Can you trust AI agents? Auditability and risk in P2P

Every CPO and CFO evaluating AI agents is hearing some version of the same question from audit and legal teams: How will you trust AI to act on the company’s behalf?

Truthfully, the answer is not “100% accuracy.” No production system, whether human or AI, can satisfy that standard. Instead, the trust comes from how the system is designed, governed, observed, and corrected when something goes wrong.

In procurement and finance, AI agents should be trusted the way a junior analyst is trusted: with clear guardrails, role-based permissions, audit trails, human oversight, and escalation paths for higher-risk decisions. The goal is to make every agent action traceable and controllable.

This is the lens procurement, finance, and risk leaders are using in 2026 to deploy AI agents safely across AI procure-to-pay workflows. It’s a way to pass audit, satisfy regulators, and earn stakeholder confidence without giving up the cycle-time gains agentic AI can deliver.

The accuracy paradox: Why is 100% accurate the wrong bar?

A standard of 100% accuracy is the wrong bar because no procurement or finance team reaches it today. Human teams still make mistakes, and AI agents should be measured against that real-world baseline. There is one notable consideration: Every action should be traceable when something goes wrong.

That is the accuracy paradox. Prashant Chamarty, a strategic alliance leader in the data and AI partnerships space, wrote about this exact thing in a piece for LinkedIn. Chamarty explained that an enterprise was sample-auditing 5% of invoice classifications, accepting the unknown error rate in the remaining 95%. At the same time, it demanded 100% accuracy from AI before allowing it to act. 

Benchmarks point to the same conclusion: GAIA found humans scored 92% on real-world assistant tasks, while the CLEAR framework found agent reliability dropped from 60% on a single run to 25% across eight-run consistency tests. It’s evident that perfection is the wrong standard. Controlled, explainable performance is better.

Procurement and finance teams should instead evaluate agentic AI on whether it can fail safely, transparently, and recoverably. That means asking:

• What human baseline is this agent being measured against?
• What data, policy, and tools shaped the agent’s recommendation?
• Which actions require human approval before they take effect?
• What happens when the agent is wrong?
• How are override rates, exceptions, and drift monitored over time?

The objective is to build a system that delivers reliable outcomes while making oversight and remediation clear when issues arise.

Five pillars of trustworthy AI agents

Trustworthy AI agents earn trust through transparent decision-making and accountability. In procurement and finance, those principles are supported by governance practices that ensure agents operate safely and in line with business requirements.

Pillar 1: Defined guardrails

AI agent guardrails are explicit rules that constrain what an agent can and cannot do. They turn autonomy into bounded autonomy. This is what makes agentic AI defensible in a governed procurement environment.

Strong guardrails usually work across three layers:

• Strategic boundaries: Agents are limited by purpose, category strategy, and business context. For contract-side workflows, AI contract orchestration depends on the same boundary: Agents can draft and flag, while legal teams approve material changes.
• Policy and threshold controls: Spending limits, approval thresholds, supplier eligibility, and contract clause requirements are enforced during the workflow, not reviewed only after the fact.
• Adaptive permissions: Access is temporary, task-scoped, and re-evaluated based on data sensitivity, task relevance, and workflow state.

For example, a Zip AI agent reviewing an invoice may need read access to supplier records and contract terms, plus permission to suggest general ledger (GL) coding. It should not have permission to release payment above policy thresholds without human authorization.

Without guardrails, an agent can be technically authorized to access a system and still take an action that violates policy or expands access too far. With guardrails, autonomy has a set boundary. Audit teams can review that boundary, test it, and understand how it was enforced.

Pillar 2: Full observability and audit trails

AI agent auditability starts with a complete record of what happened. Every agent action should create a tamper-resistant log that captures:

• What data the agent accessed
• What tools it invoked
• What decision or recommendation it made
• What policy or source shaped that action
• What a human approved or overrode
• What happened next

Tracability is the foundation for enterprise trust. As Arthur AI puts it, “the single most important thing you can do to prepare an agent for enterprise security review is to instrument it with tracing from day one.”

Regulators are moving in the same direction. The EU AI Act sets penalties of up to €35 million or 7% of global annual turnover for certain violations, and Article 86 creates a right to explanation for some high-risk AI decisions. DORA, which entered into application in January 2025, adds information and communication technology (ICT) risk management and third-party oversight expectations for financial entities. U.S. model risk guidance, including SR 26-2, also emphasizes documentation, validation, monitoring, and accountability.

For procurement teams, the takeaway is simple: An agent that cannot be traced cannot be trusted at scale. Zip’s AI agents operate inside the same workflow record that captures human approvals, policy checks, and procurement decisions. Agent activity can be tied back to the source data used, the rule applied, and the person who approved, corrected, or overrode the recommendation.

Pillar 3: Human-in-the-loop by design

Human oversight should be designed into AI agent workflows from the start. It shouldn’t be added later when something goes wrong. The right level of oversight depends on a few practical questions: Is the decision reversible? Can the agent affect financial flows or production systems? Would the outcome create material risk for the business, customers, or regulators?

That creates three common oversight tiers:

• Human-in-the-loop (HITL): A human approves or corrects the action before it takes effect. This fits high-risk decisions, such as payment release above threshold, contract execution, supplier offboarding, or security-sensitive data access.
• Human-on-the-loop (HOTL): The agent acts, while humans review outcomes and correct patterns after execution. This fits medium-risk workflows, such as invoice coding, vendor consolidation, or intake routing.
• Human-out-of-the-loop (HOOTL): The agent handles predefined, low-risk work autonomously, with monitoring in place. This fits routine validations, data enrichment, status updates, and intake completeness checks.

In Zip, the oversight model can be mapped to the risk associated with each agent’s work, rather than treating all agents the same.

The point here is to keep people in the right parts of the workflow. Low-risk checks can move quickly, while decisions that affect money movement, contracts, supplier status, or regulated data can stay under human control.

Pillar 4: Policy-driven workflows

Policy-driven workflows make compliance part of how work gets done. Agents inherit the same rules that humans follow, from spending authority and approval routing to supplier qualification and security review. They cannot bypass those controls just because a prompt asks them to.

This is important because procurement risk often appears before a purchase order is created. When an agent validates a purchase requisition, it needs to evaluate the request in context: Is the supplier approved? Does the price match the contract? Does the requester have the right authority? Does the purchase follow policy?

Zip applies this logic inside procurement workflows. When a request enters Zip, it can be validated against spend thresholds, preferred vendors, contract terms, security review requirements, and regulated workflow controls during intake. For financial services teams, that includes agent-supported workflows such as DORA assessment, where policy context and auditability matter before a supplier decision moves forward.

Pillar 5: Continuous evaluation

AI agents are not static software. Their performance can drift as policies change or users find new ways to prompt the system. Trust requires ongoing measurement; it’s not a one-time assessment made when picking a vendor.

Ongoing evaluation should track whether the agent stays grounded, follows the right process, and improves over time. Consider evals such as hallucination, answer completeness, goal accuracy, and topic adherence for agent interactions. Another useful lens is consistency across repeated runs, since a system that succeeds once but fails on repeat is not reliable enough for high-risk workflows.

In procurement and finance, the most useful metrics include hallucination rate, goal accuracy, policy adherence, run-to-run reliability, and human override rate. Rising overrides are especially important because they can signal a workflow that needs tighter controls. Zip customers can review agent decisions within the same workflows they already use for human approvals, making evaluation part of day-to-day governance rather than a separate audit process.

The regulatory landscape: What must AI agents satisfy in 2026?

In 2026, trustworthy AI agents must satisfy a combination of AI governance, operational resilience, privacy, security, and model risk management requirements. The exact obligations vary by industry and geography, but enterprise AI systems are increasingly expected to demonstrate transparency, oversight, auditability, and control.

The practical implication is simple: “We have SOC 2” is not a complete answer to “Can we trust your AI agents?” Security certifications are important, but agentic AI also needs traceability, human oversight, policy enforcement, and documented risk controls.

Zip’s approach is designed to meet that broader trust standard. The Zip Trust Center documents Zip’s SOC 2 Type II, ISO 27001, and GDPR-aligned controls, along with security, privacy, and compliance practices that support AI deployment across procurement and finance workflows.

A practical risk-mitigation playbook for AI agents in P2P

Procurement and finance teams do not need to solve every AI governance question at once. A practical 90-day plan can turn the trust framework into an operating discipline.

1. Classify every AI use case by risk tier. Map each agent capability to HITL, HOTL, or HOOTL based on reversibility, write access, financial impact, and regulatory exposure.
2. Define guardrails before agency. Document what the agent can do autonomously, what triggers human review, and what the audit trail must capture before the agent is turned on.
3. Create a cross-functional governance group. Procurement, finance, IT, legal, risk, and internal audit should approve use cases, set policies, review exceptions, and assess override trends on a regular cadence.
4. Instrument from day one. Tracing and evaluations should be part of the first deployment. An agent that cannot be reviewed will not pass the internal audit, even if its accuracy looks strong.
5. Run experiments before rollout. Test candidate agents against known failure modes and human baselines. The goal is to quantify where the agent improves the workflow and where it still needs human control.
6. Monitor continuously and revalidate quarterly. Track override rates, exception aging, hallucination incidents, policy violations, and drift indicators. AI governance should operate as an ongoing, living function.

Zip supports this operating model inside the procurement workflow. Agents can be tiered by risk, governed by policy, reviewed through audit trails, and monitored alongside the same approvals and controls procurement teams already use.

Why Zip is built for trust by design

Zip’s AI agents are built around the principle that every action must be governed and reviewable inside the workflow where the work happens. Each agent has a defined job. 

The goal is not to give one general-purpose agent unlimited room to act, but to assign specific agents to specific procurement and finance work, then govern those agents through the same controls that apply to people.

Trustworthy AI agents are accountable agents

The answer to the audit committee is not, “Our agents are 100% accurate.” The better answer is, “Our agents operate inside guardrails. Every action is auditable, humans approve high-impact decisions, and policies are enforced as work happens.” Trust in AI agents is earned by passing internal audit, satisfying regulators, and delivering better outcomes than the workflow being replaced.

Procurement and finance leaders need accountable AI agents, not perfect ones. Built correctly, agentic AI can make procurement more auditable than the human workflow it replaces. 

Visit the Zip Trust Center to see how Zip’s AI agents are built for auditability, security, and compliance, or book a demo to talk with our team about deploying agentic procurement safely.

Frequently asked questions

Can you trust AI agents?

You can trust AI agents when they operate inside clear guardrails and policy-driven workflows. Trust should not depend on a vendor’s accuracy claim alone. It should depend on whether every agent action can be governed through the same controls used for human work.

Are AI agents 100% accurate?

No AI agent is 100% accurate, and that should not be the standard. Human procurement and finance workflows aren’t 100% accurate, either. The better question is whether the agent performs better than the workflow it replaces, with stronger traceability, clearer controls, and safer escalation paths when something goes wrong.

How do you make AI agents auditable?

Make AI agents auditable by logging every action they take. That includes the data accessed, the tools invoked, the policies applied, the recommendations made, the human approval or override, and the final outcome. The audit trail should be timestamped and connected to the workflow record so reviewers can reconstruct what happened.

What is the audit trail for an AI agent?

An AI agent audit trail is the record of what the agent did, what information it used, and how humans responded. A strong audit trail should show the agent’s inputs, actions, policy checks, approvals, exceptions, and version history. It gives audit, legal, and risk teams the evidence they need to review decisions with confidence.

What are AI agent guardrails?

AI agent guardrails are rules that limit what an agent can do. They can include purpose boundaries, spending thresholds, approval requirements, supplier eligibility rules, access limits, and escalation triggers. Guardrails turn autonomy into controlled autonomy, so agents can move work forward without acting outside policy or risk tolerance.

How does human-in-the-loop work for AI agents?

Human-in-the-loop means a person approves or rejects an agent action before it takes effect. It is usually used for high-risk work, such as payment release, contract execution, supplier offboarding, or regulated data access. Lower-risk work may use human-on-the-loop review or automated execution with monitoring.

How do you mitigate risk with AI agents in procurement?

Mitigate AI agent risk by setting clear risk tiers, defining guardrails before launch, and logging every action the agent takes. Teams should also test agents against past procurement decisions and monitor performance over time.

Procurement, finance, IT, legal, risk, and internal audit should review agent policies regularly. Those reviews should focus on override rates, exceptions, incidents, and whether the agent is operating within approved controls.

What regulations apply to AI agents in 2026?

AI agents may be affected by the EU AI Act, DORA, GDPR, CCPA, SR 26-2 and updated model risk guidance, NIST AI RMF, SOC 2, and ISO 27001. The exact obligations depend on the industry, geography, data involved, and decision being made. Most enterprise deployments need traceability, oversight, security, and documentation.

How do you govern AI agents in finance and procurement?

Govern AI agents by giving each use case a clear owner, risk tier, approval model, audit trail, and performance review cadence. Finance and procurement teams should define which actions agents can take, which require human approval, and which metrics signal drift or rising risk.

Is AI agent decision-making explainable?

AI agent decision-making is explainable when the system records the data used, the policies applied, the tools invoked, and the recommendations made. Explainability helps stakeholders to understand the business reason for the action, review the supporting evidence, and see who approved or changed the result.

Ready to deploy auditable AI agents in procurement and finance? Book a Zip demo to see how governed agentic workflows work in practice.