---
title: "Built for Mass Scale: Hard-Won Lessons from Teams Running High Volume Inference Workloads in Production"
url: https://daily.dev/posts/built-for-mass-scale-hard-won-lessons-from-teams-running-high-volume-inference-workloads-in-product-pgne7wnao
source_url: https://www.digitalocean.com/blog/lessons-running-inference-workloads
type: article
source: "DigitalOcean"
published: 2026-07-02T20:19:14.218Z
updated: 2026-07-02T22:21:11.144Z
tags: ["llm", "ai-agents", "digitalocean", "ai-inference"]
reading_time: 7
upvotes: 1
comments: 0
language: en
---

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# Built for Mass Scale: Hard-Won Lessons from Teams Running High Volume Inference Workloads in Production

**[DigitalOcean](https://daily.dev/sources/do)** · 7 min read · 1 upvotes · 0 comments

## Summary

Engineering leaders from Workato, Hippocratic AI, and ISMG shared production lessons from running high-volume AI inference workloads at DigitalOcean Deploy 2026. Key themes include: tool selection accuracy degrades sharply when AI agents have access to 50+ tools; P99 latency becomes a patient-safety issue in clinical voice applications; AI agents should never have admin-level permissions but instead operate as time-scoped per-action delegates; and companies that delay structuring their data and workflows before adopting AI risk falling two years behind on their operating model. The panel's consensus is that scaling inference is fundamentally an infrastructure and governance problem, not a model problem.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.digitalocean.com/blog/lessons-running-inference-workloads>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#digitalocean](https://daily.dev/tags/digitalocean), [#ai-inference](https://daily.dev/tags/ai-inference)

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