Latitude Agents
Build self-improving AI agents
Artificial Intelligence

Featured onMarch 5th, 2025

Latitude empowers the next billion AI builders to design, evaluate, and deploy truly autonomous AI agents.

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9th

Hello Product Hunt!

We're happy to come back to PH to introduce Latitude AI Agents, the end-to-end platform to design, evaluate, and refine your agents.

Key Features:

- Autonomous Agentic Runtime: Craft prompts that run in a loop until the agent achieves its goal, fully integrated with your existing tools and data.

- Multi-Agent Orchestration: Break down complex tasks into smaller agents and easily manage their contexts.

- Self-Improving Prompts: Use other LLMs to evaluate agent performance and automatically refine the agent's instructions based on the results.

- Easy Integration via SDK or API: Integrate with agents into your codebase using our SDKs for Python and TypeScript.

- Model Context Protocol Ready: Connect with many platforms offering tools and resources for agents, or create your own custom MCP server.

We'd love to hear your thoughts. Are you building an agent in 2025?

Looking forward to your feedback!

Comment highlights

By this product Ai agents building app will be accessible to a wider audience and performance of such agents can be evaluated in realistic scenarios.

All the best with the launch! @Latitude @heycesr

This looks like a really smart way to build AI agents without all the usual headaches! I love that it can refine prompts automatically, does that mean it learns from past mistakes and improves over time?

I've been using them for months and felt in love on first sight. Great product by UX/UI and also very useful - nice to see them their innovation pace is so fast too! Looking forward to see them in great places!

Ambitious vision! Curious to see how Latitude handles real-world autonomy challenges and scalability. 🚀

Hey César! This is cool! How does agent performance evaluation work? I imagine it can sometimes be really hard to do, even for a human.

The "self-improving" part is interesting, could be useful for creating agents that get better over time instead of staying static.