optimize

You can coordinate parallel subagents for iterative experiments by using an orchestrator-driven loop that distributes structured briefs across branches and manages cross-agent state. This automates trial-and-error in research pipelines.

An evo optimization loop is an automated process that coordinates multiple semi-autonomous subagents to explore a search space efficiently. You need it for research pipelines requiring structured experimentation and trace-based learning.

Yes, you can run budgeted parallel experiments with progress tracking and gate-aware pruning. The execution is stall-limited and uses pointer-trace-driven exploration to ensure robust experimentation within constrained budgets.

To start an orchestrator-driven workflow for cross-agent coordination, initialize the evo workspace and run the orchestrator. It will automatically start parallel subagent optimization using structured briefs and scratchpad summaries.

Pointer-trace-driven exploration works by tracking cross-agent state through structured briefs and scratchpad summaries across branches. This allows subagents to learn from traces and efficiently navigate the search space during optimization.

The limitations of using semi-autonomous subagents for optimization include execution being constrained by predefined budgets and stall-limits. Progress is tracked through gates-aware pruning, which may terminate branches early if they fail to meet coordination gates.