AI Runtime Infrastructure

The layer of standardized orchestration fabrics, communication protocols, model gateways, and agent runtimes that sits between LLMs and the persistent S3-backed storage layer. The "control plane" of AI memory infrastructure — defining how reasoning engines discover, invoke, and coordinate the tools and resources stored in object storage.

As foundation models, retrieval engines, and persistent storage become distinct components, the integration surface between them needs standardization. The **Model Context Protocol (MCP)** has emerged as the de facto integration fabric — "USB-C for AI" — replacing brittle custom API connectors with a uniform JSON-RPC 2.0 interface. Agent runtimes (LangGraph) and model gateways (LiteLLM, Helicone, Traefik) provide the orchestration scaffolding that turns one-shot inference into durable autonomous workflows.

MCP server / client / host triad for discoverable tool integration with S3 resources, agent runtime state machines with S3 checkpoint persistence, model gateways with S3-backed semantic caching, prompt routing with audit-log persistence, FaaS-decomposed agentic workflows (Planner / Actor / Evaluator pattern).