Shepherd: A Runtime Substrate Empowering Meta-Agents with a Formalized Execution Trace

As LLM-based agentic systems grow more complex, they increasingly rely on meta-agents: higher-order agents that act on other agents, much like managers supervise employees. Yet existing agentic runtimes expose execution only as static environmental states, limiting the kinds of live and post-hoc interventions a meta-agent requires. To unlock these capabilities, we introduce Shepherd, a functional programming model that formalizes meta-agent operations on target agents as functions, with core operations mechanized in Lean. Shepherd records every agent–environment interaction as a typed event in a principled Git-like execution trace where any past state can be cheaply forked and replayed. Even at scale, Shepherd forks the agent process and its filesystem 5x faster than Docker, with >95% prompt-cache reuse on replay. We exemplify Shepherd's versatility in three use cases: (1) runtime intervention, where a live supervisor improves pair coding pass rate from 28.8% to 54.7% on CooperBench; (2) counterfactual meta-optimization, where a meta-agent branches to explore alternative paths, beating MetaHarness and GEPA across four benchmarks by up to 11 points with up to 58% lower wall-clock; and (3) Tree-RL training, where a meta-agent forks rollouts at chosen turns, lifting TerminalBench-2 from 34.2% to 39.4% on Qwen3.5-35B-A3B. These use cases show Shepherd is a performant and efficient substrate for programming meta-agents; we open-source it to advance future research.