agent-workflow-designer

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# Agent Workflow Designer

**Tier:** POWERFUL
**Category:** Engineering
**Domain:** Multi-Agent Systems / AI Orchestration

---

## Overview

Design production-grade multi-agent orchestration systems. Covers five core patterns (sequential pipeline, parallel fan-out/fan-in, hierarchical delegation, event-driven, consensus), platform-specific implementations, handoff protocols, state management, error recovery, context window budgeting, and cost optimization.

---

## Core Capabilities

- Pattern selection guide for any orchestration requirement
- Handoff protocol templates (structured context passing)
- State management patterns for multi-agent workflows
- Error recovery and retry strategies
- Context window budget management
- Cost optimization strategies per platform
- Platform-specific configs: Claude Code Agent Teams, OpenClaw, CrewAI, AutoGen

---

## When to Use

- Building a multi-step AI pipeline that exceeds one agent's context capacity
- Parallelizing research, generation, or analysis tasks for speed
- Creating specialist agents with defined roles and handoff contracts
- Designing fault-tolerant AI workflows for production

---

## Pattern Selection Guide

```
Is the task sequential (each step needs previous output)?
  YES → Sequential Pipeline
  NO  → Can tasks run in parallel?
          YES → Parallel Fan-out/Fan-in
          NO  → Is there a hierarchy of decisions?
                  YES → Hierarchical Delegation
                  NO  → Is it event-triggered?
                          YES → Event-Driven
                          NO  → Need consensus/validation?
                                  YES → Consensus Pattern
```

---

## Pattern 1: Sequential Pipeline

**Use when:** Each step depends on the previous output. Research → Draft → Review → Polish.

```python
# sequential_pipeline.py
from dataclasses import dataclass
from typing import Callable, Any
import anthropic

@dataclass
class PipelineStage:
    name: "str"
    system_prompt: str
    input_key: str      # what to take from state
    output_key: str     # what to write to state
    model: str = "claude-3-5-sonnet-20241022"
    max_tokens: int = 2048

class SequentialPipeline:
    def __init__(self, stages: list[PipelineStage]):
        self.stages = stages
        self.client = anthropic.Anthropic()

    def run(self, initial_input: str) -> dict:
        state = {"input": initial_input}

        for stage in self.stages:
            print(f"[{stage.name}] Processing...")

            stage_input = state.get(stage.input_key, "")

            response = self.client.messages.create(
                model=stage.model,
                max_tokens=stage.max_tokens,
                system=stage.system_prompt,
                messages=[{"role": "user", "content": stage_input}],
            )

            state[stage.output_key] = response.content[0].text
            state[f"{stage.name}_tokens"] = response.usage.input_tokens + response.usage.output_tokens

            print(f"[{stage.name}] Done. Tokens: {state[f'{stage.name}_tokens']}")

        return state

# Example: Blog post pipeline
pipeline = SequentialPipeline([
    PipelineStage(
        name="researcher",
        system_prompt="You are a research specialist. Given a topic, produce a structured research brief with: key facts, statistics, expert perspectives, and controversy points.",
        input_key="input",
        output_key="research",
    ),
    PipelineStage(
        name="writer",
        system_prompt="You are a senior content writer. Using the research provided, write a compelling 800-word blog post with a clear hook, 3 main sections, and a strong CTA.",
        input_key="research",
        output_key="draft",
    ),
    PipelineStage(
        name="editor",
        system_prompt="You are a copy editor. Review the draft for: clarity, flow, grammar, and SEO. Return the improved version only, no commentary.",
        input_key="draft",
        output_key="final",
    ),
])

From fernandogarzaaa/OpenChimera_v1, MIT.

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