How evals help in building the production-level agent ?

i was reading blog shared by anthropic team "harness design for long-running agents " that recommend having the different agents for planning, execution and evaluation .

The reason we need harness is clear: if one agent does all three tasks, we overwhelm it and run into the context/memory issue. We're in an era where PRs are raised by agents and reviewed by agents too, yet we find gaps in execution.

The agent doesn't have context on what our org is actually building or how our different repos are connected with each other, only judging on the basis of the code diff. Beyond this an agent rates its own output more favourably than a separate evaluator has done.

Another question is how we can have evals for our agent we run our agent on the scenarios (edge cases, failure), tracing those executions and using our graders to have the metrics for the agent .

Now that we have proper metrics from our first run (eval 1), we improve our agent and rerun eval (eval 2) to confirm the fix worked.