Reports, Benchmarks, and Guides for AI in Prod
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Consider these scenarios An MCP server quietly returning extra tool descriptions Prompt injection through a calendar invite An Agent invokes a tool that the principal should not have access to Cost overruns It isn't the model that failed. It isn't the tool that failed. What failed
53% of MCP servers use static API keys. Learn the four governance primitives your team needs to control shadow MCP, credentials, and audit trails in production.
Learn what an AI agent gateway is, how it differs from an LLM gateway, and why autonomous agents need centralized routing, guardrails, and observability.
AgentOps is the operational layer for AI agents in production. Learn what changes when agents replace single LLM calls, what breaks, and how to control autonomous systems.
GitHub Copilot best practices for enterprise teams: credential management, cost controls, guardrails, and provider fallbacks that scale.
The harness is converging. Context is what separates teams. A platform team's guide to owning Claude Code, Cursor, and Codex across your engineering org.
coding agents need context to be useful. skills registry lets teams author skills once, review and version them, and sync to claude code, cursor, and codex.
The first agent is almost always a success story. A well-scoped task, a small team, clear ownership, everyone watching it closely. It works. Leadership gets excited. More agents get approved. And somewhere between agent five and agent fifty, the wheels come off. This is not a warning about AI
n8n best practices for teams running AI workflows in production: credential management, cost controls, observability, guardrails, and provider fallbacks.
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