LiteLLM, RAGFlow, Kestra: Your AI Gateway Is a Control-Plane Target
AI gateway security after Microsoft's August findings: find exposed LiteLLM, RAGFlow and Kestra hosts in Azure, isolate them, rotate keys, detect abuse.
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Topic
9 articles
AI
Practical AI for people who have to run it, secure it, or pay for it. This category covers choosing between fine-tuning, RAG and prompt engineering, picking an agent framework, cutting token costs with prompt caching, and locking down Azure OpenAI with private endpoints, Entra ID authentication and content filtering. It also covers the failure modes: Copilots that surface data users should not see, and AI gateways that become control-plane targets. The posts assume you are putting AI features into an existing environment with real permissions, budgets and audit requirements.
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Securing Azure OpenAI for the Enterprise: Private Endpoints, Entra ID Auth, and Content Filtering
Azure OpenAI private endpoint security done right: lock down deployments with Private Link, Entra ID auth, and content filtering before production.
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Fine-Tuning vs RAG vs Prompt Engineering: Choosing the Right Approach for Enterprise AI Apps
Fine-tuning vs RAG vs prompt engineering compared: what each actually changes, honest cost profiles, and a decision framework for enterprise AI apps.
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Building an Internal Copilot Studio Agent: A Step-by-Step Guide for IT Help Desks
Build an internal Copilot Studio help desk agent end to end: knowledge sources, generative answers, Entra auth, Power Automate ticketing, Teams rollout.
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Prompt Caching with the Anthropic SDK: Cut Token Costs by 90%
Anthropic prompt caching turns repeated context into a 90% discount — here's the exact cache_control setup, math, and failure modes to check.
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LangGraph vs CrewAI: Choosing an Agent Framework in 2026
LangGraph vs CrewAI compared on control flow, debugging, and cost so you pick the right multi-agent framework the first time.
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Missing AI Guardrails: When Your Copilot Serves Data Users Shouldn’t See
Missing AI guardrails let users query their way into data they can't open directly. Close the gap with permission-trimmed retrieval and identity flow.
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Multi-Vendor LLM Orchestration: The Advocate/Adjudicator Pattern with GPT, Claude, and Gemini
Multi-provider LLM orchestration runs GPT, Claude, and Gemini as independent advocates, adjudicates one answer, and gates it — cutting single-model risk.
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Multi-Model AI Code Review in Claude Code: The Advocate/Adjudicator Pattern
Claude Code subagent orchestration fans a change out to rival AI models, adjudicates the best plan by rubric, and gates it behind passing tests.
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