Insights Topic
Published INVISOM articles connected to ai agents.
10 articles
All insightsIntelligent AutomationComparison
Most processes need judgment at a few steps, not everywhere. Build the process spine as a deterministic workflow, use RPA only where there is no API, and give agents the narrow steps where judgment over messy input actually pays.
Enterprise AIFramework
Token prices are the most visible line in a generative AI budget and rarely the decisive one. Production cost is set by architecture, by the people around the model and, if you self-host, by utilization.
Intelligent AutomationComparison
Both tools are now capable agentic engineers. The decision that matters is less which one you choose than how you deploy it: through which cloud, with which permissions, inside which pipeline, and measured against what.
Data ArchitectureTechnical Deep Dive
Databricks' real strength is that pipelines, models, agents and their traces share one governance layer. The price is that its newest AI features are serverless, billed on separate lines and unevenly available by region. Adopt it workload by workload, not wholesale.
Intelligent AutomationTechnical Deep Dive
The hard part of enterprise agents is no longer building one. It is making many of them reliable, affordable and governable, on products that change faster than procurement cycles. That is a control-plane problem before it is a model problem.
GovernanceImplementation Guide
Neither Saudi Arabia nor the UAE governs AI through a single AI act. The binding duties sit in data protection law, data classification, free-zone regulation and government programs, and many of them already apply to systems in production today.
Intelligent AutomationFramework
Adding a reviewer does not make automation safe. Human oversight works only where the human adds information the system lacks, and only if you measure whether reviewers actually catch errors.
Knowledge IntelligenceArchitecture Guide
Models are now interchangeable; context is not. Organizations that build context once, as a governed shared layer with permissions attached, will reuse it across every assistant and agent. Those that wire it per project will rebuild it every time.
GovernanceImplementation Guide
Most enterprise AI data exposure is not a model memorizing secrets. It is ordinary access control failing in new places: data sent out under the wrong terms, retrieval that ignores permissions, and agents that take instructions from what they read.
Intelligent AutomationArchitecture Guide
An instruction in a prompt is a request, not a control. Design every agent on the assumption that its model will eventually be manipulated, and put the guardrails where the model cannot reach them.