Improving Agents is a Data Mining Problem

Official Schedule Context

Harness Engineering, Post-Training, Continual Learning...these all boil down to the same underlying

substrate - Mining Agent Traces 1. I need to run my agents to collect Traces 2. Understand behaviors

from Traces at scale 3. Filter data for "improvement" 4. Do an improvement step There's a reason why

every continual learning platform ends up looking like an observability platform. It's because

Traces are the lifeblood of agent improvement. The mechanism that we use to attempt improvement can

vary - Harness Eng, SFT, etc. But without understanding the data agents produce, no algorithm will

truly build better agents. The holy grail of Agent Improvement is Continual Learning. Consistently

mining data and integrating it into the agent definition over infinitely long time horizons. Today,

the easiest way to do that is to build an observability platform and constantly point agentic

compute to understand the data that agents produce. We'll walk through the current methods of

understanding traces at massive scale and choosing how to integrate them to improve agents across

your personal agents, team agents, and entire company.

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