# Building Reliable AI Products in the Agentic Era

DevFeed: [Building Reliable AI Products in the Agentic Era](<https://devfeed.tech/articles/ai-disruptors-how-the-next-generation-of-business-is-being-built-19858.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/ai-disruptors>)

Author: Dinesh Murthy

Published: 2026-05-29T21:30:04Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Data analysis](<https://devfeed.tech/topics/data-analysis.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [community](<https://devfeed.tech/tags/community.md>), [data-analysis](<https://devfeed.tech/tags/data-analysis.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [llm](<https://devfeed.tech/tags/llm.md>)

## AI overview

A DigitalOcean Deploy 2026 panel explores how founders build dependable AI products when teams can access the same frontier models through APIs. The discussion focuses on reliability, human oversight, production-scale agent behavior, model selection, and creating differentiation beyond the underlying model.

## Source excerpt

Getting your hands on a capable AI model is the easy part now. Every team can reach the same frontier models through an API, so a strong model is not what sets a product apart. What separates a working product from a demo is everything around the model. You have to measure whether the agent is actually doing its job, then keep grinding on reliability until it stops making expensive mistakes in front of real users. I moderated a panel on exactly that at DigitalOcean's Deploy 2026 conference in San Francisco, a forty-minute conversation with four founders on what they've learned shipping AI products that people depend on: Angela Hoover, co-founder and CEO of Andi AI, an ad-free consumer search engine that pairs generative AI with live web data to give people direct answers instead of a page of ad-heavy links. Alex Mashrabov, co-founder and CEO of Higgsfield AI, a platform that lets creators and agencies produce cinematic video without any physical production. Hovsep Seraydarian, co-founder and CTO of LawVo, a Canadian legal platform that pairs hundreds of AI agents trained in specific legal areas with human lawyers who verify their accuracy. Peter Elias, founder of Probably, a data analysis agent that lets non-technical people query their data in plain English and runs calculations on a local engine instead of an LLM so it can decline to answer when the data does not support a clear result. The discussion got into what each founder underestimated once their agents had to run at scale, how they choose models from a field that keeps growing, what "agentic" actually means in production, and where a real moat comes from when everyone builds on the same foundation. Watch the full session from Deploy 2026: View YouTube video Making agents work in production When the founders were asked what they underestimated once their agents had to run in production, none of them pointed to the model. You need creative DNA Higgsfield spent a year on R&D without traction. What finally mov