows]() ### AI overview Coverwatch uses Temporal to coordinate long-running commercial insurance workflows involving AI agents, broker review, carrier communication, and operational follow-up. The platform is designed to preserve workflow state across delays, outages, and external-service issues. ### Source excerpt Coverwatch runs its AI-powered insurance workflows on Temporal, keeping agents, broker review, and carrier submissions durable through outages. ## 6 Deterministic Guardrails to Prevent AI Mistakes in Code DevFeed: [6 Deterministic Guardrails to Prevent AI Mistakes in Code]() Original publisher: [Read original article]() Author: Daniel Moka Published: 2026-05-27T05:01:22Z Content type: tutorial Language: en Sources: [Craft Better Software]() Topics: [Artificial Intelligence](), [Code](), [Testing](), [ci](), [human review]() Tags: [agent](), [ai](), [ai-agents](), [ci](), [code](), [how-to](), [human-review](), [testing]() ### AI overview This tutorial presents six deterministic guardrails for preventing AI-generated code from reaching production with defects. It emphasizes compilers, tests, linters, analyzers, and CI, while reserving human review for intent, tradeoffs, and product decisions. ### Source excerpt 6 Deterministic Guardrails To Prevent AI Mistakes ## Accelerating Delivery: How AI Agents Can Own the Initial Code Draft DevFeed: [Accelerating Delivery: How AI Agents Can Own the Initial Code Draft]() Original publisher: [Read original article]() Author: Stephen Walker Published: 2026-05-05T21:58:00Z Content type: article Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [Artificial Intelligence](), [Loop Engineering](), [Pull Request](), [human review](), [jira](), [coding](), [hooks](), [Script]() Tags: [agents](), [ai](), [ai-agents](), [ai-and-emerging-tech](), [code](), [hooks](), [human-review](), [jira](), [pull-request](), [review](), [sandbox]() ### AI overview The article describes a pragmatic workflow in which coordinated AI agents turn a Jira ticket into a code draft and pull request for human review. It emphasizes safety through branch permissions, scoped access, scripts and hooks, plus deterministic structures around probabilistic language models. The supplied text is truncated before the full reliability hierarchy and later conclusions. ### Source excerpt Why This Matters in the Era of Agentic Everything The gap between a Jira ticket and the first pull request (PR) is often a graveyard of productivity. Context switching, boilerplate setup, and requirement analysis can eat up hours of a senior engineer's day before they write a single line of business logic. Recently, industry leaders have showcased "Harness Engineering" [1][2] -- the practice of building autonomous agent workflows that handle end-to-end coding tasks. While their results are inspiring, the barrier to entry can feel insurmountable. The challenge for most teams is finding a starting point that balances long-term vision with immediate ROI. We recently helped a large-scale, high-traffic consumer services platform bridge this gap. Instead of an all-or-nothing architectural overhaul, we took a pragmatic first step: automating the journey from Jira ticket to PR. The Vision: "Let's work on FEAT-123" Our goal was simple. We wanted to enable a developer to provide a single prompt -- "Let's work on FEAT-123" -- and have a coordinated team of AI agents handle the planning, coding, and verification. This culminates in a PR ready for human review. To make this a reality, we focused on three strategic pillars: safety, determinism, and quality. Safety Through Pragmatic Guardrails One of the primary concerns for any engineering leader is "agent drift" -- an AI making unauthorized or hallucinated changes. While an isolated digital sandbox is an ideal long-term goal, we proved that teams can achieve significant safety and progress using existing infrastructure: Branch Permissions: Restrict agent access to specific feature branches. Scoped Access: Use scripts and hooks to define exactly what an agent can and cannot touch. Human-in-the-Loop: Ensure that while the agent proposes the change, a human must always review, approve, and merge the code. By defining the agent's scope as a specialized contributor rather than a system administrator, we mitigated risk while maximizing ou ## How XY builds an AI agent orchestration platform for healthcare with Temporal DevFeed: [How XY builds an AI agent orchestration platform for healthcare with Temporal]() Original publisher: [Read original article]() Author: The XY Engineering Team Published: 2026-04-23T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [agent orchestration](), [AI Agent](), [Orchestration](), [reliability](), [human review](), [sensitive data](), [Structured-data](), [YAML]() Tags: [agent](), [agent-orchestration](), [ai](), [ai-agent](), [automation](), [how-to](), [human-review](), [long-running](), [orchestration](), [reliability](), [sensitive-data](), [temporal](), [workflow]() ### AI overview XY describes using Temporal as a DSL-driven execution engine for an AI agent orchestration platform serving complex healthcare workflows. The approach uses a YAML-based workflow language and a generic Temporal workflow class to coordinate multiple systems, reliability features, human review, and sensitive healthcare data from prototype to production. ### Source excerpt One generic Temporal workflow class can provide infinite healthcare automation. See how XY built a DSL-driven AI agent orchestration platform that scales from prototype to production. ## AI coding tools can increase code output without improving delivery outcomes DevFeed: [AI coding tools can increase code output without improving delivery outcomes]() Original publisher: [Read original article]() Author: Brian Jenney Published: 2026-04-11T16:09:44Z Content type: opinion Language: en Sources: [Brian Jenney]() Topics: [Artificial Intelligence](), [ai-coding](), [human review](), [Code review](), [Testing](), [telemetry](), [Claude](), [cursor](), [React]() Tags: [ai](), [ai-coding](), [claude](), [code-review](), [cursor](), [engineering](), [human-review](), [pull-requests](), [telemetry](), [testing]() ### AI overview The author argues that AI coding tools may increase code output and task completion without improving delivery velocity or business outcomes. The article combines industry reports with personal experience, describing longer code review and debugging times, higher bug rates, and cleanup work after rushed AI-assisted development. ### Source excerpt I've flip-flopped on this issue more times than a politician up for re-election. ## Apache Airflow 3.1.0: Human-Centered Workflows DevFeed: [Apache Airflow 3.1.0: Human-Centered Workflows]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2025-09-25T00:00:00Z Content type: release Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [Automation](), [human review](), [Data Quality](), [Forms](), [Developer experience](), [Inference](), [React]() Tags: [apache-airflow](), [automation](), [data-quality](), [developer-experience](), [forms](), [human-review](), [inference](), [release](), [release-notes](), [ui]() ### AI overview Apache Airflow 3.1.0 introduces human-in-the-loop tasks for pausing automated workflows and collecting reviews through web forms. The release also adds internationalization support, developer experience improvements, restored Calendar and Gantt views, UI filtering, DAG pinning, and accessibility-focused theme updates. ### Source excerpt We are thrilled to announce the release of Apache Airflow 3.1.0, an update that puts humans at the center of data workflows. This release introduces powerful new capabilities for human decision-making in automated processes, comprehensive internationalization support, and significant developer experience improvements. Details: ðŸ“Ķ PyPI: https://pypi.org/project/apache-airflow/3.1.0/ 📚 Core Airflow Docs: https://airflow.apache.org/docs/apache-airflow/3.1.0/ 📚 Task SDK Docs: https://airflow.apache.org/docs/task-sdk/1.1.0/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.1.0/release_notes.html ðŸŠķ Sources: https://airflow.apache.org/docs/apache-airflow/3.1.0/installation/installing-from-sources.html 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.1.0 ðŸĪ Human-in-the-Loop (HITL): When Automation Meets Human Judgment This powerful capability bridges the gap between automated processes and human expertise, making Airflow invaluable for: AI/ML Model Validation: Pause inference pipelines for human review of model outputs Content Moderation: Route content through human reviewers before publication Approval Workflows: Require manager approval for sensitive operations Data Quality Gates: Allow data stewards to validate critical datasets HITL tasks pause in a deferred state while presenting intuitive web forms in the Airflow UI. Users with appropriate roles can review context data, DAG parameters, and XCom values before making informed decisions. Example Code: from airflow.sdk import DAG from airflow.providers.standard.operators.hitl import HITLOperator with DAG("content_moderation", schedule="@daily") as dag: moderate_content = HITLOperator( task_id="review_content", message="Please review this content for publication", data_key="content_to_review" ) 📊 UI Enhancements & Performance Calendar and Gantt Views Make Their Comeback Remember those beloved Calendar and Gantt chart views from Airflow 2.x? They're back, completely rebuilt for the modern ## How Windsurf writes docs DevFeed: [How Windsurf writes docs]() Original publisher: [Read original article]() Author: Tiffany Chen Published: 2025-05-27T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [product analytics](), [ai-coding](), [human review]() Tags: [agent](), [ai-coding](), [ai-trends](), [documentation](), [human-review](), [product-analytics]() ### AI overview Windsurf treats documentation as a core product component, with marketing leading the process and shipping docs alongside every release. The team uses product analytics to monitor adoption and guide improvements, focuses pages on user value, and applies AI tools with human review to maintain consistency and quality. ### Source excerpt At Windsurf, a leading player in the AI coding space, documentation is integrated in how they build, ship, and market their product.