theta-wave

Evaluating a multi-agent system to prevent stagnation requires running scheduled deep-scan cycles that inspect agent heartbeats, tasks, and experiment results. The system identifies bottlenecks and proposes evidence-based architectural or workflow changes to drive measurable improvements.

A system effectiveness score in multi-agent orchestration is a qualitative 1–10 rating assigned per evaluation cycle. It is justified by observed data and historical trajectories, providing a compound metric to track system-level research performance over time.

Detecting stale or converged research cycles involves running system-wide scans that review prior experiments and agent contexts. The evaluation identifies underperformance or convergence patterns, enabling adaptive cycle management to pause, modify, or remove redundant agent crons.

Automating orchestrator decisions using agent experiment logs involves running a real, non-scripted negotiation with the orchestrator. The process challenges assumptions, requires evidence from logged cycle actions, and converges on actionable system modifications.

Managing adaptive research cycles for autonomous agents requires an evaluation layer that creates, modifies, or removes cycles based on keep/discard outcomes. It detects staleness and underperformance to inform orchestrator decisions across architecture and metrics.

Multi-agent system monitoring requires a project bus to log cycle actions and command execution. Without logging infrastructure, the deep-scan evaluation cannot review prior experiments, track historical trajectories, or inform orchestrator decisions effectively.