True story: an Enterprise team spent 6 weeks, and a small fortune, building a Slack integration for their agent – and the agent still sends DMs to the wrong person 40% of the time. Why? They wrapped Slack's API directly. Now their LLM has to: - Call users.list endpoint - Paginate through responses - Find the right user ID - Track context across multiple calls - Finally send the message That's 5 opportunities for hallucination. 5x the tokens. 5x the latency. A proper tool? One function: send_dm(username, message). The conversion happens in deterministic code, not probabilistic guessing. This pattern repeats across every enterprise system. Salesforce "get deals"? That's 4 API calls collapsed into one tool. Google Calendar scheduling? 6 calls become one. Your agents are failing because you're making them work too hard. And you're paying premium prices for that failure. At Arcade.dev, we've solved this with 100+ pre-built tools that actually understand how LLMs think. But even if you build your own - stop making your agents navigate raw APIs. Time to rethink your architecture.
We need to make people list their tool taxonomy!
Are these pre-built toolkits still open source?
Wouldn’t it be easier if you choose certain tools the agent has access to before you unleash it to do the tasks it needs to?
Amazing! What's step one for those teams using raw API calls in their lean agi infrastructures?
I built a slack agent, it just responds with a link to the support site!
Solid. AI is like a junior employee. The clearer and simpler the task, the higher the chances of succeeding 🙌
Bridging the Deterministic world to the Probablistic world = MCP discreet tools
LinkIQ - Your AI-Powered Butler
1moImagine what happens at 100s, 1000s, Ms, Bs.. calls https://www.linkedin.com/posts/arvindsoni1_llm-genai-agents-activity-7334451082229624832--QzJ?utm_source=share&utm_medium=member_ios&rcm=ACoAAAB_jxcBwluOVF8FO3S0WO3y8AvOslK6bsM