Ramp 在博客中解释了自研智能体框架 Laminar 而非采用托管 agents API 的原因：一是需要模型与供应商无关的自由度，避免被单一 LLM 供应商锁定；二是框架与产品团队同公司协作，反馈闭环比外部供应商快得多。
Build your own harness, folks!
Highly recommended reading on Ramp's own agent harness and why they built one.
I've seen this closely. More hardcore engineering teams are realizing how important it is to own the harness. I expect more full-stack AI companies to follow.
Ramp quickly realized they could move faster and better with their agent harness.
Benefits:
- Model-and-provider agnostic
- Shipping faster as the harness is closer to other teams
- Owning the observability stack (a must given the complexity of agentic systems today)
Their Laminar harness supports secure conversation storage, MCP for standard tool calling, durable execution, code execution sandboxes, observability tools, and more.
When you own the harness, you can co-evolve it to meet your needs. The foundations don't change much; only the tooling and behavior need extending, so make the harness malleable from the start.
Ramp also made several interesting design choices, such as using sandbox-as-a-tool, leveraging Boring and a standard stack (Postgres, S3, etc.), always-open agent sessions, and going all-in on cloud.
after much experimentation, we landed on a single set of rails that powers our agentic systems across the company - background agents, async agents, live chat: 1. stack - accounting harness (http://ramp.com/stack) 2. glass - general purpose coworker (http://ramp.com/glass) 3. inspect - coding agent 4. our support chat bot, Ask Ramp 5. headless agents that do document OCR 6. ramp text (HIRAMP/447-267) agent works with all models, supports all modern harness features - background tasks, sandboxes, tool hooks, tool search, durability, forking, compaction, queueing, streaming, and observability laminar, our new harness, sits on top of ramp router, and brings enormous leverage to our agent developers across the company more info on our blog ->在 X 查看被引用的帖子
来源：elvis · x.com