Risk-Sharing in Agentic Delivery Who Owns the Outcome When Agents Write the Code?

Outcome-based delivery makes a promise: pay for the result, and the provider carries the risk of reaching it. Agentic delivery complicates that promise with a question the old model never had to answer. When an AI agent writes the code, and that code fails in production, who is accountable?

“The AI did it” is not an answer a regulator, an auditor, or a board will accept. Yet most enterprises are heading into agentic delivery without having settled the question. Deloitte’s 2026 State of AI in the Enterprise survey found that while 74 percent of organizations expect to use AI agents at least moderately within two years, only 21 percent have a mature governance model for autonomous agents. Adoption is running well ahead of the ability to say who owns what.

For outcome-based agentic delivery to work, that question has to be answered explicitly, in the contract, before the agents start writing code.

Start with the principle everything else depends on. An agent is an instrument, not a liable party. It cannot hold responsibility, carry insurance, or answer to a regulator. Accountability for an agent’s output always rests with a human and an organization, no matter how autonomously the code was produced.

This is the heart of the Carbon + Silicon model: agents execute, people and the firms they work for remain accountable. In agentic AI for enterprise delivery, the technology changes who writes the code, not who owns the result. The engineer who directed the agent, the provider who deployed it, and the enterprise that shipped it are exactly as responsible as they were when a person typed every line.

The practical consequence is that an outcome-based agentic contract must name an accountable owner for the result, and that owner must be an organization that can stand behind it, not a model. Outcome pricing only works if someone can actually be held to the outcome, and an agent can be held to nothing.

With accountability anchored, risk divides into three parts.

The provider owns delivery and quality risk. This is the point of outcome-based pricing, and the basis of the Services-as-Software model: when delivery is bought as a measurable result rather than as staffed capacity, defects, rework, and non-compliance in the delivered software are the provider’s problem, not the buyer’s. When agents do the writing, this does not soften. If anything it sharpens, because the provider is now accountable for output produced at machine speed and volume, which raises the stakes on software quality engineering and validation. A provider pricing to outcomes is betting its fee on catching what the agents get wrong.

The buyer owns what the buyer controls. Scope decisions, timely access to systems, data quality, and the business calls that shape the work remain the buyer’s responsibility. No credible provider will guarantee an outcome whose inputs the client controls, and a contract that pretends otherwise will not survive its first dispute.

The gray zone in between is where shared-risk structures earn their place. For results neither party fully controls, gainsharing and risk-reward arrangements let both sides carry a proportionate share of upside and downside, provided the attribution rule is agreed in advance. Shared reward without shared risk is not partnership, it is optimism.

Here is why the 21 percent governance figure matters commercially, not just operationally. You cannot take responsibility for what you cannot see. A provider that cannot trace what an agent did, on what basis, and who reviewed it, cannot credibly own the outcome, and a buyer has no way to verify that it does.

That is why governance is the mechanism that makes risk-sharing enforceable rather than aspirational. Human-in-the-loop checkpoints on consequential decisions, complete audit trails of agent actions, and traceability from requirement to shipped code are what let an organization actually stand behind agent-generated work. Deloitte’s respondents named legal, intellectual property, and regulatory compliance (50 percent) and governance and oversight (46 percent) among their top AI risks, and in regulated industries these are not optional. An agent-written change that cannot be explained to an examiner is a liability regardless of how well it performs.

This is the difference between agentic delivery that can be priced to outcomes and agentic delivery that cannot. The governance layer is what converts a provider’s willingness to own the result into something a buyer can trust.

Translating all of this into a contract comes down to a handful of clauses that a buyer should insist on and a serious provider should welcome.

An accountability clause that names the party responsible for the outcome regardless of how the code was produced, closing the “the agent did it” gap before it opens.

Clear ownership of AI-generated code and its intellectual property, since half of enterprise leaders now rank legal and IP exposure among their highest AI risks.

Warranties and indemnities covering defects, security, and IP infringement in delivered work, so the provider’s accountability has teeth.

Governance and audit requirements written as conditions of delivery, not aspirations: human review on defined decision points, retained audit trails, and traceability the buyer can inspect.

Balanced SLA remedies, with downside for missing the outcome alongside any upside for exceeding it. Risk-sharing that only shares the good outcomes is not risk-sharing.