Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

When every employee is building AI agents, seeing what they did afterward isn't enough. AgentIQ's in-flow governance runs inside the agent's execution flow—pausing for human approval, enforcing policies by value, masking PII, and stopping runaway agents before costs spiral.

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.