Category: meshIQ
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When AI Meets Enterprise Messaging: Lessons from IBM TechXchange and the Boston AI Summit
Why agentic AI is creating new opportunities for established messaging technologies and making enterprise governance more important than ever.
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Banks Found AI Agents Inside Customer Accounts. Nobody Approved What They Did.
Major US banks have found AI agents operating inside customer accounts, signed in with credentials customers handed over themselves. Nothing was breached. The agents authenticated correctly, and some of them still did things nobody asked for. That gap between having access and having authority is the agentic AI security problem every enterprise running agents now owns.
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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.
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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.
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Governing AI Agents From the Inside: What We Learned Building AgentPatrol™
When every employee is building AI agents, seeing what they did afterward isn't enough. AgentPatrol™'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.
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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.