AI Agents for Web Research
Use QVeris to help AI agents discover, inspect, and call verified research capabilities for source discovery, public information lookup, webpage extraction, topic research, and structured briefs.
Web Research Agents Need More Than Model Memory
AI agents can summarize and reason, but useful web research workflows need access to external research capabilities. A web research agent may need to discover sources, inspect webpages, extract public information, compare multiple sources, summarize documents, and generate structured briefs.
QVeris gives agents one capability layer for discovering, inspecting, and calling relevant research capabilities without hardcoding every provider or relying on manual copy-paste between search tools, webpages, and documents.
From Research Question to Source Map
How QVeris connects a research question to a structured brief through discoverable, inspectable capabilities.
Each node represents a capability type the agent can discover, inspect, and call through QVeris — not a fixed integration.
Map Claims to Sources Before You Browse
A web research agent should begin with the claims it must support—not an instruction to search broadly. Define the audience, decision, entities, geography, time range, freshness cutoff, exclusions, and final deliverable first.
Break the main question into answerable subquestions, entity aliases, jurisdictions, date windows, and conditions that would change the conclusion.
OUTPUT: a finite list of evidence needsFor each required claim, name the preferred source class, acceptable alternative, minimum freshness, and whether independent corroboration is mandatory.
OUTPUT: authority rules before ranking resultsSpecify coverage, citation granularity, unresolved-question handling, output schema, and the point at which more searching no longer changes the decision.
OUTPUT: a defensible stopping rule“Compare three vendors” becomes official product scope, current pricing, security documentation, integration support, customer evidence, and independently reported limitations. Search ranking and repeated wording do not count as proof.
浏览之前，先把结论映射到来源
网页研究 Agent 应从需要证实的结论开始，而不是接受“广泛搜索”的模糊指令。先定义受众、决策、实体、地域、时间范围、时效截止点、排除项和最终交付形式。
把主问题拆成可以回答的子问题、实体别名、司法辖区、日期窗口，以及会改变结论的条件。
输出：有限、明确的证据需求针对每个必需结论，指定优先来源类别、可接受替代来源、最低时效，以及是否必须独立交叉验证。
输出：结果排序之前的权威规则规定覆盖范围、引用粒度、未解决问题处理、输出 schema，以及继续搜索已不再改变决策的停止条件。
输出：可以解释的停止规则“比较三家供应商”应拆成官方产品范围、当前价格、安全文档、集成支持、客户证据和独立报道的限制。搜索排名与大量重复表述不能作为事实证明。
Why Web Research Agents Are Hard to Build
Four core challenges that make repeatable web research workflows difficult to build and scale.
Research Sources Are Scattered
Useful research often spans search results, webpages, public databases, documents, reports, product pages, and company information — each with its own access pattern.
Agents Need to Inspect Tools Before Using Them
Before executing a research capability, agents need to understand required inputs, response format, provider behavior, cost signals, and output limitations.
Manual Copy-Paste Does Not Scale
Searching, opening pages, copying text, comparing sources, and formatting briefs manually makes repeatable research workflows slow and inconsistent across teams.
Research Outputs Need Verification
AI-generated research outputs should be reviewed, verified, and checked against sources before publication or use in high-stakes decisions.
How QVeris Powers Web Research Agents
Discover research capabilities
The agent searches QVeris for relevant capabilities such as web search, source discovery, webpage extraction, document summarization, company research, or structured summarization.
Inspect before calling
The agent inspects schema, required inputs, response format, cost signals, and provider information before execution — no blind calls to unknown research tools.
Call and structure the brief
The agent calls selected capabilities and turns returned outputs into a structured research brief with key findings, source notes, open questions, and next steps.
Web Research Workflows You Can Build with QVeris
A research tasks board showing how capabilities map to concrete actions across the Discover, Extract, and Structure phases.
Example Structured Brief from a Web Research Agent
Illustrative example of a research brief generated through QVeris capabilities. Not live research data or verified source content.
This is an illustrative example. It does not represent real search results, verified sources, or factual claims about any company or topic. Research outputs should be reviewed before publication or high-stakes use.
Preserve the Page, Time, and Evidence Chain
A citation is useful only when it directly supports the nearby claim and can be reconstructed later. Ten articles repeating one press release remain one evidence chain—not ten independent confirmations.
For high-impact claims, follow citations to the original material and record which pages depend on the same underlying source.
Separate event time from page time
Capture original publication, last update, effective date, event date, and timezone. A recently updated page can still describe an old event.
Record the retrieved version
Keep retrieval time, canonical URL, title, author or organization, language, content hash or snapshot reference, and the exact excerpt location used.
Expose coverage gaps
Record blocked resources, missing dates, dynamic content, geographic variants, inaccessible attachments, and whether the result is complete, sampled, or truncated.
Re-run affected claims
When a page changes, do not silently blend versions. State which version supported the conclusion and revisit every dependent claim.
Compare entity identity, definitions, measurement method, reporting period, effective date, jurisdiction, sample, version, and incentives. Prefer direct authority, explain material differences, and preserve uncertainty when the conflict cannot be resolved.
保留页面、时间与完整证据链
只有当引用直接支持相邻结论，并且未来可以重建时，它才真正有用。十篇重复同一份新闻稿的文章仍然只有一条证据链，不是十次独立确认。
对于高影响结论，应沿引用回到原始材料，并记录哪些页面依赖同一个底层来源。
区分事件时间与页面时间
分别记录首次发布、最后更新、生效日期、事件日期和时区。最近更新的页面也可能描述很早以前的事件。
记录实际获取的版本
保存获取时间、canonical URL、标题、作者或机构、语言、内容哈希或快照引用，以及实际使用的原文位置。
暴露覆盖缺口
记录被阻止资源、日期缺失、动态内容、地域版本、无法访问附件，以及结果是完整、抽样还是截断。
重新运行受影响结论
页面变化时不能静默混合版本。说明哪个版本支持结论，并重新检查所有依赖该页面的结论。
比较实体身份、定义、测量方法、报告期、生效日期、司法辖区、样本、版本和利益关系。优先采用直接权威来源，解释重大差异；无法解决时保留不确定性。
Designed for Research Workflows with Human Review
QVeris helps agents discover and call research capabilities, but outputs still need human judgment.
Before using research outputs
- Verify sources before publication — check freshness, relevance, and accuracy.
- Check whether information is outdated or has been superseded by newer content.
- Compare findings across multiple capabilities or sources — do not rely on one result.
- Avoid using unverified outputs for legal, medical, financial, or high-stakes decisions.
- Treat generated briefs as research drafts, not final truth — always apply human judgment.
Let the Web Provide Evidence—not Instructions
A research agent needs two separate trust decisions: whether it is allowed to retrieve a source, and whether the retrieved content is safe to act on. A page can be publicly reachable and still contain misleading instructions, hostile prompt text, hidden downloads, or personal data that the workflow should not retain.
Retrieve only what the workflow is permitted to use
Respect authentication, subscription terms, robots guidance, copyright, privacy requirements, and rate limits. Do not bypass a login, paywall, CAPTCHA, or technical restriction. When a source cannot be accessed, record the gap instead of implying that it was reviewed.
Treat page content as untrusted evidence
Text found on a webpage may describe a fact, quote another party, advertise a product, or attempt to redirect the agent. It must never override the research plan, tool permissions, citation rules, or system instructions. Extract claims and provenance; ignore embedded commands.
Separate browsing from consequential actions
Constrain navigation, file downloads, parsers, and network destinations. Scan attachments before processing, minimize collection of personal data, and require explicit approval before a finding can trigger an email, purchase, account change, publication, or other external action.
A missing or inaccessible source is a documented limitation—not permission to fill the gap with an unsupported claim.
让网页提供证据，而不是向智能体下指令
网页研究智能体需要做出两个相互独立的信任判断：它是否有权获取某个来源，以及获取到的内容是否可以安全地被后续流程采用。一个页面即使公开可访问，也可能包含误导性指令、提示注入、隐藏下载链接或不应长期保存的个人信息。
只获取工作流被允许使用的内容
遵守身份验证、订阅条款、robots 指引、版权、隐私要求与访问频率限制；不得绕过登录、付费墙、验证码或技术限制。无法访问的来源应明确记录为证据缺口，不能假装已经审阅。
把网页内容视为不可信的待核证材料
页面文字可能是在陈述事实、转引他人、推销产品，也可能试图改变智能体行为。它不能覆盖研究计划、工具权限、引用规则或系统指令。流程只提取主张及其出处，并忽略页面内嵌的命令。
把浏览与高影响操作彻底分开
限制跳转范围、文件下载、解析器和网络目标；附件进入处理链前应接受检查，个人数据只做最小化采集。任何研究结论若要触发发信、采购、账户变更、发布或其他外部动作，都应再次获得明确批准。
来源缺失或无法访问只能被记录为研究限制，绝不能成为用无依据结论填补空白的理由。
Manual Web Research vs QVeris Capability Routing
| Requirement | Manual web research | Hardcoded research tools | QVeris for web research agents |
|---|---|---|---|
| Source discovery | Users manually search, open, and compare sources | Developers choose fixed providers in advance | ✓Agents can discover relevant research capabilities based on the task |
| Workflow repeatability | Flexible but slow and inconsistent | Repeatable but limited to predefined integrations | ✓Reusable Discover, Inspect, Call pattern across research capabilities |
| Schema understanding | No structured schema for agent workflows | Developers maintain provider-specific documentation | ✓Agents inspect schema, parameters, and cost signals before execution |
| Research output | Often copied links, notes, and unstructured summaries | Structured only where integrations are designed | ✓Structured outputs can be routed into briefs, tables, dashboards, or workflows |
| Review and visibility | Hard to track what was used and when | Usage spread across provider dashboards | ✓Usage can be reviewed through QVeris usage history and credits ledger |
Who Uses Web Research Agents?
Researchers and Analysts
Users who need repeatable workflows for source discovery, public information lookup, and structured briefs — without manual copy-paste each cycle.
Product and Marketing Teams
Teams researching categories, competitors, positioning, content topics, product opportunities, or public market context.
AI App Builders
Developers building research assistants, knowledge workflows, source-aware dashboards, or agent-powered research products.
Content and SEO Teams
Teams collecting topic context, source ideas, competitor pages, SERP patterns, and research outlines for content planning.
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Official provider references
Frequently Asked Questions
What are AI agents for web research?
How does QVeris help web research agents?
Can QVeris support source discovery workflows?
Is QVeris a search engine?
Do agents inspect research tools before using them?
Can web research outputs be used without review?
Do I need to hardcode every web research provider?
What can a web research agent build with QVeris?
Build Web Research Agents with Real Capabilities
Use QVeris to give AI agents access to research capabilities for source discovery, public information lookup, webpage extraction, topic research, and structured briefs.
面向网页研究的 AI Agent
使用 QVeris 帮助 AI Agent 发现、检查并调用经过验证的研究能力，用于来源发现、公开信息查询、网页提取、主题研究和结构化简报生成。
网页研究 Agent 需要的不仅是模型记忆
AI Agent 可以总结和推理，但有用的网页研究工作流需要访问外部研究能力。网页研究 Agent 可能需要发现来源、检查网页、提取公开信息、对比多个来源、总结文档并生成结构化简报。
QVeris 为 Agent 提供统一的能力层，用于发现、检查和调用相关研究能力——无需硬编码每个提供商或依赖在搜索工具、网页和文档之间手动复制粘贴。
从研究问题到来源地图
QVeris 如何通过可发现、可检查的能力将研究问题连接到结构化简报。
每个节点都代表一种可由 Agent 通过 QVeris 发现、检查并调用的能力类型，而不是某个写死的集成。
Map Claims to Sources Before You Browse
A web research agent should begin with the claims it must support—not an instruction to search broadly. Define the audience, decision, entities, geography, time range, freshness cutoff, exclusions, and final deliverable first.
Break the main question into answerable subquestions, entity aliases, jurisdictions, date windows, and conditions that would change the conclusion.
OUTPUT: a finite list of evidence needsFor each required claim, name the preferred source class, acceptable alternative, minimum freshness, and whether independent corroboration is mandatory.
OUTPUT: authority rules before ranking resultsSpecify coverage, citation granularity, unresolved-question handling, output schema, and the point at which more searching no longer changes the decision.
OUTPUT: a defensible stopping rule“Compare three vendors” becomes official product scope, current pricing, security documentation, integration support, customer evidence, and independently reported limitations. Search ranking and repeated wording do not count as proof.
浏览之前，先把结论映射到来源
网页研究 Agent 应从需要证实的结论开始，而不是接受“广泛搜索”的模糊指令。先定义受众、决策、实体、地域、时间范围、时效截止点、排除项和最终交付形式。
把主问题拆成可以回答的子问题、实体别名、司法辖区、日期窗口，以及会改变结论的条件。
输出：有限、明确的证据需求针对每个必需结论，指定优先来源类别、可接受替代来源、最低时效，以及是否必须独立交叉验证。
输出：结果排序之前的权威规则规定覆盖范围、引用粒度、未解决问题处理、输出 schema，以及继续搜索已不再改变决策的停止条件。
输出：可以解释的停止规则“比较三家供应商”应拆成官方产品范围、当前价格、安全文档、集成支持、客户证据和独立报道的限制。搜索排名与大量重复表述不能作为事实证明。
为什么网页研究 Agent 很难构建
四个核心挑战使可重复的网页研究工作流难以构建和规模化。
研究来源分散
有用的研究通常跨越搜索结果、网页、公开数据库、文档、报告、产品页面和公司信息——每个都有各自的访问模式。
Agent 需要在使用前检查工具
在执行研究能力之前，Agent 需要了解所需输入、响应格式、提供商行为、成本信号和输出限制。
手动复制粘贴不可扩展
手动搜索、打开页面、复制文本、对比来源和格式化简报使可重复的研究工作流变得缓慢且不一致。
研究输出需要验证
AI 生成的研究输出应在发布或用于高风险决策之前进行审查、验证并与来源核对。
QVeris 如何驱动网页研究 Agent
发现合适的研究能力
Agent 会在 QVeris 中查找与任务相关的能力，例如网页搜索、来源发现、网页提取、文档摘要、公司研究或结构化总结。
调用前先检查
执行前，Agent 会检查 Schema、必填参数、响应格式、成本信号和服务商信息，避免盲目调用不了解的研究工具。
调用能力并整理研究简报
Agent 调用选定能力，并将返回结果整理成结构化研究简报，清楚列出关键发现、来源备注、待解决问题和后续步骤。
使用 QVeris 可以构建的网页研究工作流
一个研究任务看板，展示能力如何映射到发现、提取和结构化阶段的具体行动。
网页研究 Agent 的结构化简报示例
通过 QVeris 能力生成的研究简报示意图。非真实研究数据或已验证的来源内容。
这是示意性示例。不代表真实搜索结果、已验证来源或关于任何公司或主题的事实性声明。研究输出应在发布或高风险使用前进行审查。
Preserve the Page, Time, and Evidence Chain
A citation is useful only when it directly supports the nearby claim and can be reconstructed later. Ten articles repeating one press release remain one evidence chain—not ten independent confirmations.
For high-impact claims, follow citations to the original material and record which pages depend on the same underlying source.
Separate event time from page time
Capture original publication, last update, effective date, event date, and timezone. A recently updated page can still describe an old event.
Record the retrieved version
Keep retrieval time, canonical URL, title, author or organization, language, content hash or snapshot reference, and the exact excerpt location used.
Expose coverage gaps
Record blocked resources, missing dates, dynamic content, geographic variants, inaccessible attachments, and whether the result is complete, sampled, or truncated.
Re-run affected claims
When a page changes, do not silently blend versions. State which version supported the conclusion and revisit every dependent claim.
Compare entity identity, definitions, measurement method, reporting period, effective date, jurisdiction, sample, version, and incentives. Prefer direct authority, explain material differences, and preserve uncertainty when the conflict cannot be resolved.
保留页面、时间与完整证据链
只有当引用直接支持相邻结论，并且未来可以重建时，它才真正有用。十篇重复同一份新闻稿的文章仍然只有一条证据链，不是十次独立确认。
对于高影响结论，应沿引用回到原始材料，并记录哪些页面依赖同一个底层来源。
区分事件时间与页面时间
分别记录首次发布、最后更新、生效日期、事件日期和时区。最近更新的页面也可能描述很早以前的事件。
记录实际获取的版本
保存获取时间、canonical URL、标题、作者或机构、语言、内容哈希或快照引用，以及实际使用的原文位置。
暴露覆盖缺口
记录被阻止资源、日期缺失、动态内容、地域版本、无法访问附件，以及结果是完整、抽样还是截断。
重新运行受影响结论
页面变化时不能静默混合版本。说明哪个版本支持结论，并重新检查所有依赖该页面的结论。
比较实体身份、定义、测量方法、报告期、生效日期、司法辖区、样本、版本和利益关系。优先采用直接权威来源，解释重大差异；无法解决时保留不确定性。
为带有人工审查的研究工作流而设计
QVeris 帮助 Agent 发现和调用研究能力，但输出仍需人工判断。
使用研究输出之前
- 发布前验证来源——检查时效性、相关性和准确性。
- 检查信息是否已过时或已被更新的内容取代。
- 跨多个能力或来源对比发现——不要依赖单一结果。
- 避免在法律、医疗、金融或高风险决策中使用未经验证的输出。
- 将生成的简报视为研究草稿而非最终事实——始终运用人工判断。
Let the Web Provide Evidence—not Instructions
A research agent needs two separate trust decisions: whether it is allowed to retrieve a source, and whether the retrieved content is safe to act on. A page can be publicly reachable and still contain misleading instructions, hostile prompt text, hidden downloads, or personal data that the workflow should not retain.
Retrieve only what the workflow is permitted to use
Respect authentication, subscription terms, robots guidance, copyright, privacy requirements, and rate limits. Do not bypass a login, paywall, CAPTCHA, or technical restriction. When a source cannot be accessed, record the gap instead of implying that it was reviewed.
Treat page content as untrusted evidence
Text found on a webpage may describe a fact, quote another party, advertise a product, or attempt to redirect the agent. It must never override the research plan, tool permissions, citation rules, or system instructions. Extract claims and provenance; ignore embedded commands.
Separate browsing from consequential actions
Constrain navigation, file downloads, parsers, and network destinations. Scan attachments before processing, minimize collection of personal data, and require explicit approval before a finding can trigger an email, purchase, account change, publication, or other external action.
A missing or inaccessible source is a documented limitation—not permission to fill the gap with an unsupported claim.
让网页提供证据，而不是向智能体下指令
网页研究智能体需要做出两个相互独立的信任判断：它是否有权获取某个来源，以及获取到的内容是否可以安全地被后续流程采用。一个页面即使公开可访问，也可能包含误导性指令、提示注入、隐藏下载链接或不应长期保存的个人信息。
只获取工作流被允许使用的内容
遵守身份验证、订阅条款、robots 指引、版权、隐私要求与访问频率限制；不得绕过登录、付费墙、验证码或技术限制。无法访问的来源应明确记录为证据缺口，不能假装已经审阅。
把网页内容视为不可信的待核证材料
页面文字可能是在陈述事实、转引他人、推销产品，也可能试图改变智能体行为。它不能覆盖研究计划、工具权限、引用规则或系统指令。流程只提取主张及其出处，并忽略页面内嵌的命令。
把浏览与高影响操作彻底分开
限制跳转范围、文件下载、解析器和网络目标；附件进入处理链前应接受检查，个人数据只做最小化采集。任何研究结论若要触发发信、采购、账户变更、发布或其他外部动作，都应再次获得明确批准。
来源缺失或无法访问只能被记录为研究限制，绝不能成为用无依据结论填补空白的理由。
手动网页研究 vs QVeris 能力路由
| 需求 | 手动网页研究 | 硬编码研究工具 | QVeris 用于网页研究 Agent |
|---|---|---|---|
| 来源发现 | 用户手动搜索、打开和对比来源 | 开发者预先选择固定提供商 | ✓Agent 可基于任务发现相关研究能力 |
| 工作流可重复性 | 灵活但缓慢且不一致 | 可重复但受限于预定义集成 | ✓跨研究能力复用发现、检查、调用模式 |
| Schema 理解 | Agent 工作流无结构化 Schema | 开发者维护提供商特定文档 | ✓Agent 在执行前检查 Schema、参数和成本信号 |
| 研究输出 | 通常为复制链接、笔记和非结构化摘要 | 仅在集成设计处有结构化输出 | ✓结构化输出可路由到简报、表格、仪表盘或工作流 |
| 审查与可见性 | 难以追踪使用了什么和何时使用 | 使用情况分散在提供商仪表盘中 | ✓可通过 QVeris 使用历史和 credits 账本查看使用情况 |
谁在使用网页研究 Agent？
研究人员和分析师
需要可重复工作流来进行来源发现、公开信息查询和结构化简报的用户——无需每个周期手动复制粘贴。
产品和营销团队
研究品类、竞品、定位、内容主题、产品机会或公开市场背景的团队。
AI 应用构建者
正在构建研究助手、知识工作流、来源感知仪表盘或 Agent 驱动研究产品的开发者。
内容和 SEO 团队
收集主题背景、来源创意、竞品页面、SERP 模式和研究大纲用于内容规划的团队。
继续探索 QVeris
官方 Provider 文档
常见问题
什么是面向网页研究的 AI Agent？
QVeris 如何帮助网页研究 Agent？
QVeris 能支持来源发现工作流吗？
QVeris 是搜索引擎吗？
Agent 在使用研究工具之前会检查它们吗？
网页研究输出可以在未经审查的情况下使用吗？
我需要硬编码每个网页研究提供商吗？
网页研究 Agent 可以使用 QVeris 构建什么？
用真实能力构建网页研究 Agent
使用 QVeris 为 AI Agent 提供访问研究能力的途径，用于来源发现、公开信息查询、网页提取、主题研究和结构化简报。