Agentic System Oversight and Human-AI Collaboration: Governance for Autonomous AI

AAAI 2026 Workshop AIGOV Submission20 Authors

Keywords: Ethical and Responsible AI, Human-AI Collaboration, Scaling and Operationalising AI, Adoption Roadmap

TL;DR: This paper proposes a strategic governance framework for agentic AI systems, emphasizing human-AI collaboration, ethical oversight, and operational readiness to ensure responsible and scalable deployment in high-stakes environments

Abstract:

The emergence of agentic AI systems, autonomous entities capable of reasoning, acting, and collaborating marks a significant shift in the evolution of artificial intelligence. These systems promise transformative benefits across sectors such as public safety, healthcare, and enterprise operations. However, their autonomy introduces new risks, including ethical misalignment, legal ambiguity, and operational unpredictability. This paper addresses the urgent need for a governance framework that ensures agentic AI systems are safe, accountable, and aligned with human values.

The Governance Challenge
Traditional AI governance approaches, focused on data privacy, fairness, and transparency, are insufficient for managing the complexity of agentic systems. These systems operate with varying degrees of independence, interact with other agents, and adapt to dynamic environments. Without proactive oversight, they can escalate errors, amplify bias, and erode public trust. The challenge lies in designing governance mechanisms that are not only robust and anticipatory but also adaptable to diverse organisational contexts and risk profiles.

Proposed Framework
This paper introduces a strategic, lifecycle-based governance framework tailored to agentic AI. The framework spans eight interconnected stages:

Strategic Alignment – Establishing purpose-driven AI strategies aligned with organisational values.
Capability Assessment – Evaluating data maturity, infrastructure, and team readiness.
System Design & Architecture – Co-designing inclusive, transparent, and scalable systems.
Governance Capabilities – Embedding oversight protocols, escalation paths, and compliance mechanisms.
Deployment & Integration – Operationalising AI within workflows with human-in-the-loop safeguards.
Monitoring & Feedback Loops – Enabling continuous oversight, bias detection, and adaptive learning.
Evaluation & Impact Measurement – Assessing performance, trust, and societal outcomes.
Scaling & Stewardship – Expanding responsibly with embedded governance and ethical leadership.

Each stage is examined through foundational, technical, ethical, legal, operational, tactical, and strategic lenses to ensure a comprehensive and context-sensitive approach.

Human-AI Collaboration and Change Enablement
A key contribution of this work is the Human-Centred Lean-Change model, which redefines human roles from passive operators to active orchestrators. The model comprises four phases: Trust-Building, Integration, Evolution, and Scaling, and five strategic pillars: identity safety, mindset activation, capacity-aware design, role-tailored pathways, and scalable trust. This model supports ethical adoption by fostering psychological safety, peer learning, and inclusive co-design across diverse teams and jurisdictions.

Operationalising Governance
The framework emphasises the importance of embedding governance into the daily rhythms of AI development and deployment. It advocates for integrating governance checkpoints into system lifecycles, ethical reviews into procurement processes, and oversight mechanisms that are both reactive and anticipatory. Practical tools such as transparency logs, bias audits, observability dashboards, and role-based override mechanisms are discussed as enablers of actionable governance.

Real-World Impact
Drawing from leadership experience across public and private sectors, the paper presents case studies that demonstrate measurable outcomes. At the QLD Public Sector, agentic systems reduced documentation time by 98%, enabling frontline officers to focus on community engagement. In enterprise contexts, ethical compliance improved by 25% through cross-platform orchestration. These examples illustrate how responsible agentic AI can enhance efficiency, decision-making, and public value.

Strategic Imperative
The future of agentic AI will be shaped not by algorithms alone, but by the decisions of today’s leaders, government officials, enterprise executives, and policy architects. These systems are not neutral; they reflect the values and assumptions of their creators. The paper calls for a shift from reactive compliance to proactive stewardship, urging leaders to embed governance into the DNA of AI strategy from inception.

Conclusion
Agentic AI represents both a technological breakthrough and a governance imperative. This paper offers a strategic, human-centred framework to guide the responsible development, deployment, and scaling of autonomous systems. By aligning innovation with ethical and societal values, we can ensure that agentic AI not only performs tasks autonomously but does so with accountability, transparency, and integrity, elevating human intelligence rather than replacing it.

Submission Number: 20