Agentic AI or CLM Compliance? A Buying Test for Financial Services

Consider a hypothetical bank negotiating a technology supplier agreement. An AI agent spots a change to the audit-rights clause, proposes replacement language and prepares the contract for approval. The review looks faster.

Then someone asks which policy version the agent used, whether the replacement was approved, and what prevents the unsigned draft from becoming the operational record.

Those questions should shape the buying decision.

The real question is not simply whether an AI agent can perform a contracting task, but how much authority it should have to perform it. That depends on whether the underlying process can reliably establish the governing terms, required approvals, document version and accountable owner.

Contract lifecycle management, or CLM, provides the control environment around those decisions. Agentic AI can then automate specific work within it, with the level of autonomy determined by the evidence and controls available.

Adoption figures do not settle the autonomy question

The Bank of England and FCA’s 2024 survey received responses from 118 firms. It found that 75% used AI, while only 2% of reported AI use cases involved fully autonomous decision-making. These figures describe that survey’s respondents and use cases across financial services. They are not a measure of agentic CLM adoption or a current market estimate.

For a contract buyer, the useful distinction is between having AI and authorizing it to act. Generating a draft, recommending an exception and releasing an agreement require different permissions. Putting them under one “AI-enabled” checkbox hides the decisions the buying team actually needs to make.

The voluntary NIST AI Risk Management Framework emphasizes governance, defined responsibilities, human oversight, testing and continuing risk management. These principles provide useful criteria for evaluating who controls each action and how that control will be tested.

AI fluency is not control evidence. The buying exercise needs to establish not only whether an agent produces a convincing answer, but whether the workflow behaves correctly when evidence, permissions or systems fail.

Follow one clause from negotiation to execution

Use the hypothetical supplier agreement as a demonstration exercise. Give each shortlisted vendor the same sanitized contract, an approved audit-rights clause, a deliberately outdated alternative and an approval matrix. Have Legal specify the acceptable answer before the demonstration. Record the product version, configuration and test date so later results can be compared.

The business wants to finish the agreement. Legal wants the approved clause or a documented exception. Procurement needs the executed wording to become an obligation with an owner. Ask the vendor to demonstrate that whole sequence, including what happens when the inputs are wrong.

1. Find the governing evidence. Ask the agent to identify the changed clause and show the contract passage and current playbook position it used. If it selects the obsolete alternative, the reviewer should be able to detect the error before acting on it. 2. Separate recommendation from permission. Let it suggest a redline, then attempt to advance an exception without the designated approver. The expected result is a blocked action or escalation under the agreed policy. A persuasive explanation alone does not satisfy this test. 3. Change the document after approval. Replace the approved wording with a different version. Check whether the workflow requires the changed terms to be reviewed again before release, with a visible record of the change. 4. Carry the signed term forward. Once the approved agreement is executed in the test environment, check that the obligation record points to the signed clause, names an owner and reflects the agreed requirement. It should not inherit terms from an earlier draft. 5. Interrupt the workflow. Simulate an unavailable integration or missing permission. Check that the task remains visibly incomplete and routes to an accountable person, rather than reporting success or repeating an external action.

These are proposed acceptance tests, not reported vendor results. They will need adaptation for the institution’s policies and system connections. Passing them can provide evidence for a limited pilot; it does not establish regulatory compliance or prove that the system can handle every contract type.

Buy the level of automation the evidence supports

The level of AI authority should follow the level of control evidence.

The same institution may reasonably use different levels of autonomy across different contracting workflows. A missing control should narrow the proposed deployment rather than disappear inside an average capability score. Conversely, an effective existing approval process need not be replaced simply to introduce AI.

• When the source record is unreliable: prioritize version control, permissions and approved playbooks. Restrict experimentation to a controlled environment until reviewers can reliably identify the governing document.
• When evidence is reliable but exception handling is unproven: pilot extraction, draft preparation or issue detection with mandatory human review. Keep authority to accept material deviations with the designated approver.
• When controls and exception handling pass: consider a bounded workflow in which the agent performs specified actions. Define its permitted inputs, actions, escalation conditions and accountable owner before enabling it.

The same controls need to hold across the contract lifecycle. Approved terms, permissions and policy decisions should remain connected through execution, governance, compliance and obligation management. That continuity makes end-to-end CLM part of the agentic AI evaluation, rather than treating AI as a standalone drafting or negotiation capability.

Test the connection across the contract lifecycle

The evaluation should test how the agent operates across the contract lifecycle, not just whether it can produce an acceptable redline. The buying team needs to establish whether the same controls, approved decisions and governing evidence remain connected as the contract moves through drafting, negotiation, approval, execution and ongoing governance.

This is where end-to-end CLM becomes relevant.

Sirion’s agentic CLM capabilities span approved-template drafting, playbook-based deviation detection, explained redlining and extraction, alongside contract governance. Its financial services CLM capabilities include pre-approved clauses, contract-term tracking and validation workflows with confidence scores and model governance.

For a financial services buyer, the useful question is not whether those capabilities appear on a feature list. Ask to see them operate on the same agreement under the institution’s permissions, playbooks and approval rules.

Follow that agreement through the workflow. Check whether approved terms remain authoritative through execution and governance, and whether the resulting obligations reflect the executed agreement rather than an earlier draft. Confirm what happens when an approval is missing, a source is outdated or an integration is unavailable.

That demonstration provides more useful evidence than an agent producing a fluent answer in isolation. Configuration, integrations and availability should still be confirmed for the proposed deployment.

Measure the work left after automation

A faster AI-generated draft does not necessarily mean a faster or less expensive contracting process.

Measure the complete review, including the work people still have to perform. Record preparation, first review, corrections, escalations and administrative effort under both the existing process and the pilot.

Keep elapsed waiting time separate from staff effort. Faster draft generation may reduce hands-on work while leaving the approval queue unchanged.

A simple measure is:

Net staff time saved = existing staff effort − pilot staff effort, including corrections and exception handling.

Time should not be the only measure. Also record missed deviations, incorrect suggestions, unauthorized actions attempted or completed, and whether reviewers can reconstruct the decision from saved evidence.

Agree severity thresholds and stop conditions before testing. A material unauthorized action should trigger investigation regardless of the average time saved.

The buying team can then make a specific decision: deploy the tested workflow, keep it at assisted review, or repair the missing controls first. Expand the agent’s authority only after reviewing evidence from representative contracts and exceptions.

The next budget request can then name the task being automated, the measured benefit, the remaining failure modes and the controls required for the proposed level of autonomy.

Agentic AI can make contracting faster. The more important buying question is whether the organization can show why the agent took an action, whether it had authority to take it and what happens when the evidence or workflow fails. In financial services CLM, the level of automation should follow the strength of that control evidence.