---
title: "Where to invest in the AI agent ecosystem?"
source_name: "AI Musings by Mu"
original_url: "https://kelvinmu.substack.com/p/where-to-invest-in-the-ai-agent-ecosystem"
canonical_url: "https://www.traeai.com/articles/d1143361-1478-4a10-846d-d41890611b5a"
content_type: "article"
language: "英文"
score: 8.5
tags: ["AI","初创公司","生态系统","投资"]
published_at: "2025-10-03T13:37:54+00:00"
created_at: "2026-04-29T03:43:36.496515+00:00"
---

# Where to invest in the AI agent ecosystem?

Canonical URL: https://www.traeai.com/articles/d1143361-1478-4a10-846d-d41890611b5a
Original source: https://kelvinmu.substack.com/p/where-to-invest-in-the-ai-agent-ecosystem

## Summary

文章探讨了AI代理生态系统中初创公司的最佳投资机会，聚焦于记忆、评估和安全三大领域。

## Key Takeaways

- 记忆层需独立于单一提供商，初创公司可提供中立解决方案。
- 评估领域需要独立第三方来衡量代理性能与可靠性。
- 安全性需全新架构设计，传统工具无法满足复杂需求。

## Content

Title: Where to invest in the AI agent ecosystem?

URL Source: http://kelvinmu.substack.com/p/where-to-invest-in-the-ai-agent-ecosystem

Published Time: 2025-10-03T13:37:54+00:00

Markdown Content:
With Google’s release of the Agent Payment Protocol (A2P) last month, the agent ecosystem is maturing. But the scaffolding is still incomplete.

My view: most of the underlying infrastructure will be owned by FAAMG or the large LLM labs, with only a few entry points left for startups.

IMO, the best opportunities for startups sit in three areas: memory, evaluation, and security.

1️⃣ Memory: The premise here is that memory shouldn’t be controlled by a single provider. Instead, it should be interoperable and portable across different LLMs and apps. While the majors (Google, Microsoft, OpenAI) would love to monopolize user memory, I believe GenAI usage will stay fragmented. That creates space for independent providers to manage user memory as a neutral layer. Some example of startups playing in this layer include **[Letta](https://www.linkedin.com/company/letta-ai/)**, **[Mem0](https://www.linkedin.com/company/mem0/)**, **[supermemory](https://www.linkedin.com/company/supermemory/)**, and **[Zep AI (YC W24)](https://www.linkedin.com/company/zep-ai/)**

2️⃣ Evaluation: This is the equivalent of a credit bureau for agents. Just as credit bureaus are independent from banks and lenders - and exist to provide an objective credit score - we’ll need third parties to independently assess agent performance, reliability, and trustworthiness. I think this independence is critical: you don’t necessarily want the same company building the agent to also be the one grading it. Some examples of startups include **[Galileo](https://www.linkedin.com/company/galileo-ai/)****[Patronus AI](https://www.linkedin.com/company/patronus-ai-inc/)****[LangSmith AI](https://www.linkedin.com/company/langsmith/)**

3️⃣ Security: Agent security can’t just be patched onto existing products, I think it requires a ground-up rethink. Agents expand the attack surface: more integration points, more control points, and more sensitive data flows. This calls for new systems, not incremental updates to incumbent tools. CrowdStrike was a good example - traditional antivirus couldn’t handle cloud and endpoint complexity, and a fresh architecture redefined the category. Startups like **[Auth0](https://www.linkedin.com/company/auth0/)** and **[Anon](https://www.linkedin.com/company/anon-dot-com/)** are early movers here.

Other components - like planning, tool use, and orchestration, will likely fall to incumbents. These functions can be executed within the LLMs themselves, and standardized protocols (MCP, A2P, A2A) already exist. Also, third-party independence matters much less in these areas.

Curious to hear others’ views - where else do you see room for startups to win?

[![Image 1](https://substackcdn.com/image/fetch/$s_!WsPq!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2ccedda8-fc60-4c70-9ce8-46a7e918ac23_2250x1256.png)](https://substackcdn.com/image/fetch/$s_!WsPq!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2ccedda8-fc60-4c70-9ce8-46a7e918ac23_2250x1256.png)