Low-Code Explained: Definition, Use Cases and Benefits
Low-code lets teams build apps visually and add code only where it matters. Here is what it is, 12 use cases where it pays off, and where it does not fit.
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Low-code lets teams build apps visually and add code only where it matters. Here is what it is, 12 use cases where it pays off, and where it does not fit.
Visual workflow automation lets anyone map a process on a canvas and run it. Here are 9 ideas worth automating first, with free templates to start from.
An AI agent sandbox limits what an agent can reach and break. Here are the layers a good sandbox needs, the options available and how to move agents from sandbox to production.
Every AI agent that acts on company data needs an identity, scoped access and an owner. Here is a practical framework for agent identity and access management.
Agent memory lets an AI agent keep context within a task and across sessions. Here are the types, how they are stored and when an agent should look data up instead.
Agent observability shows what an AI agent actually did and why. Here is what to trace at each layer of the stack, which metrics matter and how guardrails fit in.
Human-in-the-loop AI puts people at the points where an AI mistake would be costly. Here are the patterns, where to use them and how to keep review fast.
MCP makes it easy to give AI agents real power over company systems. Here are the security risks and the controls that keep that power in check.
Evaluating an AI agent means checking outcomes, tool use and safety, before release and in production. Here is a practical way to do it.
Workflow orchestration coordinates many automated steps, systems, AI agents and people into one reliable process. Here is how it works and when you need it.