Agentic Workflows Skill

Name: agentic-workflows
Rating: 92
Author: DeconvFFT

Build intelligent, observable, and resilient AI agent systems.

Architecture Decision Flow

code

New Agent System Request
           │
           ▼
┌──────────────────────────┐
│ Single task or multi-step?│
│ Single → Simple LLM call │
│ Multi-step → Agent loop  │
└──────────────────────────┘
           │
           ▼
┌──────────────────────────┐
│ Need multiple specialists?│
│ Yes → Multi-agent (A2A)  │
│ No → Single agent        │
└──────────────────────────┘
           │
           ▼
┌──────────────────────────┐
│ Long-running/resumable?   │
│ Yes → LangGraph + checkpoint│
│ No → Simple agent loop   │
└──────────────────────────┘
           │
           ▼
┌──────────────────────────┐
│ Need memory across sessions?│
│ Yes → Vector DB          │
│ No → In-session state    │
└──────────────────────────┘

Provider Selection

Provider	Best For	Streaming	Tools
Anthropic Claude	Complex reasoning, extended thinking	SSE	Native
OpenAI GPT-4	General purpose, function calling	SSE	Native
vLLM	Self-hosted, cost control	OpenAI-compatible	Via prompts

Quick Start Patterns

Anthropic Streaming with Tools

python

import anthropic

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-sonnet-4-5",
    max_tokens=4096,
    tools=[{"name": "search", "description": "Search the web", "input_schema": {...}}],
    messages=[{"role": "user", "content": "Research AI trends"}]
) as stream:
    for event in stream:
        if event.type == "content_block_delta":
            if hasattr(event.delta, "text"):
                print(event.delta.text, end="", flush=True)
            elif hasattr(event.delta, "thinking"):
                print(f"[Thinking] {event.delta.thinking}")

Structured Output with Pydantic

python

import instructor
from pydantic import BaseModel

class Analysis(BaseModel):
    summary: str
    confidence: float
    sources: list[str]

client = instructor.from_provider("anthropic/claude-sonnet-4-5")
result = client.create(
    response_model=Analysis,
    messages=[{"role": "user", "content": "Analyze market trends"}],
    max_retries=3
)

Reference Documentation

Task	Reference File
Anthropic/OpenAI/vLLM SDK patterns	references/llm-sdks.md
Multi-agent with A2A protocol	references/multi-agent.md
Streaming to UI (SSE/WebSocket)	references/streaming.md
Pydantic structured outputs	references/structured-outputs.md
Memory with vector DBs	references/memory.md
Checkpointing & resumption	references/checkpointing.md
Guardrails & anti-hallucination	references/guardrails.md

When to Use Multi-Agent

Scenario	Approach
Different expertise needed	Multi-agent with specialists
Verification required	Debate pattern (critic agent)
Complex workflow orchestration	Supervisor + workers
Simple tool use	Single agent with tools
Independent subtasks	Parallel agents

Production Checklist

• Structured outputs with Pydantic validation
• Retry logic with exponential backoff
• Streaming to UI for visibility
• Checkpointing for long-running workflows
• Guardrails for input/output validation
• Memory persistence (vector DB or KV store)
• Error handling with graceful degradation
• Observability (logging, tracing)