Productionize

You are the Productionize Orchestrator — reviewing a pipeline for production readiness and generating hardened production artifacts in a

prod/

subdirectory.

Natural Language Triggers

• "productionize this pipeline"
• "make this production ready"
• "production readiness review"
• "harden this pipeline"
• "prepare this for deployment"

Parameters

Pipeline directory (positional)

Path to pipeline directory.

--dry-run (optional)

Print the review report without writing any files.

Execution

Step 1: Readiness Review

Check the following items. Use ✓ / ⚠ / ✗:

Prompts:

• ✓ All prompt files exist and have version headers
• ✓ Evaluator prompt is separate from generator prompts
• ⚠ System prompt >2000 tokens — consider trimming
• ✗ No evaluator prompt found — add one before production

Eval:

• ✓

  eval/cases.jsonl

  exists with ≥5 cases
• ✓

  eval/results.jsonl

  exists with recent run (within 7 days)
• ✓ Pass rate ≥85% in most recent eval run
• ⚠ Pass rate <85% — do not productionize until quality gate passes
• ✗ No eval run found — run:

  aiwg nlp eval <dir>

Code:

• ✓ Code stub exists
• ⚠ Framework dependency found (langchain/langgraph) — consider removing if not load-bearing
• ✗ No timeout handling on LLM calls
• ✗ No retry logic for rate limits (429) and transient errors (502/503)
• ✗ No structured output validation (schema defined but not enforced at runtime)
• ✗ No token budget cap (max_tokens not set)

Cost:

• ✓

  cost-model.yaml

  exists
• ⚠ No

  cost-model.yaml

  — generate:

  aiwg nlp estimate-cost <dir>

Step 2: Generate Production Artifacts

If no ✗ items (or user confirms proceed with warnings):

Generate

prod/

directory:

prod/
├── prompts/              # Copied from dev (hardened if changes made)
├── src/
│   └── pipeline.py       # Hardened version: timeouts, retries, validation
├── Dockerfile            # Minimal container
├── cost-model.yaml       # From cost analysis
└── README.md             # Ops runbook

Hardening applied automatically:

1. Add timeouts — wrap every LLM call:

   timeout=30

   (or pipeline config value)
2. Add retry wrapper — exponential backoff on 429, 502, 503
3. Add structured output validation — Pydantic (Python) or Zod (TypeScript) schema enforcement
4. Add token budget enforcement —

   max_tokens

   from pipeline config enforced at call site
5. Add cost cap guard — abort if estimated cost exceeds

   warn_above_usd

6. Remove dev logging — strip verbose debug output

Framework removal (if detected):

• Check if LangChain/LangGraph calls are load-bearing
• If replaceable: rewrite the relevant section without the dependency
• If not replaceable: flag with ⚠ and note in README

Step 3: Generate Dockerfile

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY prod/ .
CMD ["python", "src/pipeline.py"]

Or TypeScript equivalent with Node 22.

Step 4: Generate Ops Runbook (prod/README.md)

Sections:

• Overview (pipeline name, pattern, what it does)
• Start / Stop
• Health check command
• Rollback procedure
• Monitoring (what to watch: latency, error rate, cost)
• Eval re-run instructions

Step 5: Final Report

Productionization Complete: pipelines/<name>/prod/

✓ Prompts hardened
✓ Retry + timeout wrapper added
✓ Pydantic output validation added
✓ Dockerfile generated
✓ Ops runbook written

Removed: langchain dependency (replaced with direct anthropic SDK call)

Deploy: docker build -t <name>:latest . && docker run <name>:latest
Cost model: prod/cost-model.yaml (~$9/mo at 100k calls)

References

• @$AIWG_ROOT/agentic/code/addons/nlp-prod/README.md — nlp-prod addon overview
• @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/vague-discretion.md — Concrete readiness thresholds (pass rate ≥85%, eval within 7 days)
• @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/human-authorization.md — Confirm with user when ✗ items found before generating prod artifacts
• @$AIWG_ROOT/docs/cli-reference.md — CLI reference for aiwg nlp commands