Anish Nath, practice director at Everest Group, suggested that enterprises would benefit more from frameworks like SPICE by treating them as a training capability, not autonomy in production.
“Run self-play in sandboxes with gated releases; start on low-risk/internal workflows, then graduate to critical processes as evidence accumulates,” Nath said. “Enforce guardrails: schema-constrained outputs, policy engine, least-privilege tool whitelists, drift/anomaly detection, signed actions + audit trails, rollback/kill-switches, and human approvals for high-impact actions.”
Nath added that self-generated training data does point toward autonomous development loops, but warned of risks such as model collapse, data poisoning, and untracked drift. “These can be mitigated with independent evaluation models, provenance tracking, versioned datasets, and human gates for capability upgrades,” he said. “Improvement has to remain controlled, auditable, and compliant.”
