# Quality & Degradation Guardrails (Portfolio View)

**Purpose:** explain how I prevent agentic workflows from “degrading” (context collapse, drift, compounding errors) in an LLM-driven, multi-agent architecture.

This is CEO-readable *and* technically credible.

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## 1) What “degradation” means in real systems
- **Context collapse:** early constraints get diluted or forgotten.
- **Spec drift:** later outputs diverge from the PRD/definition of done.
- **Compounding errors:** one bad step contaminates downstream steps.
- **Retrieval contamination:** “kinda relevant” context becomes assumed truth.
- **Prompt rot:** orchestration instructions slowly become inconsistent.

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## 2) My core stance
### The model is not the source of truth.
I treat LLM calls as **stateless transforms** over:
- a versioned spec,
- a persisted state object (in a DB/service),
- and a small, step-scoped context window.

If the model “forgets,” the system doesn’t—because the system’s truth lives outside the model.

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## 3) Guardrail stack (what I do today)

### 3.1 Stage gating (pipeline discipline)
- Build runs in discrete stages (Plan → Spec → UI → Code → Deploy).
- Each stage produces an artifact.
- No stage proceeds without passing the checks for that stage.

**Why it helps:** reduces hidden drift and makes errors local, not viral.

### 3.2 Contracts + schema validation
- Sub-agent outputs are structured (JSON/YAML).
- Validate required fields before progressing.
- On failure: retry with constraint reminder → escalate to HITL.

**Why it helps:** prevents “pretty text” from masquerading as a usable result.

### 3.3 Externalized truth (persisted state in a DB)
Instead of relying on chat history as “memory,” I persist the system’s truth in a database (serverless, SaaS, or local DB depending on the build).

What gets persisted:
- **SpecCore:** goal, constraints, definition of done (stable “north star”)
- **Working state:** key decisions + chosen options (what we currently believe)
- **Stage outputs:** plan/spec/UI decisions/code notes/deploy checks (artifact trail)
- **Version pointers:** orchestrator version, PRD version, map version (what generated what)

**Why it helps:** prevents context loss across long runs, handoffs, or agent retries.

### 3.4 Context layering (no giant convo dumps)
Instead of shoving huge chats into every call:
- **SpecCore** is always present (small, stable).
- **Working state** is summarized + referenced from the DB.
- **Task slice** is only what’s needed for this step.

**Why it helps:** avoids overload *and* protects critical constraints from dilution.

### 3.5 HITL at high-risk gates (your current practice)
- Human review where it matters:
  - final PRD approval
  - architecture signoff
  - deployment step validation
  - external-facing outputs

**Why it helps:** keeps risk low without slowing the whole pipeline.

### 3.6 Sandbox-first testing + spot checks
- New logic is tested in a sandbox with known inputs.
- “Spot checks” verify the system didn’t drift between iterations.

**Why it helps:** catches regressions early, before they ship.

### 3.7 Traceability (so you can replay what happened)
- Each run/stage is tagged with a **trace ID**.
- Stage outputs are stored as records so you can reconstruct:
  - what inputs were used,
  - which orchestrator version ran,
  - what the agent produced,
  - and what was approved.

**Why it helps:** debugging is deterministic, and “AI made it up” becomes “here’s the exact step that broke.”

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## 4) Upgrades (easy next steps if/when desired)

### 4.1 Golden test cases (micro-evals)
Maintain 3–10 known inputs with expected outputs.
Run them after changes to detect degradation.

### 4.2 Output diffs + change logs
Auto-generate “what changed and why” between versions of artifacts.

### 4.3 Artifact hashing
Hash stage artifacts so you can prove:
- the PRD used for a build,
- the orchestration version that generated outputs,
- and whether anything changed post-run.

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## 5) Executive summary (copy-paste for a CEO)
I prevent degradation by treating AI like a pipeline, not a chat:
- AI outputs are **validated**, not trusted by default.
- System truth lives in a **persisted state store** (DB), not in model “memory.”
- I run **stage gates**, do **sandbox testing**, and use **HITL** where risk is highest.
- I keep the system auditable with **trace IDs** and stored artifacts—so context doesn’t silently collapse over time.

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## 6) What this portfolio proves (without exposing NDA work)
- I can design **agentic workflows that remain stable** over long, multi-step runs.
- I don’t rely on “bigger prompts” as a strategy; I rely on **state, contracts, and checkpoints**.
- I can create an NDA-safe “golden demo” that shows the same architecture + guardrails in miniature.