Langhui Technology's Agentic AI data services focus on golden-trajectory collection, reinforcement-learning environment design, failure-mode taxonomy, and SWE-bench deep evaluation. From training data to evaluation benchmarks, we deliver full-stack support for autonomous agents across browser automation, software engineering, and multi-agent collaboration — accelerating the rollout of the 2026 agent era.
Tracking the agent research roadmaps of OpenAI, Anthropic, Google DeepMind, and other leading global labs, Langhui's expert team continuously translates the latest agent breakthroughs into high-quality, production-ready training and evaluation data at scale.
The 2026 commercial breakthrough in browser-operation agents. Operator pairs visual understanding with DOM parsing to drive browsers through real tasks like booking, shopping, and form completion. Langhui supplies aligned high-quality human-operation trajectories and failure-case data.
Claude's Computer Use agent capabilities received a major upgrade. Driven by screenshot perception plus mouse-and-keyboard action sequences, it controls full desktop environments. Langhui builds multi-application collaboration trajectory sets and safety guardrail data.
Google Mariner is an autonomous web-browsing agent that integrates Gemini's long-horizon reasoning, achieving 90%+ completion rates on complex multi-page tasks. Langhui supplies cross-site long-horizon task trajectories and decision-point annotations.
Open-source agent frameworks enter the 2.0 era. LangGraph 2.0 introduces state machines, persistent memory, and human-in-the-loop workflows, while AutoGPT delivers truly commercial-grade autonomous task planning. Langhui supplies aligned multi-step planning and tool-calling training data.
The benchmark for software-engineering agents levels up. SWE-bench Verified uses human-verified, high-quality GitHub issues. Langhui provides deep evaluation services and extended task sets covering the full stack — bug fixing, feature implementation, and test authoring.
Multi-agent collaboration has become the new paradigm. Frameworks such as AutoGen, CrewAI, and MetaGPT support role specialization, message passing, and shared memory. Langhui provides multi-agent collaboration trajectories, communication-protocol data, and conflict-resolution annotations.
Four core capabilities span the entire Agentic AI data lifecycle — from golden-trajectory collection to failure-mode analysis — building an agent data pipeline that is production-ready, auditable, and fully traceable.
Domain experts operate real environments while we record complete golden trajectories across browser operations, desktop applications, software engineering, and API calls — capturing every screenshot, DOM snapshot, action intent, and mouse-and-keyboard event in full dimensionality.
We build complex task environments for agent training — including sandboxed browsers, virtual desktops, code-execution sandboxes, and API simulators — supporting multi-step tasks, sparse rewards, adversarial perturbations, and a diverse range of RL scenarios.
We systematically classify and attribute agent execution failures across 8 major categories and 32 sub-types — covering planning failures, tool misuse, state loss, hallucinated operations, and infinite-loop stalls.
Built on SWE-bench Verified and our own extended benchmarks, we perform end-to-end deep evaluation of software-engineering agents — covering issue understanding, code localization, patch generation, and test verification.
Six ready-to-use standard agent datasets spanning web operations, code generation, tool calling, multi-step reasoning, failure cases, and multi-agent collaboration — delivered via subscription or fully customized engagement.
Human operation trajectories for real-world web tasks — e-commerce checkout, information lookup, form completion, and content creation — including screenshot sequences, DOM snapshots, mouse-and-keyboard events, and action-intent labels.
Complete developer trajectories from requirement understanding through code commit — including reasoning, file edits, terminal commands, debugging, and test verification — aligned with the SWE-bench task paradigm.
Function-calling and MCP tool-invocation training data covering API selection, parameter filling, error handling, result parsing, and chained calls — compatible with OpenAI and Anthropic tool specifications.
Multi-step planning and reasoning trajectories for complex tasks — including task decomposition, sub-goal setting, state tracking, and backtracking — covering ReAct, Reflexion, and Tree-of-Thought paradigms.
A curated library of agent execution failures with failure-type labels, failure-point localization, root-cause analysis, and repair suggestions — covering 8 major failure-mode categories including planning, tool misuse, and loop stalls.
Multi-agent collaboration trajectories with role specialization, task delegation, message passing, shared memory, and conflict resolution — covering AutoGen, CrewAI, MetaGPT, and other mainstream collaboration paradigms.
From browser automation to software engineering, from enterprise workflows to multi-agent collaboration, Langhui's Agentic AI data services fit every autonomous-agent deployment scenario.
For Operator- and Mariner-class browser agents, we provide real-world web operation trajectories and failure cases across e-commerce, mobility, government services, and SaaS — helping models reach 95%+ task completion rates.
For SWE agents, we provide complete development trajectories from issue understanding to PR submission, plus SWE-bench evaluation services — covering the full stack of bug fixing, feature implementation, refactoring, and test authoring.
For enterprise office-automation agents, we provide multi-system collaboration trajectories and tool-calling data across approvals, reports, email, scheduling, and CRM — helping agents land in real business processes.
For multi-agent collaboration systems, we provide collaboration data for role specialization, task delegation, message passing, and conflict arbitration — supporting training and evaluation across AutoGen, CrewAI, MetaGPT, and other mainstream frameworks.
Langhui has built a quality-assurance system that runs through the entire agent-data production pipeline — from trajectory completeness to environment fidelity, evaluation coverage, and failure-mode identification — with quantifiable KPIs at every dimension.
Partner with Langhui's expert team to inject the latest 2026 agent research into your autonomous agents. Whether you're building a browser agent, a code agent, or a multi-agent collaboration system, we tailor end-to-end solutions spanning training data, evaluation benchmarks, and beyond.