team-swarm

Multi-agent orchestration parallelizes codebase exploration by dispatching multiple ant workers across the task space with pheromone-guided selection and deterministic update loops. Workers write validated JSON artifacts so results can be safely scored and aggregated.

Pheromone-guided selection in multi-agent swarm exploration is a mechanism that maintains pheromone state, trails, and best solutions across iterations. It applies ant colony optimization principles to direct agents toward promising areas of the search space.

Yes, you can use a multi-agent swarm for bug hunting and test generation. The orchestration layer applies to code exploration, refactor search, and other structured search problems where parallel exploration of the task space is valuable.

Yes, JSON schema validation is required for multi-agent swarm outputs. The orchestration forces each ant worker to write schema-locked JSON artifacts so that results can be safely scored, aggregated, and managed in the session state.

The best way to coordinate multiple agents on a complex refactor search is using a deterministic control loop with DAG-like coordination. Python scripts handle initialization, selection, pheromone updates, convergence checks, and final reporting.

Multi-agent swarm exploration fails to converge when the deterministic control loop lacks proper pheromone state management or convergence checks. The system requires strict JSON ant artifacts and session state tracking to maintain convergence history across iterations.