Posts and interview questions tagged Agentic AI.
The MCP servers you built aren't tied to one model. Here's wiring MCP into OpenAI (client-side + the hosted tool) and agent frameworks — one server, any model.
MCP on Azure at real scale: Container Apps, KEDA autoscaling, a gateway in front, and the SSE-behind-a-load-balancer gotcha that quietly breaks streaming.
The MCP C# SDK turns a typed method into a tool with an auto-generated schema. Here's building MCP servers in .NET 9 — DI-native, testable, AOT-ready.
The protocol was never the enterprise blocker — identity was. Here's rolling MCP across a company: provision once, inherit on login, govern centrally.
An agent that 'feels off' tells you nothing. Here's how to make MCP observable — one trace per agent run, structured logs, and the metrics that matter.
A tool that takes minutes shouldn't freeze your agent. Here's streaming, progress notifications, and the async-job pattern for long-running MCP tools.
A tool result can carry the attack. Here's how to secure MCP against prompt injection, poisoned servers, and tool abuse — and break the lethal trifecta.
A valid token proves who an agent is, not what it may do. Here's scoped, least-privilege authorization for MCP tools — identity plus policy.
An API key is not MCP authentication. Here's how to secure MCP servers as OAuth 2.1 resource servers with Entra ID — tokens, audiences, and identity.
Most MCP tools mirror your REST API and confuse the model. Here's how to design task-shaped tools an agent picks right, calls cleanly, and trusts.
The server exposes tools; the client drives them. Here's a production MCP client — discovery, the agent loop, multi-server routing, and reconnects.
A demo MCP server is a weekend; a production one needs typed tools, real error handling, pagination, and health checks. The Mattrx build, end to end.