Definition
The AI supply chain is the network of people, artifacts, services, and infrastructure whose integrity can change an AI system's behavior or authority. It includes training and retrieval data, labels, models and weights, prompts, skills, tools, schemas, agent packages, libraries, containers, registries, APIs, model providers, evaluators, deployment systems, and the organizations maintaining them.
This chain is behavioral as well as executable. A poisoned document, changed system prompt, deceptive tool description, compromised skill, or silent provider update can alter outcomes without changing application source code. Credentials and identity mappings also belong in the inventory because a harmless-looking component can become dangerous when granted broad authority.
Manage composition and change
Record origin, version, integrity, owner, rights, dependencies, update path, permissions, evaluation status, and deployment reach for material components. Pinning a hash helps only when the thing can be pinned; hosted models and APIs require change monitoring and regression evaluation. Track transitive dependencies and preserve execution lineage so a later compromise can be traced to affected outcomes.
Distinguish it from nearby terms
A software supply chain centers on source, packages, builds, and distribution. A data supply chain tracks collection and transformation. The AI supply chain joins those with models, behavioral context, evaluation artifacts, and external agent capabilities. Agent supply chain is a narrower emphasis on tools, skills, protocols, and delegated agents.
Check your understanding
Your application commit is unchanged, but an installed skill updates its instructions and an MCP server adds a tool. Did the supply chain change? Yes. Reassess provenance, authority, tests, and affected deployments before treating behavior as the same version.