Enterprises that deploy autonomous software agents are finding that the familiar software‑as‑a‑service (SaaS) playbook no longer matches the risk profile of “agentic AI.” As Bain & Company notes, generative and agentic AI are already automating tasks that were once “human + app” workflows, from drafting code to handling support tickets. When an AI system can plan, execute and act without a human click, the contractual relationship shifts from a simple license to a service that performs business functions on the company’s behalf.
That shift is the focus of the legal update titled Contracting for Agentic AI Solutions: Shifting the Model from SaaS to Services. It argues that the industry should look to the business process outsourcing (BPO) playbook—where providers are already bound by detailed service definitions, performance warranties and audit rights—to craft contracts that balance autonomy with accountability.
Why the traditional SaaS model falls short
In a typical SaaS agreement, the provider delivers a hosted platform and the customer receives a non‑exclusive right to access and use it. The contract usually limits the provider’s obligations to platform availability and data security, while the buyer bears responsibility for how the software is employed. That framework works when the AI tool is a “co‑pilot” that merely suggests content. As the source explains, “Agentic AI … can autonomously plan and execute multi‑step tasks to achieve a goal,” turning the AI into an actor that can, for example, approve payments or enroll employees in benefits without human oversight.
Because the AI now performs core business processes, the risk of errors, compliance breaches or unintended decisions moves from the buyer to the provider. A SaaS‑style “as‑is” disclaimer—often phrased “THE SERVICE IS PROVIDED AS‑IS, WITH ALL FAULTS”—doesn’t address the demand for performance guarantees, outcome‑based service levels or liability for autonomous actions.
Borrowing from business process outsourcing
The BPO sector has long grappled with similar challenges when a company outsources functions to a third‑party workforce. Contracts in that space contain clauses that define the exact tasks, set guardrails, allocate risk and provide the buyer with audit rights to verify compliance. Adapting those provisions to AI agents offers a “hybrid” model that retains the scalability of SaaS subscriptions while adding the accountability of a service agreement.
McKinsey’s discussion of the “agentic AI advantage” reinforces this trend, highlighting that organizations must now manage both the technology and the governance framework that directs autonomous decision‑making as they scale AI adoption.
Six contractual building blocks for agentic AI services
1. Definitions and scope of service
The contract should describe the “Service” as the specific tasks the provider will execute with AI agents, not merely a software platform. It must spell out the “delegation of authority” (what the AI may do) and the “policy guardrails” (when it must defer to a human‑in‑the‑loop). Clear thresholds for escalation protect both parties and create a defensible liability line.
Practice tips for buyers:
- Identify every business process the AI will handle.
- Define critical steps that require human approval.
- Set precise delegation limits and escalation triggers.
2. Service warranties
Instead of an all‑caps “as‑is” disclaimer, a BPO‑style warranty promises that services will be performed in a “good, professional, diligent and workman‑like manner” and in compliance with applicable law and the agreed‑upon guardrails. The warranty extends to both the human staff who develop and monitor the agents and the agents themselves, treating the AI’s output as a service deliverable.
Practice tip: The tighter the definition of delegation and guardrails, the more likely a provider will accept a performance warranty.
3. Outcome‑based service level agreements (SLAs)
Traditional SaaS SLAs focus on uptime—often 99.9% or higher. For autonomous agents, the relevant metrics are outcome‑oriented: accuracy of processed invoices, timeliness of ticket resolution, or the rate of consumer complaints arising from AI actions. Service credits can be tied to failures in these metrics rather than mere downtime.
Practice tips:
- Translate business expectations (“no mistakes,” “fast response”) into measurable targets.
- Negotiate outcome‑based SLAs alongside standard availability clauses.
- Ensure remedies such as service credits are not limited to “sole and exclusive” options.
4. Indemnification
In a SaaS deal, indemnities often cover only third‑party IP infringement. For agentic AI, buyers should seek indemnification for third‑party claims that arise from the AI’s autonomous actions—provided those actions stay within the agreed scope. Typical carve‑outs protect the provider from harms caused by the buyer’s misconfiguration, faulty data or a human‑approved escalation.
Practice tip: Propose a tailored indemnity clause that specifies the categories of claims covered and the agreed‑upon exclusions.
5. Governance and audit rights
Audit rights in SaaS contracts usually limit the buyer to a SOC 1 or SOC 2 report and may even allow the provider to audit the buyer. A service‑oriented model flips this, granting the buyer “right to transparency” over AI decision logs and the ability to assess compliance with SLAs and guardrails.
Practice tips:
- Require the provider to maintain structured decision logs for every autonomous action.
- Secure contractual rights to audit those logs and evaluate performance against SLAs.
- Plan a technical and operational audit framework before signing.
6. Data, IP rights and model training
Many SaaS agreements give providers a broad, perpetual license to use all data generated on the platform, allowing them to train future models on a customer’s confidential inputs and outputs. A BPO‑style clause instead affirms that the buyer owns both the data it feeds to the AI and the outputs produced. Any use of that data for model training must be expressly consented to, typically in a de‑identified form.
Practice tip: Insert a clear prohibition on unauthorized model training and negotiate any desired data‑sharing arrangements as separate, mutually beneficial agreements.
Putting it together: a hybrid contract for autonomous agents
The emerging consensus is that companies should not discard the SaaS subscription framework entirely but should augment it with the performance, governance and risk‑allocation provisions that have long governed BPO relationships. By doing so, buyers gain the scalability of cloud‑based AI while ensuring that autonomous agents operate within well‑defined, auditable boundaries.
Legal practitioners and procurement teams are already drafting “hybrid” agreements that blend subscription pricing with outcome‑based SLAs, explicit indemnities and robust audit rights. As the market for agentic AI expands, vendors are expected to standardize these clauses, making it easier for enterprises to adopt autonomous solutions without exposing themselves to unmanaged risk.
Stakeholders should watch for the next round of industry guidance, which is likely to appear in forthcoming legal updates and vendor‑specific contract templates. Those updates will clarify the precise language that balances innovation with accountability.
Readers are encouraged to share their experiences with agentic AI contracts and to comment on how their organizations are adapting to this new service model.
