Large language model agents are usually deployed with fixed configurations, yet tasks differ widely in required model capability, context length, tools, permissions, memory and compute budget. AgentBetta introduces an adaptive AI nano‑agent framework that treats these factors as an executable configuration and updates it through verification‑driven diagnosis, selective expansion and counterfactual verification‑based contraction.
On the AB‑ConfigBench benchmark, AgentBetta achieved a 91.38% verified success rate while cutting the median context allocation from 64,000 to 8,000 characters and reducing tool exposure from five tools to zero compared with a fully provisioned setup. The configuration‑deficiency diagnosis obtained a macro‑F1 of 0.819 with precision of 1.000 across all dimensions, and selective expansion avoided unnecessary changes to unrelated configuration axes. Post‑success contraction preserved verification outcomes in 56.41% of one‑dimension contraction probes, indicating that some successful configurations contained removable capability. External evaluations showed that adaptive configuration can improve the trade‑off between verified task completion and capability exposure, but results vary across benchmarks and agent families; cross‑family replication did not reproduce the primary‑backbone accuracy ordering, and specialized systems remained advantageous for certain domains.
These findings suggest that AgentBetta functions as a configuration‑adaptation mechanism that regulates capability allocation and inference cost rather than a universal replacement for specialized agent architectures.
Review: AgentBetta provides a concrete example of using formal verification to drive configuration adaptation, offering a promising direction for deploying LLM agents in resource‑constrained settings.