我们正在推出 Codag (@ycombinatorS26) - 代理工具的压缩和控制层。
我们正在推出 Codag (@ycombinatorS26) - 代理工具的压缩和控制层。我们从日志开始。然后客户让我们进行搜索、测试、构建和 API 响应。模式总是相同的：代理在重复输出上燃烧上下文，而团队无法看到token去了哪里。 Codag 现在以答案对等的方式将输入令牌的使用量减少了约 3 倍，并为团队提供了整个代理堆栈的可见性。代理商正在成为软件的主要用户——公司正在增加代理商、模型、提供商和MCP服务器的速度快于其测量或控制的速度。大多数人无法回答他们的代理使用了哪些工具，这些工具的成本是多少，或者浪费在哪里。我们正在他们和他们阅读的所有内容之间建立一层。目前，90 多个工程组织已使用 Codag 来减少支出并查看跨提供商的代理工具使用情况。
查看英文原文
we’re launching Codag (@ycombinator S26) - the compression and control layer for agent tools. we started with logs. then customers pulled us into searches, tests, builds, and api responses. the pattern was always the same: agents burning context on repetitive output, with teams unable to see where the tokens went. Codag now cuts input-token usage ~3x at answer parity and gives teams visibility across their agent stack. agents are becoming the primary users of software - companies are adding agents, models, providers, and MCP servers faster than they can measure or control them. most cannot answer which tools their agents invoke, what those calls cost, or where the waste is. we’re building the layer between them and everything they read. 90+ engineering orgs already use Codag today to reduce that spend and see agent tool usage across provider. find out more and book a demo at https://codag.ai/
we’re launching Codag (@ycombinator S26) - the compression and control layer for agent tools. we started with logs. then customers pulled us into searches, tests, builds, and api responses. the pattern was always the same: agents burning context on repetitive output, with teams unable to see where the tokens went. Codag now cuts input-token usage ~3x at answer parity and gives teams visibility across their agent stack. agents are becoming the primary users of software - companies are adding agents, models, providers, and MCP servers faster than they can measure or control them. most cannot answer which tools their agents invoke, what those calls cost, or where the waste is. we’re building the layer between them and everything they read. 90+ engineering orgs already use Codag today to reduce that spend and see agent tool usage across provider. find out more and book a demo at https://codag.ai/
we’re launching Codag (@ycombinator S26) - the compression and control layer for agent tools. we started with logs. then customers pulled us into searches, tests, builds, and api responses. the pattern was always the same: agents burning context on repetitive output, with teams unable to see where the tokens went. Codag now cuts input-token usage ~3x at answer parity and gives teams visibility across their agent stack. agents are becoming the primary users of software - companies are adding agents, models, providers, and MCP servers faster than they can measure or control them. most cannot answer which tools their agents invoke, what those calls cost, or where the waste is. we’re building the layer between them and everything they read. 90+ engineering orgs already use Codag today to reduce that spend and see agent tool usage across provider. find out more and book a demo at https://codag.ai/
- 来源
- X @ycombinator · x-ycombinator
- 证据角色
- x_project_network
- 时间状态
- known
- 用户判决
- 未判