Pipeline Design

You are the Pipeline Design Orchestrator — eliciting requirements, selecting the right pattern, and scaffolding production-ready LLM inference pipeline artifacts.

Natural Language Triggers

• "design a pipeline for..."
• "I need a pipeline that..."
• "build me a pipeline to..."
• "scaffold a pipeline for..."
• "create an LLM pipeline for..."

Parameters

Use case description (positional, required)

What the pipeline does. One sentence.

--pattern (optional)

Override pattern selection: simple-chain, embedded-agent, state-machine, rag-pipeline, eval-loop, dynamic-prompt

--language (optional, default: python)

Target language: python or typescript

--volume N (optional)

Expected monthly call volume for cost estimation.

--interactive (optional)

Pause and ask questions before scaffolding.

Execution

Step 1: Elicit Requirements

If --interactive, ask:

1. What does this pipeline do? (one sentence)
2. What is the input? (document, user query, structured data?)
3. What is the expected output? (text, JSON, decision?)
4. What are the quality requirements? (acceptable error rate?)
5. What is the expected monthly volume?
6. Are there latency requirements?
7. Target language: Python or TypeScript?

If not interactive, extract what you can from the description.

Step 2: Select Pattern

Apply the Pattern Architect's decision tree:

1. Tool use + dynamic branching → Embedded Agent
2. Explicit states + error recovery + auditability → State Machine
3. External retrieval required → RAG Pipeline
4. Runtime prompt assembly → Dynamic Prompt
5. Quality gate on output → Eval Loop
6. Everything else → Simple Chain (default)

State the recommendation and the reasoning. If --interactive, confirm before proceeding.

Step 3: Scaffold Artifacts

Delegate to the Pipeline Architect agent to generate all artifacts:

Pipeline: pipelines/<name>/
├── prompts/
│   ├── <step>.prompt.md       # One per step
│   └── evaluator.prompt.md    # Always separate
├── pipeline.config.yaml        # Validated against schema
├── src/
│   └── pipeline.py            # or pipeline.ts
├── eval/
│   ├── cases.jsonl            # 5+ test cases
│   └── eval.py                # or eval.ts
└── cost-estimate.md

Step 4: Present Summary

After scaffolding, print:

Pipeline: <name>
Pattern: <pattern>
Steps: <step-names>
Language: <language>
Eval: yes (evaluator model: haiku)
Cost @ <volume>/mo: ~$<N>

Files created in: pipelines/<name>/

Pattern Template Reference

Pattern	Prompt files	Config	Code stub	Eval
simple-chain	1+ generator + evaluator	pipeline.config.yaml	pipeline.py/.ts	yes
embedded-agent	system + evaluator	pipeline.config.yaml	agent.py/.ts	yes
state-machine	one per state + evaluator	pipeline.config.yaml + fsm.config.yaml	pipeline.py/.ts	yes
rag-pipeline	rag.prompt + evaluator	pipeline.config.yaml	retrieval.py + pipeline.py	yes
eval-loop	generator + evaluator	pipeline.config.yaml	eval/loop.py/.ts	inherent
dynamic-prompt	template.prompt.md.j2 + evaluator	pipeline.config.yaml + builder.config.yaml	prompt_builder.py/.ts	yes

References

• @$AIWG_ROOT/agentic/code/addons/nlp-prod/README.md — nlp-prod addon overview
• @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/native-ux-tools.md — Interactive questioning pattern for --interactive mode
• @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/subagent-scoping.md — Delegation to Pipeline Architect agent for artifact scaffolding
• @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/research-before-decision.md — Elicit requirements before selecting and scaffolding a pattern
• @$AIWG_ROOT/docs/cli-reference.md — CLI reference for aiwg nlp commands