Contracting for Agentic AI Is Starting to Look Like Outsourcing

Enterprise software contracts were built for tools. Agentic artificial intelligence (AI) is forcing lawyers and procurement teams to rethink them for AI systems.

For two decades, Software-as-a-Service (SaaS) agreements revolved around access. Companies paid per seat or per license to use software employees logged into. Vendors promised uptime, security standards and service credits if systems failed. Liability was capped. The risk profile was manageable because the software did not act on its own. Humans did.

Agentic systems change that equation. These systems do not simply generate text or analyze dashboards. They approve refunds, reconcile invoices, negotiate with suppliers, trigger payments and move data across systems. They operate inside live workflows. When they make a mistake, the impact is operational and financial, not cosmetic.

According to global legal firm Mayer Brown, contracts for agentic AI increasingly resemble managed services or outsourcing agreements rather than traditional SaaS subscriptions. Similarly, U.S. law firm Stoel Rives argues that agentic deployments require enhanced governance, supervision and risk allocation frameworks. The shift reflects a simple reality: when software starts taking action, responsibility shifts with it.

From Access to Accountability

Under classic SaaS models, performance was measured by uptime percentages and response times. Agentic systems require new metrics tied to outcomes: How accurate are automated decisions? How often does a human need to intervene? What is the financial error rate? How are decisions logged and audited?

Mayer Brown said in its Tuesday (Feb. 17) blog post that agentic contracts must address supervision requirements, human-in-the-loop provisions and audit rights, moving well beyond boilerplate SaaS terms. Enterprises want visibility into how models are trained, what data sources they rely on and how decisions are explained after the fact. In regulated sectors such as financial services or healthcare, auditability is not optional. It is mandatory.

Liability structures are also under pressure. Traditional SaaS contracts often limit vendor liability to the fees paid over a defined period. That framework becomes harder to defend if an autonomous system moves millions of dollars incorrectly or violates compliance rules. Buyers are pushing for expanded indemnities, clearer risk allocation and stronger governance clauses, a dynamic highlighted by Stoel Rives.

Data governance is another fault line. Agentic systems frequently require broader system access to function effectively. That access raises questions about data ownership, retention and cross-system risk. Contracts are starting to specify granular permissions, monitoring obligations and termination rights if risk thresholds are breached.

In short, as AI moves from assistant to actor, contracts are being rewritten around accountability.

Pricing Is Also Shifting

The contractual shift is tightly linked to a pricing transformation.

SaaS pricing was optimized for scale through seats and subscriptions. AI agents do not map neatly to either. One agent may replace the work of several employees. Another may operate intermittently but handle high-value transactions. Charging per user makes little sense when the user is a system.

As reported by PYMNTS, AI pricing models are moving beyond flat subscriptions toward consumption and usage-based frameworks. With agentic systems, that trend is accelerating. Vendors are experimenting with pricing tied to transactions processed, workflows automated or financial value delivered.

In some cases, pricing begins to resemble business process outsourcing. Enterprises may pay per refund resolved, per invoice reconciled or as a percentage of savings generated. That model aligns vendor revenue with measurable outcomes, but it also increases contractual complexity. Defining performance benchmarks, service levels and acceptable error rates becomes critical because payment depends on them.

SaaS was built on access. Agentic AI is being built on execution. As digital systems move money, approve transactions and negotiate on behalf of companies, contracts and pricing models are adapting to reflect a new reality: when software works like a worker, it must be governed and paid like one.

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