LLM Workflow Patterns

Based on Anthropic's Building Effective Agents, which found that the most successful LLM implementations use simple, composable patterns rather than complex frameworks or specialized libraries.

The patterns here follow that principle: start with a single LLM call, and introduce structure only when the task requires it.

All seven examples use the same e-commerce dataset so the structural differences between patterns are easy to compare.

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What are agents?

Anthropic draws a line between two architectural categories:

• Workflows — LLMs orchestrated through predefined code paths
• Agents — LLMs that dynamically direct their own processes and tool usage

Patterns 01–04 and 06 in this repo are workflows. Pattern 05 (Orchestrator-Workers) is the closest to an agent: the orchestrator decides mid-run whether to continue, pivot, or stop, rather than following a fixed plan.

When (and when not) to use agents

1. Start simple. For many applications, a single well-prompted LLM call with good retrieval is enough. Only add structure when the task requires it.
2. Agents trade latency and cost for task performance. Use them when flexibility and model-driven decision-making are needed — not as a default.
3. Match the tool to the task. Workflows offer predictability for well-defined tasks; agents are better when the problem space can't be predetermined upfront.

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Decision Tree

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Patterns

File	Pattern	Task
01_workflow_single_call.ts	Single Call	Which product had the highest Q3 revenue, and why?
02_workflow_chain.ts	Prompt Chaining	Raw orders → daily totals → trend → executive one-liner
03_workflow_route.ts	Routing	Classify analyst query → dispatch to specialist (revenue / inventory / customer / anomaly)
04_workflow_parallelization_sectioning.ts	Parallelization — Sectioning	Analyze revenue by region, category, and segment simultaneously via Promise.all
04_workflow_parallelization_voting.ts	Parallelization — Voting	Three independent anomaly reviewers; surface only findings flagged by ≥2 reviewers
05_workflow_orchestrator_workers.ts	Orchestrator-Workers	Full Q3 business review — orchestrator plans subtasks, observes intermediate results, and re-plans across iterations (max 3) before synthesizing
06_workflow_evaluator_optimizer.ts	Evaluator-Optimizer	Generate a board-slide narrative, refine until it passes CFO-grade criteria (max 3 iterations)

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Dataset

Hardcoded synthetic Q3 2024 snapshot.

• 8 products across Electronics, Apparel, and Home & Kitchen
• 25 orders with orderId, date, customerId, productId, quantity, unitPrice, region
• 4 regions: North, South, East, West
• 10 customers with segment: Enterprise | SMB | Consumer

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Quick Start

npm install

cp .env.example .env
# add your OPENAI_API_KEY

npx tsx src/01_workflow_single_call.ts
npx tsx src/02_workflow_chain.ts
npx tsx src/03_workflow_route.ts
npx tsx src/04_workflow_parallelization_sectioning.ts
npx tsx src/04_workflow_parallelization_voting.ts
npx tsx src/05_workflow_orchestrator_workers.ts
npx tsx src/06_workflow_evaluator_optimizer.ts

# or via npm scripts
npm run 01
npm run 02
npm run 03
npm run 04-sectioning
npm run 04-voting
npm run 05
npm run 06

# validate all patterns against ground truth
npm run validate

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Architecture

openai.ts — Typed LLM calls

Every pattern imports llmCall<T>:

llmCall<T>(prompt: string, schema: ZodType<T>, system?: string, model?: string): Promise<T>

The model is instructed to return valid JSON, validated against the caller's Zod schema.

data.ts — Shared dataset

All seven pattern files import from the same orders, products, and customers arrays.

Self-contained files

Each pattern file imports only from openai.ts and data.ts. No cross-pattern dependencies.