Data Engineering - Astronomer: Can you trust your AI agent?
This is a guest post for the Computer Weekly Developer Network written by Vikram Koka, chief strategy officer at Astronomer.
Astronomer is the commercial developer of Apache Airflow, a community-driven open source tool that works to deliver data orchestration – Astronomer is a managed Airflow service that allows users to orchestrate workflows in a cloud environment.
Koka writes in full as follows…
When you give an AI agent autonomy, it has to do more than perform; it needs to explain its actions. Without that, how can you trust the decisions it makes or understand its failures? This demand for transparency isn’t new; it’s the same challenge we solved with data pipelines.
Airflow was built to give teams visibility into their data workflows.
It provided the tools to see what was happening, fix what was broken and understand why. That same foundation can and should be applied to AI agents and their workflows.
Data engineering use
Data engineering teams already use Airflow for AI-related tasks like tracking the path to production, validating data lineage and ensuring trust in results. Now, the focus is on extending that transparency to agent-based workflows.
But why do agentic workflows matter?
Agentic workflows are a step forward for AI systems. Unlike traditional automation, they’re built to incorporate decision-making and collaboration between multiple intelligent agents.
These workflows bring real advantages to solving complex problems:
- They pull from multiple knowledge sources, combining information in ways that single systems can’t.
- They support multi-agent setups, letting teams match the right model to the right task within a workflow.
- They handle multi-step operations, including retrieval, processing and validation, to ensure results are accurate and meaningful.
But with complexity comes risk. Without proper orchestration and visibility, these workflows can become unmanageable. That’s where Airflow comes in.
Powering agentic workflows
AI workflows often have dependencies between data sources, machine learning models and decision-making processes. If any part fails, it can create a ripple effect. Airflow provides the orchestration layer that keeps everything on track.
Here’s what it offers…
Transparency from start to finish: Airflow maps every step of the workflow, from model training to deployment, so teams know what’s happening at all times. That means data teams can trust i.e. it tracks data lineage and validates inputs to ensure that AI outputs are accurate and reliable. There are also real-time diagnostics i.e. when a workflow breaks, Airflow helps pinpoint the issue and provides tools to fix it quickly.
This level of control turns agentic workflows from a potential headache into a streamlined system.
Explainability isn’t optional
It’s not enough for an AI agent to deliver results. Teams need to know how it got there and what decisions it made along the way. This isn’t just about debugging; it’s about building systems that can operate autonomously while still being accountable. With the right orchestration tools, teams can move from reactive firefighting to proactive management.
They can use workflows that adjust in real-time, assign the right tasks to the right models and catch problems before they escalate. This isn’t just about improving AI performance – it’s about making AI workflows robust enough to trust in critical environments.
Building smarter, transparent systems
It’s not just about building smarter agents; it’s about making those agents accountable. By combining Airflow’s orchestration capabilities with agentic workflows, teams can take full advantage of what AI has to offer without losing visibility or control.
When workflows are clear and explainable, it’s easier to innovate, scale and solve problems that matter. Trust starts with transparency and transparency starts with the right tools.