Your Agent Is Not a Prototype
Your team's agent works. The executive brief on the third path to production: keep it, wrap it thin, and let the runtime do the rest.
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KDCube's point of view on changes shaping AI applications and infrastructure.
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The Second Project: Running an AI Agent for Real Users
Your agent works. The second project is the AI agent infrastructure around it — identity, spend, evidence, updates. A runtime takes it off your roadmap.
MCP Security Best Practices: The Plug Is Standard. The Permissions Are Yours.
MCP plugs your agents into the world’s tools — and your product into the world’s agents. The executive brief: who may, on whose authority, who pays.
MCP Security Architecture: Why Connecting AI Agents Is the Easy Part
MCP makes agent connectivity portable. The enterprise challenge is governing which agents receive which tools, whose authority crosses the endpoint, who pays, and what evidence remains.
Keep the Agent. Add the Runtime.
Your agent already works. Production should keep it — the framework, the behavior, the edge you built — and add the runtime around it.
Before Your AI Agent Acts: An Executive Brief on Governance
Seven plain questions that decide whether your AI agent governance is real — and what it means for each answer to be enforced, not written down.
How Companies Govern Agentic AI: From Policy To Runtime Enforcement
AI agent governance from policy to runtime enforcement: identity, authority, action policy, isolation, cost controls, and evidence working as one system.
AI Agent Infrastructure: The Runtime Around Your Framework
AI agent infrastructure beyond the agent loop: how a self-hosted runtime adds identity, cost controls, isolation, and deployment around your framework.