Tag: model serving

This is a useful operational map: the model produces outputs, the inference service runs and meters it, the harness assembles context and tools, and the complete agent system turns those pieces into behavior. The distinction matters most during diagnosis. Missing context, unavailable tools, malformed tool calls, slow inference, and weak reasoning can look like one vague "AI problem" while belonging to different owners and requiring different fixes.

The boundaries are not laws of nature. Products bundle layers, vendors reuse terms, and even experienced teams disagree about how much orchestration is required before a tool becomes an agent or a harness. So this vocabulary should be treated as a troubleshooting interface, not a universal ontology. If naming a layer helps route a defect, compare two systems fairly, or avoid replacing a model when the real problem is context assembly, it has earned its keep. If it only creates a taxonomy argument, it has become another abstraction to maintain.

This is the kind of platform link that turns model serving into an operations conversation. NIM, GKE, and operators are not glamorous pieces, but they are the difference between "we can run a model" and "we can run the model with repeatable infrastructure."

The caution is that Kubernetes can make a simple model deployment expensive in both money and attention. It earns its place when repeatability, policy, and scale matter.

This is useful because it connects agent adoption to inference architecture. Agents do not make one call; they fan out across planning, retrieval, tool use, retries, and evaluation, which changes the serving math quickly.

Worth keeping as a scale reference for Google Cloud AI work. The product question is where inference cost becomes a feature constraint rather than a backend detail.