How to Evaluate AI Agents in AR Software: 18 Questions to Ask Vendors Before You Buy

AI is now everywhere in accounts receivable. Every vendor claims to have AI agents, “autonomous workflows”, and “agentic collections”.

But most finance leaders have the same worry: what happens after we buy?

If AI doesn’t integrate with your existing systems, doesn’t handle exceptions, or introduces compliance risks, it won’t reduce human effort. It will create a new category of failure—one that’s harder to spot, harder to audit, and harder to explain to leadership.

The good news: evaluating AI agents doesn’t need to be complicated. You just need to ask the right questions—questions that expose the vendor’s underlying AI systems, training data, governance, and how their agent development actually works in real world usage.

This guide gives you 18 questions to ask vendors before you buy, plus what “good” looks like and common failure modes to watch out for.

First: what do we mean by “AI agents” in AR software?

In AR, AI agents are AI-powered systems that take action—not just provide suggestions. Instead of generating a recommended email or summarising invoices, they can execute workflows with minimal human input: chasing payments, handling customer queries, escalating disputes, and coordinating tasks across human teams and systems.

That’s fundamentally different from traditional robotic process automation. RPA follows rules. AI agents use generative AI, machine learning, and natural language processing to interpret messy inputs, infer intent, and decide what to do next.

But here’s the catch: if a vendor’s “agent” isn’t connected to the accounting layer, isn’t safe around sensitive data, and can’t prove measurable outcomes, it’s not an agent. It’s a chatbot bolted onto collections software.

18 questions to effectively evaluate AI agents in AR software

Looking to identify whether an AR software vendor’s AI agents are up to scratch? These 18 questions will help you unearth what they can and can’t do—and whether they’re actually right for your business.

1) What does your AI agent actually do end-to-end?

Ask for a concrete demo of what the agent can execute without human intervention. For example:

• send reminders
• respond to customer emails
• update CRM/AR status fields
• create tasks for collectors
• propose disputes
• support data entry automation where needed

A credible vendor should show the agent doing real work, not just generating agent drafts.

2) Is this a single agent, multiple agents, or a multi-agent system?

Many vendors now claim “agentic workflows”, but their platform may only support one agent.

If you’re serious about scalability, ask whether the platform supports multiple agents or multi agent systems (e.g., a collections agent + dispute agent + reconciliation agent). Multi-agent systems matter when AR becomes a volume problem, not a people problem.

3) What is the underlying model?

Don’t settle for “we use AI”.

Ask what the underlying model is:

• which large language models are used (or a proprietary language model)
• whether it supports edge AI use cases (processing closer to the system boundary)
• how model choice affects latency, accuracy, and cost

This reveals whether the vendor has genuine AI technologies under the hood—or a thin layer of marketing.

4) How do you prevent hallucinations and ensure logical consistency?

AR is not a place for creative writing.

Ask what safeguards exist for:

• logical consistency
• avoiding fabricated payment details
• not inventing invoice numbers or statuses

AI agents that generate confident nonsense introduce data integrity risk—and downstream finance issues.

5) Where does the agent get context from?

Agents are only as good as their context.

Ask:

• What systems does it read from?
• What data sources inform decisions?
• Does it have access to invoice history, account notes, CRM data?

This is where AR vendors prove whether they have real data pipelines and integrations, or just shallow UI prompts.

6) How does your AI handle exceptions and disputes?

Most AR automation breaks on exceptions:

• partial payments
• disputed invoices
• missing remittance info
• cross-entity billing

Ask what dispute workflows exist and how the AI agents behave when something looks wrong. Mature vendors will show explicit branching and safe escalation.

7) What “human in the loop” controls do we get?

AI agents should reduce repetitive tasks, not remove responsibility.

Ask what human in the loop features exist:

• approval flows before messages go out
• thresholds for escalation
• controls for tone and customer communications
• ability to require human review on specific accounts

The stronger the agent, the more important human oversight becomes.

8) What access controls exist for sensitive data?

Any AR platform touches:

• bank details
• invoices
• customer identities
• credit risk signals

Ask about:

• access controls
• role-based permissions
• audit trails

Also ask whether the agent can be restricted from viewing or acting on sensitive data fields.

9) What are your data security standards?

This should be non-negotiable.

Ask about:

• data security controls at rest + in transit
• storage policies for AI prompts
• retention periods
• whether customer data trains future models

You’re not just buying software. You’re making a risk decision.

10) What compliance risks have you already encountered?

Most vendors dodge this question. Don’t let them.

Ask:

• what regulated customers they support
• examples of compliance risks they’ve handled
• their policies for incident response

If they pretend risk doesn’t exist, you’re dealing with immature AI implementation.

11) Can you show fraud detection or anomaly detection capabilities?

AR is a fraud surface.

Ask whether the platform supports:

• fraud detection
• detection of unusual payment behaviour
• suspicious account activity
• duplicate invoices

Fraud detection is one of the best places for AI systems to create real value—if implemented properly.

12) How do you measure success?

This question separates hype from accountability.

Ask how they:

• measure success
• define measurable outcomes
• track ROI over time

Look for language like:

• improved collections rate
• reduced days sales outstanding (DSO)
• fewer manual touches per invoice
• higher user satisfaction

AI agents should produce measurable business outcomes, not “cool AI features”.

13) What evaluation tools do you provide?

Ask what they use internally to validate performance:

• accuracy benchmarks
• testing harnesses
• audit logs
• QA processes

Serious vendors will have evaluation tools and repeatable testing methods, not just “trust us”.

14) How do you handle continuous training and model updates?

AR changes constantly:

• seasonality
• customer behaviour shifts
• new products
• new payment terms

Ask:

• do they support continuous training
• how human feedback is captured and applied
• whether AI improves with usage

If the AI is static, your results will degrade over time.

15) What’s your approach to ethical AI practices?

This isn’t about PR.

Ask whether they have:

• documented ethical AI policies
• ethical AI practices in development
• bias mitigation (credit scoring and prioritisation can introduce risk)
• controls over automated comms tone and escalation

AI in collections has reputational impact.

16) Who built the agents—and who supports them?

This is where vendor maturity shows.

Ask:

• do they have in-house data scientists
• the role of human expertise
• whether they rely heavily on contractors

If a vendor has strong technical expertise, they can explain it simply.

Also ask who owns the AI roadmap and incident triage. AI agents need attention, not neglect.

17) If we need custom workflows, do you offer custom AI solutions?

Some teams want “off the shelf solutions”. Others need tailored workflows.

Ask:

• do they provide custom AI solutions
• can they design AI agents around your collections policies
• what their AI agent development process looks like

If they can customise agent development responsibly, it may become a genuine competitive advantage.

18) Are you a software vendor, or an AI agent development partner?

This is the big one.

Many AR tools will claim they do AI agent development, but what they really offer is configuration support. That’s fine—but you need to know the difference between:

• a SaaS vendor
• an AI agent development company
• an agent development company
• a long-term development partner

If you’re planning broader AI investments, you may need a true AI agent development partner—someone who can support building AI agents, extending systems safely, and delivering ongoing improvements.

Ask:

• what ongoing support looks like
• SLAs
• whether they provide ongoing support for agent behaviour changes
• how they handle performance degradation

Also ask about “bad fit signals” like poor communication. It matters more than people admit.

Final checklist: key factors that predict success

If you’re short on time, focus on these key factors:

• security + access controls for sensitive data
• evidence of proven track record (not vague claims)
• ability to integrate with existing systems
• human-in-the-loop governance
• measurable outcomes and evaluation tools
• ongoing support and continuous training

AI projects fail when vendors treat agent development as a one-off feature release. AI agents succeed when the vendor treats them as evolving systems.

How Kolleno’s AI Agent transforms AR management

Kolleno’s AI Agent is designed to act like a collections team assistant—not just an “AI feature” bolted onto accounts receivable software. It focuses on execution: automating routine collections work, supporting teams with answers and insights, and helping reduce the manual back-and-forth that slows down cash collection. Kolleno describes it as “AI that works for collections”, spanning core AR workflows like collections, payments, reconciliation, disputes, credit risk, and cash flow.

A big differentiator is how Kolleno frames automation. Rather than only offering workflow rules, it highlights “Agentic Workflows”—including the claim that you can assign AI Agents to fully automate collections and reconciliation processes. This moves AR management from “AI suggestions” to guided execution, while still keeping a human approval layer where needed (for example, drafting communications for review rather than auto-sending without oversight).

Kolleno also leans heavily into AI-driven decision support. Its AI features include predicting payment likelihood, detecting at-risk accounts, dynamic credit scoring, and prioritising client issues—which is exactly where AI delivers real value in AR: not by writing prettier emails, but by helping teams focus effort where it will shift cash flow outcomes.

Final thoughts

You don’t need to become an AI expert to buy AI agents. You just need to ask questions that force vendors to explain the truth underneath the marketing.

Because in AR, AI doesn’t win by sounding smart. It wins by doing repetitive tasks reliably, protecting data integrity, and driving measurable business outcomes.

If a vendor can’t answer these questions clearly, they’re not ready for your finance team—and they’re definitely not the right development company for a serious AI journey.

Ready to start assessing AI agents for your business? Book a demo to learn more about how Kolleno’s AI Agent can help enhance your AR processes.