Tung Nguyen / Independent AI engineer
Make your AI
workflow easier
to run.
From unclear processes to agent harnesses and operating guardrails. Practical engineering, starting with one workflow.
Services
Where is the workflow getting stuck?
Three ways I help teams move from brittle demos to systems they can run.
The process is
hard to explain.
Map the workflow and identify a safe first build.
The demo breaks
on real inputs.
Add typed outputs, validation, repair paths,
and clear handoffs.
Failures are
hard to see.
Make failures visible and strengthen
operating guardrails.
Featured work
Recorded showcase
Engineering the Agent Loop
A custom harness with host-owned completion.
20 records validated, saved, and independently verified from one fixed test page.
Watch the showcaseEdited walkthrough of a retained run. Broader generalization is future work.
Next step
Bring one workflow.
Find a practical next step.
About the practice
Close to the work.
Clear about the limits.
The work stays close to the point where a model’s answer becomes someone else’s work. That means tracing the real workflow, making outputs explicit, testing the edges, and designing a useful handoff when the system is uncertain.
The goal is not autonomy for its own sake. It is a system your team can operate, question, and improve without pretending the hard cases disappeared.
Explore workflow examples
Field notes
Follow the practical work.
Occasional notes on agent reliability, document-heavy operations, evidence boundaries, and the decisions that keep AI useful.
Read the notes