everything under the prompt. the part of ai engineering that is actually engineering.
a good demo is one layer deep. a product that holds is eight.
the screenshots that started this were a list of the hard parts: batching and quantization, structured output and tool contracts, agent budgets, retrieval and reranking, evals, observability, cost attribution, multi-tenant isolation, failure modes. that list is not trivia. it is the job description for shipping ai that does not fall over. so here it is as a stack you descend, and at every layer, where it already lives in what you build.
an agent without a stopping rule is a bug with a bill. guardrails are the gate, budgets are the closer, termination is the done-when. and when the good model is down, you route to a lesser one and degrade on purpose, instead of failing in the user's face.
this is the stratum the loop note already lives in. agent-stack encodes all of it: max_gate_failures is your closer, budget_cap and time_cap are your loop budgets, the coordinator routes and hands over. you shipped this layer.
the model returns text. your system needs a contract. validate the shape, and when it is malformed, repair it by re-asking with the error before you ever fall back. idempotency means calling the same tool twice does not double-charge or double-send.
your agents emit a red-list as structured json. trademind certs must be schema-valid to BS 7671 or they are worthless paper. the outreach closer must be idempotent: one reply per inbound, never two.
retrieval is not a vector search, it is a pipeline: chunk well, embed, search hybrid, rerank, keep it fresh. then prove it. can you measure recall, is the answer grounded in what you pulled, do the citations point at the right span.
evidence is this stratum: a sealed per-clinic rag with code-built pinpoint citations. agentdb is the memory tier under your agents. reasoningbank is retrieval over past experience.
this is the layer you mostly rent, not run, for now. but the tradeoffs are still yours. quantization buys speed and cheapness and can cost quality; latency, quality, cost and reliability move against each other and you pick the mix per feature.
your version of this is model tiering. opus for judgement, sonnet mid, haiku for routine, fable only for the heavy agentic legs because it runs at roughly twice the cost. that is you managing the inference tradeoff without ever touching a gpu.
if you cannot measure whether it got better or worse, you are not engineering, you are vibing. adversarial tests try to break it, an llm-as-judge scores at scale, a human checks the judge. the quiet killer is the silent regression: a change makes it worse and nothing tells you.
the logic-gap-harness is this stratum: an adversarial verifier that defaults to "not real" until it can confirm. verify_stack is the machine gate. you already build the eval before you trust the output.
trace every call, span the steps, watch drift over time. and attribute cost where it is spent: per feature, per tenant, per journey. per-model spend hides the one number you need, which clinic is quietly burning your margin.
sentinel, rebuilt as the coordinator, is this stratum. it watches drift, runs the reconciler, reports the five dials. strydeos is multi-tenant, so cost per clinic is a real number you should be able to pull on demand.
the moment you are multi-tenant, isolation stops being a feature and becomes the product. a prompt injection turns a helpful agent into an exfiltration tool. a shared cache leaks one user's context into another's. a missing boundary lets one tenant write another's data.
the check-staleness path lets any admin write any clinic. that is a cross-tenant write, exactly this stratum's worst failure. the identity split and the reconciler sit here too. sentinel already watches for injection.
everything above rests here. name the failure modes and engineer against each one by name. a hallucinated tool call needs a contract. malformed json needs validate-then-repair. stale retrieval needs freshness. a runaway agent needs the closer. a silent regression needs an eval. and know when your approach is the wrong tool: fine-tuning a model to fix a retrieval problem is the wrong tool.
you have the gate, the harness, the closer, the coordinator. the failure modes are already mapped to the layers that catch them. that mapping is what the stack is.
and you are already standing in it. strydeos is a multi-tenant ai product. evidence is a rag system. the coordinator is agent engineering. the harness is your eval discipline. the loop note was one stratum. this is the eight it sits inside.
the boundary stratum has a live cross-tenant write. that is not a learning task, it is a fix, and it outranks every shiny layer below it. scope the write, ship the reconciler, then descend the rest at the pace the fleet lets you.