Agent design adapts safely amid changing conditions with lower costs
Conservative Meta-Agent Search: Architecture Adaptation under Non-Stationarity
Artificial IntelligenceMachine LearningMultiagent Systems
Summary
Designing automated agents that work well over time is hard when task conditions keep changing. The authors offer a method called Conservative Meta-Agent Search (CMAS) that carefully tests new agent designs against current ones before making changes. They use a special simulator to measure performance and control risks in these updates. Tests show CMAS leads to better agent performance on financial data streams while reducing deployment costs and keeping error rates low.
What this means in practice
- •For financial algorithm developers: Improve adaptive trading algorithms to maintain performance despite changing market conditions and costly architecture updates.
- •For software reliability engineers: Manage risk and performance when updating critical systems by using controlled testing and rollback strategies for architecture changes.
Authors
Ali Atiah Alzahrani
Abstract
Automated agentic-system design is typically evaluated on static task suites with immediate scalar rewards. Deployed systems face persistent shift, delayed dependent feedback, correlated component failures, and costly architecture changes. We formalize meta-agent architecture search as sequential incumbent replacement with switching costs and hard risk constraints and introduce Conservative Meta-Agent Search (CMAS). CMAS retrieves archived architectures and proposes typed graph edits, then scores each against the incumbent by paired shadow replay under common random numbers, with interventional component credit. Promotion requires an empirically calibrated, variance-penalized simultaneous score above a pre-specified margin, and is followed by a simulated canary stage and pre-specified rollback. Since backtests cannot certify meta-level skill, we introduce MetaInvest-Bench, a regime-switching simulator whose specified worker competence makes a surrogate-optimal architecture computable, with leakage-controlled replay, adversarial governance tracks, and five negative controls. Financial streams serve as a risk-sensitive testbed for meta-level generalization; we claim no alpha. Across 96 streams, CMAS cuts normalized mean surrogate dynamic-oracle regret from 0.116 to 0.104 and raises held-out utility from 0.44 to 0.48 over the strongest adaptive baseline; when extra workers add no value, it contracts to near-single-agent teams. Its deployment inference cost is ~6% lower, though its search-inclusive token cost is the highest of the methods compared. The observed per-update false-promotion rate is 3.4% against a nominal 5% target, and 4.8% in a sealed rerun. The calibration is empirical: it concerns an update-level replay statistic on an author-designed benchmark, and guarantees neither full-utility improvement, nor absence of simulated canary-stage harm, nor sequence-level error control.