Home / Companies / Arize / Blog / Post Details
Content Deep Dive

Hyland’s Approach To AI Agent Engineering

Blog post from Arize

Post Details
Company
Date Published
Author
David Burch
Word Count
1,035
Company Posts That Month
11
Language
English
Hacker News Points
-
Post removed?
No
Summary

Hyland is integrating AI agent technology with their platforms such as OnBase, Alfresco, and Nuxeo to enhance document processing capabilities, emphasizing reliability and context-awareness in enterprise environments. By leveraging the Hyland Agent Builder and agentic document processing, the company aims to transition from impressive demonstrations to dependable, real-world applications, focusing on system behavior under actual conditions. Gabriel Keith, Senior Manager of Engineering, highlights the importance of determinism, human-in-the-loop strategies, and robust security measures, including multi-tenant cloud runtime and the Content Federation Service, which allows agents to interact with on-premises data securely. Hyland's approach includes integrating AI agents within existing business processes to accelerate decision-making and improve workflow efficiency. The platform's architecture, consisting of components like the Agent Builder and Core Runtime, facilitates seamless data access and integration with external and internal APIs, reducing the need for federation while maintaining security mappings. The company utilizes Arize AX for online evaluation and observability to ensure trust and consistency in agent performance, even as data landscapes evolve.

Trends Found in this Post
Trend Post Mentions Total Month Mentions Posts Companies MoM
AI Agents 3 4,870 811 231 +25%
RAG 3 1,251 204 89 +8%
MCP 2 5,348 447 159 0%
Observability 2 3,036 613 175 +13%
Agent Context Engineering 1 221 48 28 +69%
LLM 1 5,275 883 235 +8%
Real-time 1 5,653 1,261 290 -21%
Vector Search 1 1,609 329 156 -17%
Use This Data

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.