Alex Wang

<start of post>

The gap between "AI demo" and "production reality" is usually found in the edge cases.

I spent the last week looking at how teams are actually deploying agentic workflows in enterprise environments.

It is one thing to have an agent answer a question. It is another thing entirely to have it navigate a legacy database, handle a timeout, and maintain security protocols.

𝟏. 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐎𝐮𝐭𝐩𝐮𝐭𝐬 𝐨𝐯𝐞𝐫 𝐂𝐡𝐚𝐭

The most successful implementations aren't using open-ended chat interfaces. They are using LLMs to generate structured JSON that triggers specific, pre-defined functions.

𝟐. 𝐇𝐮𝐦𝐚𝐧-𝐢𝐧-𝐭𝐡𝐞-𝐥𝐨𝐨𝐩 𝐢𝐬 𝐧𝐨𝐭 𝐨𝐩𝐭𝐢𝐨𝐧𝐚𝐥

For high-stakes workflows, the agent proposes the action, but a human provides the final "click" to execute. This isn't just for safety—it's for building trust with the end users.

𝟑. 𝐒𝐦𝐚𝐥𝐥𝐞𝐫 𝐦𝐨𝐝𝐞𝐥𝐬 𝐟𝐨𝐫 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐭𝐚𝐬𝐤𝐬

We are seeing a shift away from using the largest frontier models for everything. Teams are routing simple classification tasks to smaller, faster models to reduce latency and cost.

Behind the hype, the real work is happening at the infrastructure level.

It’s not about finding the "perfect prompt." It’s about building a resilient system that can handle the messiness of real-world data.

Worth keeping in mind as you plan your next build.

🔗 https://lnkd.in/example-link

#AIStrategy #EnterpriseAI

PS. We are hosting a deep-dive session on RAG optimization this Thursday. Feel free to join if you're working on this.

<end of post>