Category: Integration
Blog
What Are AI Agents? How They Work, Why They Matter and Why They Need Governance
An AI agent doesn’t just answer questions—it takes actions in real systems. As teams across the enterprise build their own agents, governance can’t be an afterthought. Learn how agents work, why they create new risks, and what controls are needed before they act, not after.
Blog
Agentic AI Is Becoming the Enterprise’s Connective Tissue. Governance Has to Move Into the Flow.
McKinsey's 2026 tech report calls agentic AI the 'connective tissue of the enterprise.' But agents that act at machine speed need governance at machine speed too. Static controls won't work for dynamic agents—active, in-flow guardrails must run before the action, not after.
Blog
Moving Mainframe Data to Snowflake and AWS Through Apache Kafka®: Treehouse Software and meshIQ
Treehouse Dataflow Toolkit moves mainframe data from Db2, VSAM, and IMS through Apache Kafka® pipelines into Snowflake and AWS targets. meshIQ keeps the streaming layer visible and stable. Together they give data science teams continuously updated enterprise data for AI and ML.
Blog
Your AI Agents Can Take Action Now. Can You Prove They Should Have?
Enterprise AI agents clear every demo and pilot, then hit a compliance wall. The gap isn't technology—it's architecture. Discover why governance must sit inside the execution flow across six control points, not bolt on afterward as an afterthought at input and output only.
Blog
The Real Cost of Custom Code: Why Buying a Unified Middleware Management Platform Protects Enterprise IT Budgets
Building custom middleware monitoring appears cost-effective but creates expensive maintenance debt, fragmented visibility, and operational risk. Enterprise teams spend 60-80% of IT budgets on software maintenance while unified platforms deliver immediate, production-ready capabilities.
Blog
The AI Bottleneck: Why Your Modern Models Are Choking on Legacy and Streaming Data Architecture
Enterprise AI struggles not from inadequate models, but from fragmented data architecture. Critical business data remains trapped in legacy systems or lost in streaming complexity. Success requires bridging the gap between modern intelligence layers and underlying systems of record.