AI Orchestration Fundamentals: how to put AI inside data pipelines
Language models have become one more step in the pipeline. This Astronomer certification covers what changes when that step is an AI: context, tools and human evaluation.
by Gabriel Henrique · · 2 min read · Ler em português
I'm Gabriel Henrique, a data engineer, and I use Apache Airflow to orchestrate governance automations and pipelines. After the Airflow 3 Fundamentals and DAG Authoring certifications, I took AI Orchestration Fundamentals, also from Astronomer.
The subject is integrating AI into workflows and data pipelines with the same rigor we apply to any other task.
The four topics
- Multi-agent systems
- Context engineering
- Secure AI tool calling
- Human-in-the-Loop evaluations
Why orchestrate AI
A call to a language model fails, runs slow and sometimes answers wrong. Those are the same problems as any external API integration, and an orchestrator already solves several of them: retries, timeouts, scheduling, a log of every run and an alert when something breaks.
When the AI runs inside a DAG, every response is recorded next to the other steps. You can tell which input produced which output, and that is governance applied to AI.
Multi-agent systems
Instead of one giant prompt, the work is split among agents with defined roles: one classifies, another fetches context, another writes, another reviews. Each agent becomes easier to test. In an orchestrator, each agent can be a task with a clear input and output.
Context engineering
The model answers based on what it receives. Context engineering means deciding what goes into the context: which documents, which data, in what format and with how many tokens. For someone from data, it's a familiar selection and modeling problem, with the model as the consumer.
Secure tool calling
Once the model can call tools, like querying a table or triggering an action, the question becomes what it is allowed to do. The practices I take from this:
- Give each tool the least access possible.
- Validate parameters before executing.
- Require human approval for actions that change data.
- Log every call for auditing.
Human in the loop
For outputs that affect a customer or a business decision, a person reviews before the flow continues. Orchestration lets you pause the flow, wait for approval and resume. Human evaluations also become data to measure the system's quality over time.
Where this meets SQL
The other half of the story is where the data feeding these agents lives. I covered it in the SQL AI Developer Associate article, which goes through vector search and RAG inside the database.
My other studies and projects are on the portfolio of Gabriel Henrique, Data Engineer. I'm Gabriel Henrique, and I follow closely where data engineering meets AI.