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AI Agent Capability Directory

A curated catalog of AI agents and tools for the financial services industry. Static reference — no API calls required.

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Production-Ready
AI agent capability catalog and selection workflow

Turn a catalog entry into a testable agent capability

A useful AI tool catalog does more than list attractive names. It helps a team move from a business intent to a capability whose inputs, outputs, permissions, evidence, and failure behavior can be inspected before an agent is allowed to call it.

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Start with the outcome

Define who will use the result, what decision it supports, which entity and time range matter, and what a successful answer must contain. This prevents teams from selecting a tool because its label merely sounds relevant.

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Inspect the capability contract

Confirm required arguments, field definitions, source coverage, freshness, units, rate limits, and authentication. A tool is only a fit when its contract can express the user’s actual request without silently changing intent.

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Define permissions and review

Separate read-only research from actions that send messages, place orders, modify records, or expose sensitive data. Document approval points, least-privilege credentials, and the conditions that require clarification.

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Run an acceptance test

Test a normal request, a missing parameter, stale or incomplete data, a provider timeout, and an unauthorized action. Record the selected capability, arguments, source timestamps, response status, and any fallback used.

What to verify before implementation

  • Coverage: the provider supports the required markets, regions, entities, languages, and historical depth.
  • Evidence: the response exposes source identity, as-of time, units, and enough metadata for a reviewer to reproduce the result.
  • Failure policy: the agent can distinguish retryable transport errors from missing meaning-changing inputs or prohibited actions.
  • Operational fit: latency, quotas, cost, caching rules, and data licensing match the expected call pattern.

Practical rule: shortlist by use case, inspect by contract, and approve by evidence. A maturity badge or concise description is a planning shortcut—not a substitute for testing in your environment.

把目录条目转化为可验证的 Agent 能力

有价值的 AI 工具目录不只是罗列名称,而是帮助团队从业务意图出发,找到一个在调用前就能检查输入、输出、权限、证据和失败行为的能力。

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先明确结果

先说明谁会使用结果、结果支持什么决策、涉及哪个对象和时间范围,以及一份合格答案必须包含什么。这样可以避免仅凭名称相似就选错工具。

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检查能力契约

核对必填参数、字段定义、数据覆盖、新鲜度、单位、限流和认证方式。只有当能力契约能够准确表达用户请求,而不悄悄改变原意时,它才真正适用。

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划清权限与复核边界

将只读研究与发送消息、下单、修改记录或接触敏感数据等操作分开,明确人工审批点、最小权限凭证,以及必须向用户追问的情况。

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完成验收测试

分别测试正常请求、参数缺失、数据过期或不完整、服务商超时和越权操作,并记录所选能力、调用参数、来源时间、响应状态和回退路径。

实施前需要确认什么

  • 覆盖范围:服务商是否支持目标市场、地区、实体、语言和所需历史深度。
  • 证据完整性:结果是否包含来源、截至时间、单位,以及足以让审核者复现结论的元数据。
  • 失败策略:Agent 能否区分可重试的传输错误、会改变语义的缺失参数与被禁止的操作。
  • 运行适配:延迟、配额、成本、缓存规则和数据许可是否适合预期调用模式。

实用原则:按使用场景初选,按能力契约核验,按证据质量批准。成熟度标签和短描述只能辅助规划,不能替代真实环境中的测试。