What Research Says About Structuring LLM Agent Harnesses

1. Deterministic guardrails beat smarter models
2. Majority vote beats debate
3. Hierarchies work at scale; multi-agent orgs fail in human ways
4. Memory needs an architecture, not a bigger context window
5. Sandboxed execution AND verified outcomes
6. Harnesses are finally measurable — and they matter more than the model
7. Cost routing
8. Autonomy is earned: trust calibration and supervised practice
9. Open problems

No standardized, repeatable A/B comparisons of full harness architectures yet — Harness-Bench (§6) is the first step, but diagnostic rather than leaderboard-grade.
Injection defenses saturate current public benchmarks (firewalls paper, §1) while failing on dynamic ones (AgentDyn) — evaluation lags deployment.
Single-agent safety certification does not transfer to multi-agent deployments (Anthropic, §3).
Trust calibration research (§8) studies human perceptions of agents; equivalent measured results for system-side earned-autonomy policies (e.g., statistically tuned promotion gates) are still emerging.