agent-ops-optimizer

Multi-agent orchestration optimization analyzes memory events and git activity to detect coordination friction, then recommends concrete tuning adjustments like modifying max_parallel_threads and max_delegation_depth to resolve runtime bottlenecks.

Multi-agent systems exceed runtime limits due to suboptimal max_runtime_minutes configurations and delegation depth issues, which agent profiling detects by analyzing execution signals and git history to recommend specific parameter adjustments.

Analyzing agent execution signals involves processing memory events and git activity logs to identify recurring development patterns and agent failures, enabling data-backed recommendations for new specialized agent profiles.

New specialized agent profiles are needed when agent ops analysis identifies recurring development patterns and coordination failures in event logs, indicating that current agent roles lack the specialization required for optimal task execution.

Agent runtime parameters including max_parallel_threads, max_delegation_depth, and max_runtime_minutes can be tuned based on git history analysis that reveals observed friction points and coordination issues within multi-agent execution.

Reducing coordination issues requires analyzing event logs and git activity to detect friction, then applying data-backed recommendations for orchestration tuning and new agent profiles to improve multi-agent system performance.