# Stop Patching. Start Building: The Kong Context Mesh Stack > Source: > Published: 2026-07-23 20:51:00+00:00 Every prompt your agent sends, every response it receives — it flows through Kong's AI Gateway before it reaches your LLM. That's true whether you're routing to OpenAI, Anthropic, a hosted model on Vertex AI or Bedrock, or a self-hosted model running on your own inference infrastructure. This isn't middleware for middleware's sake. The AI Gateway does things your LLM provider can't: ****PII masking before prompts leave your perimeter.**** If an agent is reasoning over customer data, the raw PII gets masked before the prompt hits the model. The model sees anonymized context. The original data never leaves your environment — not to a cloud API, not to any external endpoint. ****Confidence threshold enforcement.**** If the model returns a low-confidence response below a threshold you set, Kong can route to a fallback model, trigger a human review workflow, or simply block the action. You decide what acceptable looks like — and that policy travels with you across providers and deployments. ****Semantic caching.**** Agents ask similar questions repeatedly. Kong recognizes semantically equivalent requests and serves cached responses — reducing latency, cutting LLM costs, and reducing load on your inference endpoints regardless of which model or provider serves them. ****Prompt injection detection.**** Adversarial inputs designed to manipulate agent behavior get caught at the gateway before they reach the model. ****Cost attribution and metering.**** Every token consumed, by every agent, attributed to the right team or workflow. When AI costs become significant at scale — and they will — you need this data. Especially if you're running across multiple providers or model tiers.