Designing Your First AI Agent: A Practical Guide for Accounting Firms
3 MIN READ
Jessica Kentch, Founding Partner
Agentic AI is one of the most exciting shifts in modern tech—and it’s not just for startups or software companies. Accounting firms can start using agentic systems today to automate meaningful workflows, improve service, and free up team capacity.
But how do you actually get started?
Here’s a practical guide to designing your first AI agent—from identifying the right use case to choosing the tools that bring it to life.
What Is an AI Agent?
Unlike traditional automation (which follows static rules), AI agents are goal-oriented systems. They can:
Interpret instructions
Decide how to accomplish tasks
Interact with multiple tools
Adjust based on new data
In an accounting firm, that means AI can do things like:
Chase overdue invoices
Review cash flow weekly
Send client summaries
Detect anomalies and flag issues
Assign tasks to team members
Step 1: Choose the Right Use Case
You want something that is:
✅ Repetitive
✅ Data-driven
✅ High-volume
✅ Low-risk (for your first agent)
Examples:
An “Overdue Invoice Agent” that checks Xero and emails clients
A “Month-End Checklist Agent” that creates tasks in Karbon
A “Cash Flow Pulse Agent” that sends weekly summaries to managers
A “Client Update Agent” that drafts status emails using financial data
Step 2: Map the Inputs and Outputs
Before you build anything, define:
What data does the agent need? (e.g. Xero invoices, CRM contacts)
What actions should it take? (e.g. email, Slack, create a task)
What should it do if it fails or hits an edge case?
Use this format:
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WHEN [X occurs], IF [Y condition is met], THEN [do Z].
Example:
When a client invoice is more than 14 days overdue, if the balance is > $1,000, then email the client and CC their account manager.
Step 3: Choose Your Tools
Here are a few platforms accounting firms can explore:
Zapier or Make: Great for simple, rule-based automations
LangChain / Autogen / CrewAI: Advanced tools for creating multi-step AI agents with memory and planning
GPT-4o via API or Playground: Easy for drafting emails, messages, or interpreting reports
Microsoft Copilot + Power Automate: Enterprise-grade automation with deep M365 integration
Custom tools: Built on top of your data warehouse or BI stack
Step 4: Test It Like a Teammate
Once built, treat the agent like a new hire:
Give it a clear name and job description
Run simulations before it acts on real data
Monitor performance closely
Give feedback and iterate
You’ll likely start with a “co-pilot” mode (where it drafts actions), then move to “autonomous” mode once you trust it.
Step 5: Measure ROI + Expand
Start tracking:
Time saved
Errors avoided
Client satisfaction
Internal capacity freed up
Once you’ve proven ROI, expand to new agents. Soon, you could have an entire AI team working alongside your human team.
Final Thoughts: Start Small, Scale Fast
Designing your first AI agent isn’t just a cool experiment—it’s the beginning of transforming how your firm operates. The best time to start is now, before your competitors do.
At Ablaze Analytics & Collective, we help firms build and implement their first agentic systems—from workflow mapping to tool selection and testing.
Want help launching your first AI agent?