Practical perspectives on deploying AI agents in production, MCP server architecture, and platform engineering for the agentic era.
Teams spend weeks choosing between LangChain and CrewAI. Then they spend months stuck trying to get their agent into production. The framework isn't the problem.
88% of organisations reported a confirmed or suspected AI agent security incident in the past year. Here's what's going wrong and how to fix it.
78% of enterprises are piloting AI agents. Only 14% have made it to production. Here's why the gap exists and how to close it.
Teams keep treating AI agents like microservices. It doesn't end well. Here is why your Kubernetes playbook needs a new chapter for agents.
The demo works. The prototype impresses. Then reality hits. Here is why deployment is where most AI agent projects actually break down, and what to do about it.
MCP servers are more than a protocol spec. In production, they solve the hardest integration problem in agent systems: secure, standardised tool access at scale.
Kubernetes is the obvious choice for running AI agents at scale. But without the right abstractions, it quickly becomes a maintenance burden that slows your team down.