Service-as-software: A new economic model for the age of AI agents

The emergence of AI agents has the potential to do far more than just change software workflows; it can fundamentally redefine the economic value of software. This shift could be characterized as a move from software-as-a-Service (SaaS) to service-as-software (SaS).

That change in terminology might be subtle but it is, in fact, significant. Traditional SaaS is about tools: software that enables humans to solve problems. Service-as-Software (SaS), meanwhile, sells outcomes. It’s a new class of tool that doesn´t just enable work but instead automates the reasoning process itself.

Here are some examples of SaS in action:

• Marketing agents that draft campaigns end-to-end.

• Financial agents that model forecasts and simulate outcomes.

• Operations agents that triage requests across systems.

Companies will no longer pay for an agent based on seats or features. Instead they’ll pay based on its demonstrated alignment and impact.

The competitive frontier isn't just using these agents, it's in our ability to integrate them. There’s a critical distinction between an organization that allows employees to use public, standalone AI tools (simple adoption) and one that embeds an autonomous agent into its core business processes — like a supply chain or financial close. The latter group are the true early adopters. They will compete on capability velocity.

The human–machine cognitive shift

This new economic model is not just a strategic choice; it's a direct response to a practical change in what software can do. The primary driver is the shift from software that could only execute human instructions to software that can automate human reasoning. 

Software has always changed the way we think and act. From early mainframes to cloud-based systems, the interaction model was largely the same: humans reasoned, software executed. Each shift — from client-based computing to client–server networks, to web and cloud services — moved complexity around, but the cognitive contract remained the same. Humans had to instruct the machine.

Today, that contract is evolving. Interactions with software are no longer limited to issuing commands and waiting for deterministic responses. Systems increasingly interpret context, integrate feedback and propose actions. In other words, the cognitive burden is shifting: we are moving from controlling software to collaborating with it.

We can already see some of this happening. People turn to AI assistants for advice, reflection and support, even when these systems can’t comprehend context. The demand is human, not computational. Users today are actively seeking dialogue, guidance and co-creation.

From scripts to agentic systems

This cognitive shift from controlling to collaborating is made possible by a new class of software: agentic systems — software that acts on goals rather than just instructions. These systems:

• Operate beyond fixed workflows, selecting actions dynamically based on goals, constraints and context.

• Retain memory and refine understanding across interactions.

• Coordinate autonomously across tools, APIs and data sources.

Think of the difference between a GPS that waits for coordinates and a co-pilot that anticipates conditions and adjusts your route. Agentic systems don’t execute simple commands; they execute goal-directed reasoning under uncertainty.

Technically, they rely on:

• Reasoning engines that decide what to do next.

• Memory and context systems that track prior interactions.

• Tool orchestration layers that act autonomously across systems.

These capabilities introduce something previous software lacked: continuous feedback loops that allow systems to adjust behavior through interaction.

A new cognitive contract

When systems interpret goals and propose actions, the human–software relationship changes. Three principles define this new cognitive contract:

• Interpretable and auditable. Users need to be able to understand why the system made a decision.

• Aligned with human goals. The system’s objectives must match human intent and ethical boundaries. If that goal is misaligned, the system will deliver precisely the wrong outcome, even if it seems to work correctly. This isn't abstract: it means an autonomous agent trained on biased data will scale that bias at an unprecedented rate. It means an agent given a vague goal like maximizing engagement might learn to do so through harmful or polarizing content. Alignment is the non-negotiable safeguard against these autonomous, high-speed risks.

• Trained and iterated in real time. Systems continuously refine behavior based on feedback and interaction. That means human guidance is essential.

Together, these principles ensure agentic systems operate reliably and safely while augmenting human decision-making.

Organizational and design implications

Building with agentic systems isn’t about replacing old workflows; it's about managing autonomous ones. This demands a new role, the cognitive orchestrator, which is focused on concrete, practical tasks: 

• Feedback loop design. This is more than a “thumbs up/down.” It means designing the review process. For example, an agent's financial forecast isn´t done; it's proposed. The orchestrator designs the UI that allows a human expert to review, approve and correct the agent's reasoning step-by-step, creating a verified data-set for future refinement.
  
  

• Managing uncertainty with guardrails. This is the new risk management. Guardrails are hard-coded business rules (e.g., “an agent may never contact a client more than twice a week”). Circuit breakers, meanwhile are operational failsafes (e.g., “If an agent´s proposed ad-spend exceeds the 24-hour budget by 10%, halt the campaign and require human approval”).
  
  

• Measuring alignment. With SaS we shift from “did the task get done?” to “how well did it get done?” The new metric is an alignment score, a quantifiable measure of how often an agent's autonomous decision(like a support-ticket triage) matches the judgement of a human expert in A/B testing or post-incident reviews. Value, then, needs to be measured in effectiveness, alignment with goals, and trustworthiness.

The role of humans shifts from operator to cognitive orchestrator, guiding agentic systems to amplify intelligence and accelerate innovation. 

Conclusion: Cultivating the reasoning agent

We are at a pivotal moment. The shift to hybrid intelligence — humans co-thinking with agentic systems — is already creating measurable value. We see this in the competitive advantages gained by firms that automate complex processes, from high-frequency trading algorithms to dynamic supply-chain management, where a human-in-the-loop model is proven to be more effective than either humans or AI working alone.

SaS is the commercial framework for this new reality. In simple terms, it means outcomes, trust, and alignment are no longer just features; they are the fundamental basis of the business model.

This reveals the central, practical tradeoff: we cannot sell autonomous outcomes (the SaS model) without first mastering the new operational model required to produce them (alignment, trust, and cultivation). The responsibility isn't a separate, ethical point; it is the work. How we train, guide, and trust these agents will determine whether they are not just effective, but commercially viable.

The next era of software is here — no longer just passive tools built for human intelligence, but reasoning agents built with it.