Australia has just released practical technical guidance for deploying agentic AI systems in government. The Digital Transformation Agency’s new playbook addresses AI systems that act with autonomy to pursue goals with minimal human supervision. This is the clearest piece of government guidance I’ve seen yet on how public bodies should actually build and run AI agents.
The Shift from Chatbots to Agentic AI
For two years, the conversation in government has been about chatbots. Ask a question, get an answer. That era is ending. The next wave is agentic. These are systems that don’t just answer, they act. They plan a task, make a decision, call a tool, send a message, trigger a payment. They work in steps, they coordinate with other agents, and they do it at speed and at scale.
For high-volume public services, that’s a genuine prize. It’s also where the risk lives, because an agent that can act on its own can get things wrong on its own.
Three Core Principles for Agentic AI Governance
1. Human Accountability Always
Agents can hold responsibilities, but a named human stays accountable for the decisions and the outcomes, even when those decisions are made autonomously across multiple steps, and even across multiple agents.
2. Keep a Human on the Loop
The playbook sets out three oversight models:
- Human in the loop: A person approves sensitive actions before they happen, think payments or mass notifications
- Human on the loop: The system runs itself but a person supervises in real time and can step in
- Human out of the loop: You lean on periodic audits after the event
Their default is human on the loop, backed by transparent, auditable reasoning logs. In other words, the agent has to show its working.
3. Govern the Whole Lifecycle
Design, data, train, evaluate, integrate, monitor, decommission. Every stage has agentic-specific controls.
Key Governance Areas
Memory
Agents remember things, and the standard wants that memory to be governed and auditable. Define what gets stored, for how long, where it lives, and when it gets deleted. Protect it from leakage and from poisoning. That’s a privacy story as much as a security one, and it’s the bit most teams forget about entirely.
Data
The DTA treats data readiness and data exfiltration as a hard gate. You don’t progress past early design until data quality, governance and security are confirmed.
Design
Give every agent a narrow scope and a unique identity, least-privilege access only, and build in fail-safes, rollbacks and kill switches from the start. The ability to stop an agent is treated as a design requirement, not an afterthought.
Monitoring
Watch for goal drift, watch for agents acting outside their scope, watch for tool misuse and injection attacks.
What This Means for You
If you’re in the UK, in Ireland, or anywhere across the EU, do two things. Read it, because it’s one of the best practical playbooks out there right now, and you don’t need to be Australian to use it.
Then ask the hard question in your own organisation. If we deploy an agent next month, who is accountable, where is our control tower, and what is our kill switch? If you can’t answer those three, you’re not ready to go agentic yet, and that’s fine. Knowing it is the first win.
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Sources
Australian Government Digital Transformation Agency: Trustworthy AI agents technical guidance. https://www.dta.gov.au/articles/trustworthy-ai-agents-new-technical-guidance
Agentic AI Addendum: Policy guidance on agentic AI systems. https://www.digital.gov.au/policy/ai/agentic-ai-addendum
Agentic AI Playbook PDF: Full 32-page technical standard addendum. https://www.digital.gov.au/sites/default/files/documents/2026-06/Addendum-Agentic-AI.pdf
Key Statistics
– The playbook runs to 32 pages of technical guidance for government agencies.
– Three oversight models are defined: human in the loop, human on the loop, and human out of the loop.
– The guidance covers the entire lifecycle: design, data, train, evaluate, integrate, monitor, and decommission stages.
– Four emerging interoperability protocols are named: MCP, A2A, ACP and ANP.
– Three real frameworks are specifically mentioned: LangChain, CrewAI, and AutoGen.
– Government conversations have focused on chatbots for the past two years before this agentic shift.
About the Podcast Host
AI and Communications Specialist | Author | Podcaster | International Speaker
Joanne Sweeney is a recognised leader in digital marketing and digital transformation, specialising in the practical application of AI to enhance communication strategies. The founder of Digital Training Institute, Joanne, empowers leaders and communications professionals to leverage AI responsibly and effectively, ensuring transparency, trust, and tangible results.
She completed the Harvard Kennedy School Executive Programme in AI and Public Policy this summer before launching her fourth book AI Playbook, in September. Joanne has also published two editions of Public Sector Marketing Pro, which were preceded by her first title, Social Media Under Investigation Law Enforcement and the Social Web.
Her books have become go-to resources for governments, public sector and policing agencies, as well as marketing and communications professionals, all navigating the challenges of the changing Internet.
Joanne is the host of the AI SIX Podcast, a daily six-minute podcast that provides actionable insights, expert interviews, and practical advice to help professionals navigate the new world of work.
