diff --git a/llm-inference-optimization/foundations.md b/llm-inference-optimization/foundations.md
index 53d4a9b..e09063d 100644
--- a/llm-inference-optimization/foundations.md
+++ b/llm-inference-optimization/foundations.md
@@ -4,53 +4,25 @@ Read before Lab 1. Assumes you know transformers, attention, and PyTorch; establ
 
 ## 1. The layer this module lives in
 
-```
-┌─────────────────────────────────────────────┐
-│ Agent applications (this repo's other modules)│  planning, tools, RAG, reflection
-├─────────────────────────────────────────────┤
-│ Orchestration: routing, fallbacks, tracing   │  consumes THIS module's evidence
-├─────────────────────────────────────────────┤
-│ ► Serving engine (vLLM): scheduler,          │  ◄ THIS MODULE
-│   continuous batching, PagedAttention,       │
-│   prefix cache, OpenAI-compatible API,       │
-│   /metrics                                   │
-├─────────────────────────────────────────────┤
-│ ► Model artifact: checkpoint + quantization  │  ◄ THIS MODULE
-├─────────────────────────────────────────────┤
-│ Kernels/runtime: CUDA graphs, FlashAttention,│  referenced, not authored here
-│ Marlin, tensor parallel                      │
-├─────────────────────────────────────────────┤
-│ Hardware: VRAM, bandwidth, tensor cores      │  the constraints everything obeys
-└─────────────────────────────────────────────┘
-```
-
-Training-time ML intuition transfers imperfectly here. Three shifts to internalize:
-
-1. **The bottleneck is memory bandwidth and capacity, not FLOPs.** Decode streams the entire weight matrix per token; VRAM left after weights bounds concurrency ([math §1–2](math-foundations.md)). GPU utilization % in `nvidia-smi` is routinely misleading for decode workloads.
-2. **The unit of performance is the distribution, not the mean.** Production questions are p95/p99 questions; a system can look fine on averages while violating every SLO.
-3. **Quality is a gate, not a metric to maximize.** Compression results are (memory Δ, speed Δ, quality Δ) triples; the third is checked against pre-registered acceptance criteria ([concepts.md#acceptance-criteria](concepts.md#acceptance-criteria)).
-
-## 2. Why an *agentic* engineer must own this layer
-
-Agents multiply inference: one user task fans into planning, tool selection, retrieval synthesis, code generation, reflection — 10–100× token amplification. Consequences:
-
-- **Cost is multiplicative.** A 30% serving-throughput gain is a 30% agent-fleet cost cut ([math §6](math-foundations.md#6-cost-and-fleet-sizing)).
-- **Latency compounds.** k chained calls compound their percentiles; per-call SLOs must be derived from the task SLO ([math §5](math-foundations.md#5-latency-slos-queueing)).
-- **Agent traffic is structurally cache-friendly.** Long shared prefixes (system prompt, tool schemas, growing history) make prefix caching disproportionately valuable — the module's strongest through-line ([concepts.md#prefix-caching](concepts.md#prefix-caching), [diagram D8](diagrams.md#d8-agent-workload--inference-optimization-map-module-thesis)).
-- **Quantization risk is agent-specific.** Benchmarks can hold while tool-call JSON fidelity degrades — the failure mode that actually breaks agents, hence structured-output checks in the eval suite.
-
-## 3. Prerequisites checklist
-
-You should already be comfortable with: transformer forward pass and attention shapes (`n_layers`, `n_heads`/`n_kv_heads`, `head_dim`); softmax attention cost intuition; Python + HTTP clients; reading Prometheus-style metrics (taught briefly in Lab 5 if not). You do **not** need CUDA programming, distributed training, or prior vLLM experience.
-
-## 4. The five questions this module teaches you to answer
-
-1. How much memory does this deployment need, and what concurrency does that buy? → Lab 1, [math §1](math-foundations.md#1-memory)
-2. Which quantization scheme, and how do I prove it didn't hurt? → Labs 3–4, 7
-3. At what load does this deployment stop meeting its SLO? → Lab 6, [D6](diagrams.md#d6-latencythroughput-frontier-and-the-slo-knee-lab-6-target-figure)
-4. Is this server memory-bound or compute-bound right now, and what's the fix? → Lab 5, [D7](diagrams.md#d7-memory-pressure-diagnostic-from-metrics)
-5. Given a GPU budget, an SLO, a quality floor, and agent traffic — what do I deploy? → Lab 8 capstone
-
-## 5. Module map
-
-Coverage and lab specs: [COVERAGE_GUIDE.md](COVERAGE_GUIDE.md). Tier selection: run `detect_tier.py` first; T0 (CPU-only) completes every learning objective via the mock server + audited result packs — a skipped real-tool step is by design, never a broken lab.
+```mermaid
+flowchart TB
+    A["Agent applications<br/>Planning · tool use · RAG · reflection"]
+    B["Orchestration layer<br/>Routing · fallbacks · tracing<br/>Consumes serving evidence"]
+    C["Serving engine — vLLM<br/>Scheduler · continuous batching · PagedAttention<br/>Prefix cache · OpenAI-compatible API · /metrics"]
+    D["Model artifact<br/>Checkpoint · quantization format · tokenizer"]
+    E["Kernels and runtime<br/>CUDA graphs · FlashAttention · Marlin · tensor parallelism"]
+    F["Hardware constraints<br/>VRAM · memory bandwidth · tensor cores"]
+
+    A --> B --> C --> D --> E --> F
+
+    classDef app fill:#eef6ff,stroke:#2563eb,stroke-width:1.5px,color:#111827;
+    classDef orchestration fill:#f5f3ff,stroke:#7c3aed,stroke-width:1.5px,color:#111827;
+    classDef module fill:#ecfdf5,stroke:#059669,stroke-width:2.5px,color:#111827;
+    classDef referenced fill:#fff7ed,stroke:#ea580c,stroke-width:1.5px,color:#111827;
+    classDef hardware fill:#fef2f2,stroke:#dc2626,stroke-width:1.5px,color:#111827;
+
+    class A app;
+    class B orchestration;
+    class C,D module;
+    class E referenced;
+    class F hardware;