Why production AI fragments
A demo is one completion. A production system retrieves private context, calls tools, and has to explain itself later. Those jobs usually land in different repositories: the prompt in one service, embeddings in another, tool credentials in a third. The person who wired them becomes the architecture. Every provider change reopens all three.
What a durable system separates
Separate knowledge from the model, the agent definition from any one SDK, and the record of a run from application logs. Knowledge answers “what was true.” The agent answers “what may this workflow do.” The trace answers “what happened.” Evaluation answers “was it good enough to ship the next change.” Mixing those four into a single prompt makes each of them uninspectable.
How to implement the seams
- Define agent capabilities—knowledge scope, instructions, tools, and policy—without embedding a provider SDK.
- Ingest knowledge with authority, freshness, and conflict signals so retrieval can be questioned.
- Route models by task and keep the comparison on the execution, not in a spreadsheet.
- Record every retrieval and tool call so the run can be replayed when something looks wrong.
How Obliq holds the architecture
The runtime follows Know, Build, Act, Observe, Improve. Knowledge is ingested and scoped. Agents bind instructions, an endpoint, tools, and policy. Tools include MCP, APIs, webhooks, and functions. Executions store the steps. Evaluation scores groundedness, instruction adherence, and tool reliability. Obliq owns those seams so your application logic can stay put.
How to keep the system when the model changes
Point the agent at the new endpoint. Replay a known input. Compare traces. Keep the knowledge scope and the tool grants unless the comparison says they are the problem. That is the test of the architecture: the model moved, and the workflow did not have to.
Questions
What is production AI architecture?
It is the set of seams around a model: knowledge, agent definition, tool access, an execution record, and evaluation. The model is one component. The architecture is what remains when you replace it.
Why do production AI systems fragment?
Teams connect a model, a vector store, an agent framework, and a handful of APIs as separate projects. Each new capability adds an operator. Nobody owns the trace of a single run.
How does Obliq fit this architecture?
Obliq holds the seams—knowledge pipeline, agent runtime, tool bus, traces, and evaluation—so application logic stays portable across model and protocol changes.