How to secure MCP servers in production. Transport security (stdio vs SSE vs WebSocket), OAuth authentication flows, API key management, request validation, rate limiting, and production deployment patterns for MCP.

How to secure tool access for AI agents in production. Least privilege principles, MCP server scoping, tool call validation, parameter whitelisting, credential management, and production patterns for securing agent tool access.

A deep dive into prompt injection attacks against AI agents in 2026. Direct injection, indirect injection, jailbreaking, and the defense patterns that actually work in production — input guardrails, output validation, tool sandboxing, and prompt hardening.

Real-world production patterns for prompt engineering in 2026. Banking compliance prompts, SaaS onboarding flows, dev tool agent instructions, and the 12 most common anti-patterns to avoid.

How to test prompts in production. LLM-as-judge evaluation, prompt versioning strategies, A/B testing for prompts, regression testing pipelines, and automated evaluation frameworks for AI agents.

Production patterns for structured prompting. How to use XML tags, JSON schemas, Pydantic output types, and chain-of-thought to get reliable, parseable output from AI agents in 2026.

Your MCP tool definitions are prompts too. How tool names, descriptions, input schemas, and error messages influence agent decision-making. Production patterns for writing tools that steer agent behavior.

How prompt engineering has evolved in the agent era. Compare system prompts, Kiro steering files, Claude Code .claude.md rules, Cursor .cursorrules, and Agent SDK instructions. Production patterns for each approach.