How can I build my own AI agent?
Building personal AI agents for productivity allows finance professionals to automate routine tasks to reduce time and focus on higher value work.
Finance professionals can start with no-code tools that require little more than logical thinking and clear instructions.
“Today, almost anyone can build agents using low-code or no-code providers like Microsoft Copilot or n8n,” says Rajkovic, adding that the skill set varies depending on the agent’s complexity.
A dedicated few may want to progress towards more customised solutions with low-code and build-your-own agents, but this path requires more familiarity with data structuring, programming language and AI frameworks.
“There are a lot of manual activities done on a repeated cycle that you can turn over to an agent and gain a lot of operational efficiency.”— Harshu Deshpande, Publicis Sapient
With agentic tools now available in products such as Microsoft Copilot Studio and Google Gemini Enterprise Agent Platform, finance professionals can begin experimenting with agent workflows in line with organisational policies. Pre-built templates are available to guide structured workflow design.
“If you are working with annual reports, there is an in-built workflow that lets you ingest several reports and then use natural language to make queries about them,” Deshpande says.
Other finance-related agent templates include invoice processing, procurement approvals, budgeting and financial analysis.
A note on company culture and AI implementation
When adopting agents in finance and accounting workflows, tools and capability are only part of the equation. Creating and using agents may require a culture change to embrace experimentation and manage risk considerations with strong governance.
“Unless you put these tools in employees’ hands and give people the permission to experiment,” Deshpande says, “you do not know what use cases are in an organisation.”
Ethical considerations for AI agents in accounting
When it comes to agent deployment in finance workflows, there are important ethical elements to consider. Inaccuracy and explainability of output are two of the most reported risks of AI, although according to McKinsey’s The state of AI in 2025 report, more organisations are implementing mitigation strategies.
High AI performers are more likely to have defined processes to determine how and when model outputs need human validation to ensure accuracy, the report notes.
“A finance environment’s specific risk appetite dictates their level of acceptance. While some applications allow for a higher tolerance, critical financial functions demand absolute precision,” Rajkovic explains.
“For these high stakes environments, even marginal errors are unacceptable. Consider transaction integrity — bank balances must be exact, as losing even $0.01 during a transfer constitutes a critical failure.”
“You need the ability to map workflows clearly — defining triggers, actions and outcomes. Then iteratively refine through testing. Basic systems-thinking is more important than heavy technical development.”— Jasmine Liu, Cubic Promote
Organisations need to be explicit about what agents should and should not do, and build processes that ensure a human will always review their work. The rules need to reflect that LLMs are not deterministic and are, in fact, prone to fill in gaps in output. Rajkovic suggests “a rule might be: if you are missing data, do not guess”.
Regulatory reporting is another area where errors are unacceptable, he says. “Reports for regulatory bodies such as the Australian Taxation Office require total accuracy to prevent legal action or heavy fines.”
Likewise, audit trails have no margin for error. “Documentation must be 100 per cent verifiable and precise,” Rajkovic continues.
With AI, transparent governance is a business requirement rather than an optional extra, particularly in finance and accounting. Frameworks need to ensure AI remains ethical, transparent and compliant with internal policies such as data access restrictions or bans on agents modifying production code.
“As autonomous agents become central to operations,” he says, “organisations must transition from manual oversight to real-time monitoring and automated controls.”