Designing and Evolving Multi-Agent Systems via a Typed Intermediate Representation

Automatic design of LLM-based multi-agent systems (MAS) has largely taken one of two forms: a meta-agent writes each system as free-form code, which is expressive but hard to validate and entangles design quality with coding ability; or a search over prebuilt operators, which is reliable but usually settles for one design per task family. We take a middle route in which the meta-agent designs and an engine implements. For each query the meta-agent emits a typed intermediate representation (IR) that composes agents from interaction primitives and specifies each agent by role, objective, and instructions. A rule-based compiler deterministically lowers each valid IR into an executable MAS, making every design statically checkable, executor-independent, and fully traceable from architecture to outcome. An experience-guided evolutionary loop then mines successful strategies and failure lessons from development traces to refine the meta-agent's design policy, updating no model parameters and adding no test-time cost. With a lightweight open-source executor, our system attains the best average accuracy across four benchmarks spanning math and coding, exceeding the strongest of eight baselines by 9.3 points, and evolution improves every benchmark. Because execution is fully traced, gains and losses are attributable: evolution repairs coordination while residual errors localize to executor capability, making failures of automatically designed MAS inspectable.