Karmaşık İş Akışları Tasarlama
Gelişmiş görevler için birden çok aracı, araç ve karar noktası içeren karmaşık otonom iş akışları tasarlayın.
Karmaşık İş Akışları Tasarlama, CoddyKit'te ücretsiz bir AI Agents with LangChain & Autonomous Workflows dersidir. Bu, 4 dersinin 1. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, AI Agents with LangChain & Autonomous Workflows öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. AI Agents with LangChain & Autonomous Workflows kursu toplamda 4 dersten oluşur.
Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.
Beyond Simple Linear Chains
So far, we've built agents that follow a straightforward path. But real-world tasks are rarely simple!
Complex workflows allow your AI agents to handle intricate problems by combining multiple steps, making decisions, and coordinating different AI capabilities.
The Workflow Conductor
Think of a complex task like writing a research paper. It involves many steps:
- Researching topics
- Finding relevant sources
- Summarizing information
- Drafting sections
- Reviewing and editing
Each step might need different tools or even different specialized agents. Orchestration is about making these steps work together seamlessly.
Building Blocks for Intricate Tasks
Designing complex workflows means thinking about how to combine:
- Agents: Specialized for different sub-tasks.
- Tools: External actions agents can perform.
- LLMs: The brain for reasoning and generation.
- Decision Points: Logic to choose the next step or agent.
- Memory/State: How information flows between steps.
These elements are combined to create powerful, multi-stage processes.
Guiding the Workflow Path with Routers
A crucial part of complex workflows is the decision point, often implemented using a router.
A router analyzes the current input or intermediate result and decides which path the workflow should take next. This allows for dynamic, adaptive behavior.
For example, if a user asks for 'weather,' route to a weather tool. If they ask for 'news,' route to a news agent.
Router in Action: A Simple Example
A router directs your workflow by making decisions based on the input. Here's a simple Python function that acts as a conceptual router, directing queries to different 'handlers' based on keywords.
Try changing the input query to see how it routes!
def route_query_to_handler(query: str) -> str:
"""
Simulates a router that directs a query to a specific handler
based on its content.
"""
query_lower = query.lower()
if "weather" in query_lower:
return f"Routing to Weather Tool for: '{query}'"
elif "news" in query_lower:
return f"Routing to News Agent for: '{query}'"
elif "calculate" in query_lower or "math" in query_lower:
return f"Routing to Calculator Tool for: '{query}'"
else:
return f"Routing to General LLM for: '{query}'"
# Main entry point for demonstration
if __name__ == "__main__":
print(route_query_to_handler("What's the weather like today?"))
print(route_query_to_handler("Tell me the latest tech news."))
print(route_query_to_handler("Can you calculate 5 + 7?"))
print(route_query_to_handler("Hello, how are you?"))Keeping Context Across Steps
In a complex workflow, you often need to carry information from one step to the next. This is called managing state or context.
For example, an agent might extract key entities from a document, and then a subsequent step uses those entities to perform a web search.
LangChain helps by allowing you to define how outputs of one runnable become inputs for the next, often through dictionaries or specific input/output schemas.
Agents Working Together
Complex problems can be broken down into smaller tasks, each handled by a specialized agent.
- An "Editor Agent" might refine text.
- A "Data Agent" might retrieve information.
- A "Planner Agent" might decide the overall sequence.
The workflow orchestrator ensures these agents receive the correct inputs and their outputs are correctly processed for the next stage.
Case Study: Research & Report Workflow
Let's design a workflow to generate a report on a given topic:
- Input: User provides a topic.
- Step 1 (Research Agent): Gathers relevant articles.
- Step 2 (Summarizer Agent): Processes findings, identifies key points.
- Step 3 (Drafting Agent): Writes an initial report draft.
- Step 4 (Reviewer Agent): Critiques the draft.
- Step 5 (Editor Agent): Applies improvements, finalizes report.
- Output: A polished report.
Mapping Out the Orchestration
When designing, it's helpful to visualize the flow. Imagine arrows connecting steps:
- User Query → Research Agent
- Research Agent Output → Summarizer Agent
- Summarizer Agent Output → Drafting Agent
- Drafting Agent Output → Reviewer Agent
- Reviewer Agent Feedback + Drafting Agent Output → Editor Agent
- Editor Agent Output → Final Report
Each arrow represents data flow and a potential decision point (implicitly, 'next step').
Workflow Decision Time
Consider a complex workflow designed to assist with customer support. It needs to either answer common FAQs or escalate to a human agent for complex issues.
Orchestrating Intelligence
You've learned how to think about designing complex AI agent workflows!
We covered the importance of orchestration, key components like routers for decision-making, managing state, and how multiple agents can collaborate on sophisticated tasks.
The ability to architect these intricate flows is key to building truly intelligent and autonomous applications.
Sıkça Sorulan Sorular
“Karmaşık İş Akışları Tasarlama” dersi ücretsiz mi?
Evet — “Karmaşık İş Akışları Tasarlama” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve AI Agents with LangChain & Autonomous Workflows kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. AI Agents with LangChain & Autonomous Workflows kursu toplamda 4 dersten oluşur.
“Karmaşık İş Akışları Tasarlama” dersinde ne öğreneceğim?
Gelişmiş görevler için birden çok aracı, araç ve karar noktası içeren karmaşık otonom iş akışları tasarlayın. AI Agents with LangChain & Autonomous Workflows ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.
AI Agents with LangChain & Autonomous Workflows öğrenmeye başlamak için deneyim gerekli mi?
Önceden deneyim gerekmez. CoddyKit'te AI Agents with LangChain & Autonomous Workflows, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 1. dersidir.
“Karmaşık İş Akışları Tasarlama” dersi ne kadar sürer?
Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.
Bu AI Agents with LangChain & Autonomous Workflows dersinde kod yazıp çalıştırabilir miyim?
Evet. Her AI Agents with LangChain & Autonomous Workflows dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.
Bu kursun tüm dersleri
- Karmaşık İş Akışları Tasarlama
- Asenkron Aracı Yürütme
- Hata Yönetimi ve Dayanıklılık
- İnsan Onaylı İş Akışları