The Langhui Technology Agency AI Smart Data Service, Focus on Gold Track Collection, Enhanced Learning Environment Design, Failed Model Classification and SWE-Bench In-depth Assessment provides full support to autonomous smarts, such as browser automation, code development, multiple Agent collaboration, from training data to benchmarking, and helps accelerate the fall of 2026 intelligent bodies in the year.
Following the Agent research threads of global forward laboratories, including OpenAI, Anthropic, Google DeepMind, the Longway expert team continues to transform forward smart breakthroughs into high-quality training and assessment data for scalable production.
2026 Browser Operation Agent Commercial Breakout. Operator performs real tasks like booking, shopping, form filling, etc. by visual understanding + DOM. Long provided high-quality manual trajectories and failure case data matching them.
Claude computers use the Agent open power upgrade. By blocking the screen recognition + mouse keyboard action sequence, you control the complete desktop environment, and Langhui builds multi-application synothesizing track collections and security guard data.
Google Mariner is an autonomous viewer of Agent, which integrates Gemini long-range reasoning to achieve 90%+ completion in complex multipage tasks. Long-distance tasks across sites and decision point labeling data are provided by Long-Range.
The OpenAgent framework is now 2.0 age. LangGraph 2.0 introduces state machines + lasting memory + humans + humans & circuits, AutoGPT achieves a truly commercial autonomous mission planning, and Long Wai provides accompanying multistep planning and tools to access training data.
Software engineering Agent evaluation benchmark upgrades. SWE-bench Verified filters high quality GitHub Issue by manual verification, and Langhui provides in-depth assessment services and extended task sets, covering code repair, functional realization, testing of all-in-house capability.
The multi-intelligence collaboration system is a new paradigm. Frameworks such as AutoGen, Crewai, MetaGPT support role segregation, messaging and sharing of memories, and Langhui provides multiple Agent collaboration tracks, communication protocol data and conflict resolution markers.
Four core competencies cover the full life cycle of Agenic AI data, from gold track collection to failure model analysis, and build quantifiable, auditable, and traceable intelligent data flow lines.
Field experts operate the real environment and record the complete gold trajectory, covering the browser operation, desktop application, code development, API call etc., with each step-by-step cut-off, DOM snapshot, operating intent, mouse keyboard event full dimension data.
Create a complex mission environment for Agent training, including sandbox browsers, virtual desktops, code-executing sandboxes, API simulators, etc., to support diversity RL scenarios such as multistep missions, dilute incentives, and counterturbation.
Systematize and attribute the defaults of Agent, covering planning failures, tool misuse, loss of status, hallucinations, loop card death, etc., 8 major categories, 32 subcategories fail.
Based on the SWE-bench Verified and the Self-Research Extension Benchmark, the software project Agent is evaluated in depth from end to end, covering problem understanding, code positioning, patch generation, testing and validation of the whole process.
The six standard Agent data set product is then available to cover web operations, code generation, tool call, multistep reasoning, failure cases, multiple Agent collaborations, and support for subscription and customized dual mode delivery.
The manual trajectories that cover real web tasks such as electrical powerer's tabs, information queries, form filling, content creation, etc., contain screenshot sequences, Dom snapshots, mouse keyboard events, operational intent labels.
Developer understands the full development trajectory from demand to code submission, including thought reasoning, file editing, terminal commands, debugging process, test validation, aligning the SWE-bench job paradigm.
The Function Calling and MCP tools call for training data, overwrite API selection, parameter filling, error processing, results resolution, chain call etc., and support OpenAI/ Anthropic Tool specifications.
Multistep planning and reasoning tracks for complex missions, including task decomposition, sub-targeting, status tracking, retroactive corrections, covering the reasoning paradigms of Rect, Reflexion, Tree-of-Thought.
Agent's failure casebook, which contains the failure type label, the failure point location, root analysis, the repair proposal, the coverage of the planning failure, the misuse of tools, the recycling card death, etc., for the eight main categories of failure.
Multi-intelligence collaboration trajectory data, including role segregation, task assignment, messaging, shared memory, conflict resolution, and coverage of mainstream collaboration frameworks such as AutoGen, Crewai, MetaGPT.
Automation of browsers to code development, from enterprise work to multiple Agent collaboration, and adaptation of the LANG Ageic AI data service to the needs of the autonomous SSI-wide landscape.
Provides a real web-based trajector, Mariner type browser Agent with a live trajector and failure case, covering high frequency stations such as electricians, travelers, politicians, SaaS, and the enabling model achieves 95% + mission completion.
Provides SWE Agent with a complete development trajectory from Issue to PR submissions and SWE-bench assessment services, covering Bug repair, functionality realization, re-engineering, testing to develop full- house software engineering capabilities.
The Automation of Enterprise Offices Agent provides multiple system synergetic trajectories and tools such as approval, statements, mail, calendar, CRM, etc., to leverage data and help Agent to get down to real business processes.
The multi-intelligence collaboration system is supported by collaborative data such as role segregation, task assignment, messaging, conflict arbitration, and support for mainstream Agent framework training and evaluation, such as AutoGen, CrewAI, MetaGPT.
Langhui has built a quality assurance system that runs through the entire process of Agent data production, from trajectories integrity to environmental authenticity, assessment coverage, failure pattern recognition, with quantifiable indicators for each dimension.
With the Luang Hui team of experts, you can inject the 2026 Agent research results into your intelligence. Whether you are a browser, a code, or a team of Agent, we can tailor the entire house from training to benchmarking.