Governed Agentic AI for Software Platforms: A Reference Architecture for Safe Autonomy at Scale

Recent advances in large language models (LLMs) have accelerated the emergence of agentic AI systems capable of reasoning, planning, and acting across software platforms [1], [9]. While such systems promise significant productivity and operational gains, unconstrained autonomy introduces substantial risks related to safety, reliability, security, regulatory compliance, and organizational trust-particularly in large, distributed enterprise environments [3], [11], [12]. These concerns are further amplified by emerging regulatory frameworks such as the European Union's Digital Services Act [5], which impose stricter requirements on transparency, accountability, and human oversight. This paper introduces a governance control plane for agentic AI systems in software platforms and empirically characterizes the trade-offs between autonomy, safety enforcement, and human oversight. The proposed architecture balances autonomous decision-making with deterministic policy enforcement and structured human control. The proposed architecture decomposes agent behavior into four controlled layers: (1) intent interpretation using LLMs [1], (2) policy-aware planning and decision logic informed by enterprise rules and risk constraints [6]–[8], (3) guarded execution over platform APIs inspired by tool-augmented LLM paradigms [9], [10], and (4) continuous telemetry, auditing, and rollback mechanisms to ensure operational safety and accountability. Central to the architecture is the concept of bounded autonomy, where agents automate routine and low-risk decisions while remaining constrained by declarative policies, approval gates, runtime validation, and execution allow-lists. We demonstrate the applicability of this approach through representative enterprise scenarios, including AI-assisted feature rollout and automated issue triage, building on established software engineering and DevOps principles [2], [4]. Beyond qualitative analysis, we report prototype-based measurements of governance overhead and safety effectiveness, including policy-evaluation latency, invalid-action blocking rates, and changes in human intervention volume across autonomy tiers. We further discuss adversarial robustness considerations such as indirect prompt injection [11], [12], concurrency and state-drift management in long-running agents, and policy conflict resolution in multi-agent settings [13]. Overall, this work contributes a practical, governance-centered blueprint for deploying agentic AI systems with explicitly bounded autonomy, observable decision-making, and enforceable safety controls in large-scale software platforms.