Ahead-of-Time (AOT) and Just-in-Time (JIT) harness paradigms represent two contrasting methodologies for agent harness optimization:

• Artifact Durability: AOT methods optimize a durable scaffold intended to remain fixed or periodically edited across tasks, whereas JIT methods generate transient, instance-specific scaffolds on the fly for each individual task.
• Optimization Timing: AOT methods perform extensive offline search or test-time editing over accumulated experience streams prior to deployment, while JIT methods amortize search into a generative meta-agent that produces a customized harness at inference time.
• Assumption of Homogeneity: AOT assumes relative stability and homogeneity in deployment distributions, whereas JIT embraces the heterogeneity of real-world tasks where optimal harness priors (e.g., search strategies, memory structures, and verification hooks) are instance-dependent.
• Adaptation Mechanism: AOT systems rely on manual engineering or iterative post-hoc search across large parameter spaces, whereas JIT systems combine immediate task-conditioned synthesis with short-horizon learned repair and online archive evolution.