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When selecting an orchestration tool for data engineering workflows, two leading platforms stand out: Apache Airflow and Dagster. Both tools excel at managing data pipelines, but they take fundamentally different approaches to orchestration. This comparison evaluates both tools across key metrics relevant to modern data engineering requirements.

: Task-centric orchestration focusing on workflow execution

Tasks represent discrete units of work (running scripts, queries, data transfers)

: Assets (data products produced by operations/ops)

: Data-centric approach emphasizing data dependencies and lineage

Assets represent data entities with explicit inputs, outputs, and metadata

: Dagster for data-centric workflows, Airflow for task-based orchestration

1. Data Tracking, Lineage, Dependency Visualization, and Quality Checks

: ✅ Basic task-level monitoring and logging

Requires integration with OpenLineage or custom solutions

Task dependencies are visible, but data dependencies are not explicit

Shows task dependencies but not data lineage

Requires integration with external tools (Great Expectations, dbt tests, etc.)

Must implement custom operators for validation

Automatic tracking of data asset dependencies and transformations

: ✅ Asset dependency graph with detailed metadata

Visual representation of data flows and asset relationships

: Dagster (significantly more robust out-of-the-box)

2. Data Definitions (Asset Specifications)

Data passing between tasks uses XComs (cross-communication)

XComs can be opaque and difficult to manage in complex workflows

Data definitions are implicit within task logic

No standardized framework for defining data schemas or specifications

✅ Software-defined assets as core concept

Support for asset observations and materializations

: Dagster (clear advantage with explicit asset modeling)

3. Isolation of Dependencies (Code Locations)

⚠️ Supports dependency isolation through:

Docker containers (KubernetesExecutor, DockerOperator)

Different executors (LocalExecutor, CeleryExecutor, KubernetesExecutor)

Requires manual configuration and maintenance

No standardized approach for modular deployments

Separate environments for different pipeline parts

Dagster vs Airflow: A Comprehensive Comparison for Data Orchestration

If you tinkered with AI already, you probably used a chatbot. You ask it a question, wait a second, and it gives you the answer. It’s impressive, but it is also limited. The entire field is now moving beyond just creating chatbots, into the world of 

: autonomous or semi-autonomous systems that can reason, plan and interact with tools in order to complete a task.

Let’s first start with what AI Agents are.

At their core AI Agents are LLMs. Traditional LLMs have limitations:

They cannot obtain information after the cutoff of their training

They cannot interact with external systems

That is what an AI Agent is: an LLM that can reason, plan and interact with the external systems using tool use.

This ability to reason, plan, and interact isn't a single feature. It's a "stack" of several distinct concepts and techniques that work together.

Let's break down the "must-know concepts" that give an AI agent its power. We can group them into how an agent 

How Agents "Think" (The Reasoning Engine)

At the core of an agent is its ability to reason. We are all already accustomed to how an LLM works. You give it a prompt and it gives you the answer. You don’t know how the LLM got to the answer and the LLM lacks the power to plan its steps. This is something that needed to be solved for AI Agents to become usable and reliable. The agents needed to:

Have ability to “think” in plain English (for debugging purposes)

A simple prompt-and-response is not enough. Agents need strategies to solve complex problems. This is where "thoughts" come in. There are 2 important concepts that enable agents to think: 

This is the foundational concept. Instead of just asking an LLM for the final answer, you prompt it to "think step-by-step." This simple instruction forces the model to lay out its logical progression. Let’s take an agent that I built as an example: recruiting agent. The goal of this agent is to extract all of the information from candidate resumes and generate a uniform format of the resume.

 the agent might just output: "Skills: Python." 

 the agent's internal monologue looks like this:"1. My goal is to extract skills from the provided text. 2. I should first scan the document for common section headers like 'Skills', 'Expertise', or 'Technical Proficiencies'. 3. I found a section titled 'Technical Proficiencies' on page 2. 4. Now, I will read the bullet points under this section and list them. 5. The skills are: Python, React, SQL, and AWS."

This is the next evolution. CoT follows a 

 step-by-step path. But what if that path is a dead end? Resumes are messy—what if the "Skills" section is poorly formatted?ToT allows the model to explore 

 different paths at the same time, like branches on a tree.

 job listed under the 'Experience' header. I've extracted 'Software Engineer at OldJob Inc, 2018-2020'."

 "I'll scan all job entries, extract their date ranges, and look for the one with an end date of 'Present' or the latest year. I found 'Senior Engineer at NewJob Corp, 2020-Present'."

 "I'll look at the candidate's 'Summary' at the top of the resume. It says, 'Senior Engineer with 5 years of experience...' This matches Branch B."

A ToT-enabled agent can evaluate all three "thoughts," conclude that Branch A is incorrect, and confidently proceed with the correct information from Branch B.

This is how our AI agents can “think”, let’s now explore how they can act.

Thinking is important, it shows that our agents can really understand the problems at hand and solve them, not only that, but that they can also tell us the thought process so we can correct them. Although it is only half of the story, our agents also need to be able to act, or more specifically, to use the tools.

A tool gives the agent access to external systems like:

To be useful, our resume agent must be able to 

 on its thoughts and interact with the digital world. This is where it goes from a "thinker" to a "doer."

It's the bridge to the outside world. We give our LLM a list of tools (which are just our own code functions) that it can call.

By itself, a Large Language Model can only process and generate text; it cannot check the weather, browse a website, or save a file. Function calling solves this by providing the LLM with a "toolbox" of predefined code functions (like get_weather(location) or save_resume_data(data)). When the LLM's reasoning determines it needs external information, it doesn't try to 

 the code itself. Instead, it outputs a structured JSON object that essentially says, "I need to use the get_weather tool with the location set to 'Novi Sad'." Your application then executes this real function, gets the real-time weather, and feeds that data back to the LLM to continue the conversation.

read_pdf_text(file_path): A tool that takes a PDF and returns its raw text.

extract_structured_info(text, schema): A powerful tool that takes the raw text and a schema (e.g., {skills: [], projects: []}) and uses the LLM to "fill in the blanks."

save_to_database(candidate_data): A tool to save the extracted JSON object to our company database.

generate_uniform_resume(data, template_id): The final tool that takes the structured data and generates the new, client-ready resume.

 to ask for it by outputting a special JSON command.

Function calling and tool use is very impressive when paired with our next concept.

This is the heartbeat of the agent. 'ReAct' stands for 

 and it provides the fundamental loop that connects thinking (CoT) and doing (Function Calling). Instead of trying to solve a complex problem in one go, ReAct teaches the agent to work in a continuous cycle. First, the agent 

 about the problem (e.g., "The user wants the weather, I need to find the right tool"). Then, it 

 by choosing a tool (like get_weather) or forming a response. Finally, it 

 the result of its action (e.g., "The weather is 30°C"). This observation feeds back into its next "Reasoning" step, allowing it to plan, self-correct, and take a series of logical actions until the final goal is complete.

 in action, handling one new resume using the ReAct loop:

 "The user has uploaded 'candidate_resume_v4.pdf'. My first step is to read the text from this file."

 call tool: read_pdf_text(file_path='candidate_resume_v4.pdf')

 (The tool runs and returns 2 pages of raw, jumbled text.)

 "I have the raw text. It's messy, but I can see sections for skills and experience. I need to use my extraction tool to get the structured data."

 call tool: extract_structured_info(text='...', schema={...})

 (The tool runs and returns a clean JSON object: {name: 'Jane Doe', skills: ['JavaScript', 'LangGraph', 'Python'], ...})

 "The extraction was successful! The data is clean. Now I must save this to our central database so the candidate is in our system."

 call tool: save_to_database(candidate_data={...})

 (The tool runs and returns: {success: true, candidate_id: 'c-456'})

 "The candidate is saved. The final task is to generate the standardized resume for the client using our 'modern' template."

 call tool: generate_uniform_resume(data={...}, template_id='modern')

 (The tool runs and returns: {file_path: 'client_resume_jane_doe.pdf'})

 "The final file has been generated. My work is complete."

 (Respond to user: "The resume for Jane Doe has been processed and the new file is ready.")

This "Thought-Action-Observation" loop is what allows an agent to complete a complex task that a simple chatbot never could.

This is how we bridge the gap. By combining sophisticated reasoning patterns like 

, we fundamentally change what an AI is capable of.

We're no longer limited to a simple chatbot that just answers questions. We now have the building blocks for a true collaborator, an agent that can understand a complex, multi-step goal, plan a course of action, interact with external tools, and see a task through to completion. As we saw with the AI Recruiter example, these concepts are what allow an agent to take a messy PDF and turn it into a perfectly formatted, database-ready entry, all on its own.

And this is just the beginning. Concepts like 

 are already building on this foundation, creating systems that can learn from past interactions and work in teams. The shift from chatbot to agent isn't just an upgrade; it's the start of an entirely new way to build and interact with software.

If you tinkered with AI already, you probably used a chatbot. You ask it a question, wait a second, and it gives you the answer. It’s impressive, but it is also limited. The entire field is now moving beyond just creating chatbots, into the world of AI Agents: autonomous or semi-autonomous systems that can reason, plan and interact with tools in order to complete a task.

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Dagster vs Airflow: A Comprehensive Comparison for Data Orchestration

AI Agents: From chatbots to collaborators