Results for “algorithm-confusion”

Design multi-agent AI workflows with clear boundaries, handoffs, and monitoring for complex product management tasks.

Defines the architecture and orchestration primitives for multi-agent teams, covering role allocation, conflict resolution, reward routing, and the operational lifecycle of agent collectives.

Orchestrates three frontier models to debate a question and synthesizes their best insights into a single superior answer.

Runs an autonomous optimization loop that iteratively improves an artifact against an objective and evaluator using Hypothesis Tree Refinement, with subagent executors in isolated git worktrees.

Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.