Houman Azimi-Nejadi
Founder, Barg Labs
I’m building Barg Labs to explore a question that feels increasingly important:
What does it look like for machines to operate inside markets responsibly?
Artificial intelligence is rapidly expanding what parts of complex workflows can be automated. But most real-world systems — especially markets — still lack the infrastructure required to safely operate autonomous systems.
Background
I have spent more than 25 years working as a software engineer, building and operating complex software systems across multiple domains.
Over the past several years my work has increasingly focused on artificial intelligence and machine learning. I completed professional certification in AI and ML through Imperial College London and have been exploring how these technologies behave in real-world operational systems rather than purely research environments.
Through this work I became increasingly skeptical of much of the narrative around AI trading. Many systems marketed as AI-driven trading rely on thin abstractions, opaque models, or automation without meaningful operational discipline.
Why Barg Labs
Barg Labs emerged from the belief that the next generation of intelligent systems in markets will require more than better models.
They will require infrastructure — systems that allow machine reasoning and automation to operate inside explicit risk boundaries, operational controls, and observable workflows.
Instead of pursuing full autonomy, the focus is on governed agentic systems — systems that can reason, act, and adapt while remaining auditable, controllable, and constrained by policy.
Systems built so far
Operational proof before scale.
A live-capable prediction market runtime supporting ingestion, evaluation, sizing logic, execution, monitoring, and rollout systems.
A second FX runtime with signal generation, Kelly sizing logic, and execution infrastructure.
Operational infrastructure including canary deployments, shadow evaluation paths, monitoring systems, and rollback controls.
Notes from the lab
Early essays and working ideas.
Why most AI trading systems fail
Many systems described as AI trading platforms are primarily research pipelines connected to execution engines. Without governance layers, monitoring, and operational discipline, intelligent systems quickly become difficult to trust in real market environments.
Markets need governed agents
Intelligent agents operating in markets must function within clear policy boundaries. The future likely belongs not to unconstrained autonomy but to systems that balance reasoning ability with explicit operational control.
Prediction markets as proving grounds
Prediction markets provide a unique environment for testing machine-assisted decision systems: clear outcomes, probabilistic signals, and real incentives. They represent a natural early wedge for exploring governed agentic systems.
Building quietly.
If you're interested in the ideas behind Barg Labs or would like to follow the work, feel free to reach out.