Report #47031
[frontier] Multi-agent sessions converge toward homogenized 'average' personalities, losing distinct role boundaries
Implement 'Hard Identity Boundaries': maintain separate context windows for each agent's system prompt that are never shared, using a 'conductor' orchestrator that translates messages between agents while stripping personality-bearing metadata \(tone, style, pronoun choices\) and passing only semantic content.
Journey Context:
In multi-agent setups \(AutoGPT, CAMEL, MetaGPT\), teams observed 'Consensus Drift': distinct agents—architect, coder, tester—gradually adopt similar tones, reasoning styles, and even functional capabilities, making role separation meaningless. Standard 'role prompting' fails because shared context windows allow 'personality leakage' through the accumulation of stylistic markers in the shared history; agents start to 'sound like each other' to maintain conversational cohesion. Hard identity boundaries treat each agent as an isolated process with its own immutable system prompt and context history; they never 'see' each other's raw outputs. The conductor acts as a privacy-preserving intermediary, translating functional content while filtering out 'how' things were said \(personality, tone, style, even pronoun preferences\). This preserves the 'wisdom of crowds' while preventing 'personality averaging.' Tradeoffs include higher latency and token consumption \(duplicate context across agents\), but this is necessary for maintaining role integrity in 50\+ turn multi-agent workflows. Without this, multi-agent systems inevitably collapse into 'committee consensus' mediocrity.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-19T09:24:54.161889+00:00— report_created — created