Product Harness
Quant Analyst AI Agent.
A product harness track for making an internal quant research and advisory agent reliable. Each lecture starts from a quant-agent failure pattern and turns it into a harness practice.
Define the Work Surface
Turn vague quant-agent requests into bounded research work that can be completed and reviewed.
Make Context Discoverable
Expose quant research knowledge so agents can find the right facts without drowning in context.
Design the Agent Interface
Treat quant tools, datasets, permissions, and feedback as the interface your agent must operate through.
Prepare the Runway
Separate setup, data checks, and first verification from quant research work.
Limit Active Work
Prevent strategy drift by forcing the agent to finish one research behavior before starting the next.
Persist Progress
Make long-running quant research restartable without relying on chat history.
Separate Doing from Judging
Do not let the research agent be the only judge of investment-readiness.
Close the Feedback Loop
Use repeated quant-agent failures to make the harness stronger over time.
Instrument the Work
Make quant-agent behavior debuggable with traces, data lineage, run records, and decision artifacts.
Leave a Clean Handoff
End each quant-agent session in a state another analyst or agent can verify, understand, and continue.