INTHEBLACK September 2026 - Flipbook - Page 44
F E AT U R E
AI AGENTS ACT
Unlike AI tools such as ChatGPT and
Claude, agents can take actions. AI tools
answer questions, generate content and
solve problems such as planning itineraries
or producing background research. While AI
tools generate these outputs, agents perform
the task itself. In finance, this capability must
be subject to permissions and controls.
If an AI agent is used to prepare tax
research, it can be restricted to drawing
on approved sources and referencing
specific legislation. It can also
provide links to the supporting
material used to arrive at its
conclusions. The agent can
be instructed to identify
the professional standards
it has considered when
producing advice
and document any
assumptions, risks
and alternative views.
The system can then
prepare the output in
a document, together
with supporting workpapers
for review.
While major accounting
platforms such as MYOB are
rolling out AI agents, it is also
possible to build agents through platforms
like ChatGPT and other emerging tools.
Praxio AI has developed an AI app that
helps with tax research. “When we built
our practical tax assistant, we only used
legislation and guidance from the Australian
Taxation Office or their documents, nothing
else,” says Praxio AI adviser and content
creator, William Young FCPA.
With all the enthusiasm around AI, the risk
is that employees will use it without approval
or the business’s knowledge. Education is key.
44 INTHEBLACK September 2026
“Employees need to understand the
opportunities and risks. From a practical
perspective, I believe finance teams should
provide approved, enterprise-grade AI tools
rather than simply prohibiting their use,”
Perrett says. “If you ignore AI, employees
will seek out their own solutions using
free consumer products, which can create
a much greater security and compliance risk.
The objective should not be to prevent the
use of AI, but to provide secure tools, clear
policies and appropriate training so it can
be used responsibly.”
A NAMED HUMAN
The way agents are overseen must be decided
before they are adopted. Ideally, a strong AI
governance framework supports a system
where every significant action an agent
takes, source it draws on and assumption
or decision it makes can be reviewed.
“The control question has changed. It is not
enough to ask whether a human reviewed the
output,” says David Lee Kuo Chuen, professor
in the School of Business at Singapore
University of Social Sciences. “The better
question is whether the agent was authorised
to perform the exact action, within exact
limits, using approved data, code and policy,
and with a named human accountable.”
The objective is to design a governance
system where any failure is immediately
surfaced by the system and addressed.
This means constantly monitoring agent
output and maintaining clear accountability
for its actions.
“That is why I would like to move from
the phrase ‘human-in-the-loop’ to a clearer
‘delegation-of-authority’ model,” Lee says.
“Every finance agent capable of material
actions should have an identity, human
sponsor, permitted purpose, a financial
authority limit, approved tools, an expiry
