disagreement-resolution

Multi-agent disagreement resolution works through an orchestrator-subagent pattern that isolates context per phase to avoid anchoring, generates discriminating tests, executes them, and formally adjudicates a clear winner or preserved dissent.

Test-based adjudication for architectural tradeoffs is a structured workflow where competing AI approaches are evaluated by generating and executing discriminating tests, yielding transparent, evidence-backed choices instead of biased or indecisive outcomes.

Use an orchestrator pattern for multi-agent decision-making when multiple valid approaches exist, users ask which approach to take, or architectural tradeoffs must be decided with structured, test-backed evaluation and preserved dissent.

Start a four-phase disagreement-resolution workflow by initiating the orchestrator-subagent pattern, which isolates context per phase, generates discriminating tests, executes them, and produces a documented, test-backed decision.

Multi-agent disagreement resolution works without external dependencies, using an internal orchestrator-subagent pattern with isolated context per phase to generate tests and adjudicate competing AI approaches independently.

A limitation of test-based adjudication is that when discriminating tests fail to produce a clear winner, the workflow must preserve dissent and request user input, potentially halting automated decision-making in complex architectural tradeoffs.