Contracting for Effective Agentic AI Pilots

Introduction: Agentic AI Pilots Matter

A hallmark of agentic AI is its use of probabilistic models to plan, make decisions, and execute goal-based tasks that have real-world effects, often with substantial autonomy and limited human oversight. Depending on the use case and controls, AI agents may bring game-changing gains or cause irreversible harm.

This tension often pulls companies toward one of two unproductive extremes on deploying agentic AI. They may move too quickly, deploying AI agents before adequately understanding their effectiveness, reliability, limitations, controls, and potential consequences (leading to wasteful “agentic sprawl” or financial, legal, competitive, or reputational harm). Alternatively, they may become so concerned about the risks that they delay or abandon a use case capable of creating significant value.

A well-structured pilot program—one designed to generate real evidence of value and risk, rather than merely build momentum toward full deployment—can address both concerns by providing a sound basis for determining whether, where, and under what conditions broader deployment is appropriate. Such a pilot is more than a vendor-led demonstration or proof-of-concept. Given the unique characteristics of agentic AI, those who plan or contract for its evaluation would do well to liken the process to vetting a potential employee, rather than simply testing software to confirm that it provides accurate outputs with acceptable speed, usability, and uptime.

Just as a prudent employer would evaluate a candidate’s job-related skills, bad-actor risks, and financial justification before hiring, a prospective agentic AI deployer should design the pilot to assess, at a minimum, the system’s (1) ability to do the job, (2) safety and security, and (3) anticipated profitability. Well-crafted pilot agreements reflect and support those objectives.

The Agent’s Ability to Do the Job

Define the Job

An effective pilot agreement should define the target use case precisely. It should identify (or ensure the parties’ operational teams establish):

• The business process the agent will support;
• The data, tools, credentials, and external systems it may access;
• The users who may interact with it; and
• The actions it may recommend, initiate, or complete.

These restrictions can be defined collectively as the AI agent’s operating parameters. They should reflect the organization’s delegation-of-authority rules and establish any monetary, operational, regulatory, or other limits on the AI agent’s authority. A procurement agent, for example, might be permitted to draft purchase orders below a defined dollar threshold but be prohibited from approving or transmitting them without human authorization. Alternatively, it might be allowed to issue certain purchase orders but not modify or deviate from approved budgets.

Evaluate Capabilities

Once the AI agent’s “job” has been defined, the pilot should place the agent in that “job” to generate evidence supporting a deployment decision. Thus, the parties should identify what the organization needs to learn and how the pilot will generate that evidence.

Specific use-case measures may include goal-completion rates, accuracy (compared against acceptable human work product), ability to interpret ambiguous or nonstandard inputs, processing time, exception and escalation rates, user feedback, hallucination rates, unauthorized actions, and the frequency and severity of errors. Minimum required performance levels in the agreement should distinguish issues that may be addressed through remediation and retesting versus events or conditions that require the pilot to be paused or terminated.

The agreement should define the methodology, data sources, evaluation responsibilities, and decision rules by which those metrics will be assessed. Relevant considerations include who will perform the evaluation, the testing benchmark data sets, the human or system baseline, the measurement period, and the treatment of excluded or failed transactions.

Testing should cover ordinary workflows, edge cases, ambiguous inputs, attempts to exceed authorized boundaries, failures of connected tools, and situations requiring human escalation. It should assess whether the agent uses only approved data and tools, responds appropriately to uncertainty, avoids repeating or compounding failed actions, recovers from errors, and fails safely.

Throughout the pilot, the company should monitor—or require the provider to monitor—the agent for deviations from its operating parameters and other foreseeable concerns associated with autonomous operation. Testing and monitoring should be documented in logs sufficient to reconstruct the AI agent’s material activities, including actions attempted or completed, relevant inputs, tools or external systems used, approval status, timestamps, errors, overrides, retries, and outcomes.¹

Safety and Security

Before allowing an AI agent to operate with sensitive data or systems, or in ways that could have financial, legal, or other consequences that cannot readily be reversed, the company should evaluate the system’s safety and security.

This means evaluating the system as a whole—the AI agent together with the models, tools, integrations, and controls on which it depends. It involves more than establishing guardrails and limiting access credentials. It requires testing whether the system as configured reliably enforces those constraints (for example, whether the agent accesses unauthorized databases or disregards data privacy rules, spending caps, or other hard limits). It also requires carefully planning for appropriate human-in-the-loop dynamics;² evaluating responses to adversarial inputs such as prompt-injection attacks; considering interactions with third-party models, tools, and integrations; and testing the efficacy and speed of kill switches. Audit-trail clarity is also a key part of ongoing safety and security, so the pilot should assess the readability and completeness of log files that may be needed for forensic or compliance reviews.

Care should also be taken to distinguish theoretical oversight (i.e., a human could override or stop the agent) from practical oversight (i.e., the human also has meaningful opportunity to know when intervention is needed). Conversely, excessive alerts can undermine efficiency and cause alert fatigue, increasing the likelihood of disregarding a valid warning. Therefore, measuring human reviewers’ catch rates may be as important as measuring the agent’s error rates.

A pilot agreement can address certain safety and security risks contractually. For example, it can prohibit the provider from expanding the agent’s use case, access, integrations, or autonomy without the company’s written approval and compliance with the company’s change control procedures. It can also include assurances by the provider that the agent will not modify its own operating parameters or falsely represent itself as human, as well as provisions governing notice, remediation, suspension, and responsibility when specified risks materialize.

Those protections are important, but they are not sufficient. Contractual terms generally allocate the consequences of a failure; they do not prevent the failure from occurring or establish that the system can be operated safely. The provider is not likely to assume all risks, including those associated with the agent’s inherent unpredictability, the deployer’s particular use case, or third-party models, tools, and integrations. The company should therefore supplement the agreement with appropriate technical, operational, and vendor due diligence, including an assessment of the provider’s willingness and practical ability to manage and bear agent-related risks in production.

The pilot agreement should support that diligence by requiring rigorous pressure-testing of the agent’s safety and security, meaningful access to the resulting evidence, and criteria for determining whether identified risks have been adequately addressed. An agreement that contains legal protections but does not produce reliable evidence about how the agent behaves under foreseeable and adversarial conditions leaves the company without a sound basis for deploying.

Profitability

The pilot may  measure not only how well and safely the agent performs, but also its financial costs and benefits.

Costs include not only license or service fees, but also computational costs and third-party API or model-token fees. Putative benefits must also be evaluated holistically: an agent may reduce the number of frontline workers needed but require more supervisors to handle increased transaction volumes or exceptions.

The agreement can promote measurable and actionable economic assessment by identifying the relevant cost drivers (such as model calls, token volumes, other compute and tool usage, storage, retries, and human review) and requiring the appropriate party to report associated consumption and cost data. Reports should ideally use agreed upon measuring conventions and be created at the task or transaction level in an exportable format. The agreement can also specify the productivity data each party will supply and the methodology for calculating cost per successful outcome, including escalations, exceptions, and rework, as well as permit the company to verify the resulting data and use it in its internal business case.

To make measured pilot economics meaningful, the agreement should address how they translate into production. This may involve prospective pricing commitments, volume tiers, allocation of third-party charges, fee adjustments for rework attributable to agent error, and notice or consent before cost-increasing model or configuration changes.

What Next?

Once the pilot has run its course and the data has been analyzed, the agreement should define what happens next. The move from pilot to deployment should not occur automatically, and an inconclusive or unsuccessful pilot should not leave the organization locked into functionality it no longer wants. A clearly defined endpoint and an affirmative decision requirement help ensure that the pilot’s lessons translate into the right result, rather than becoming background noise as the pilot slides into a production deployment by default.

Accordingly, the parties should address exit from the outset, including the treatment of the company’s data and testing logs and the provider’s agentic assets. They should consider alternatives to a binary “go/no-go” outcome, as an inconclusive pilot may warrant extension, remediation, retesting, or a narrower deployment rather than immediate expansion or abandonment.

These provisions prevent a pilot from expansion by inertia before the organization has determined that the system is ready. At the same time, they create a defined path to deployment when the evidence supports it, reducing the likelihood that uncertainty alone will cause the organization to abandon a valuable use case.

Conclusion

Pilot programs are key to successful, purposeful agentic AI deployments. Pilot agreements can foster value-driven decision-making based on the agent’s ability to do the job, its safety and security, and an honest assessment of its profitability. With these factors defining success or failure, and clear terms governing expansion or exit, a pilot agreement can help the organization avoid both premature scale and unnecessary abandonment.

¹ The objective is not necessarily to reproduce the model’s internal reasoning, which may be proprietary or technically inaccessible. Rather, it is to create an operational record showing what the agent did, what information and authority it used, and whether required controls operated as intended. The agreement should address access to these records, retention, export formats, audit rights, confidentiality, and, where relevant, cooperation with internal investigations or regulatory inquiries.

² In considering human-in-the-loop dynamics, an important question is which actions are reversible. Actions with irreversible consequences should require human review, at least until the agent has earned an acceptable track record. Reversibility may therefore be more important than accuracy in determining how much autonomy to grant.