Quick answer
Agent as a service, defined
Agent as a service (AaaS) is a delivery model in which AI agents are provided as a service: your product or team calls an API or platform, and managed agents plan, execute, and return finished work.
The term carries two senses that are worth separating. In the infrastructure sense, agents as a service means agents delivered through an API on managed infrastructure: you send a task, a hosted agent runs it, and the result comes back. That is what HarnessRouter provides, with Codex, Claude Code, and Hermes as the agents. In the business sense, AaaS means finished agents sold as digital workers: a support agent that resolves tickets, a research agent that produces briefs, an outcome-priced replacement for a software seat.
The term picked up visible momentum in 2026. Analysts frame AaaS as the successor stage to SaaS, and investor essays quote Nvidia CEO Jensen Huang's line that every SaaS company will evolve into an AaaS company. The demand behind it is broader than the term: Gartner projects that 40% of enterprise applications will feature task-specific agents by the end of 2026, whether those agents are sold as workers or embedded in software.
The two senses do not compete. Digital-worker products are built on agent infrastructure, and agent infrastructure is itself consumed as a service. This page covers both, and where each maps onto the stack.
The shift
From SaaS seats to AaaS outcomes
The SaaS comparison belongs to the business sense of the term. The classic SaaS contract was simple: software stays ready, humans do the work, and the vendor typically bills per seat. AaaS changes the unit of value. When an agent completes the work itself, the seat stops being the natural unit, and vendors increasingly experiment with billing by tasks completed, hours of agent work, or outcomes delivered.
That shift is where both the appeal and the hard questions live. Buying outcomes is attractive; being accountable for an autonomous worker's output forces questions that per-seat software rarely had to answer this directly: how reliable is the work, what does a run cost, and can the output be audited.
It also predicts where the model lands first. The use cases most often cited for AaaS are support, research, data operations, and sales development, and they share a property that fits outcome billing: the outcomes are countable and checkable.
The stack underneath
What an AaaS product actually runs on
Underneath, AaaS products share the same anatomy: an agent is a model plus a harness. The model does the reasoning. The harness does everything else: tools, context, permissions, and the isolated environment where multi-step work actually happens.
That makes harness infrastructure a load-bearing layer of an AaaS business. Sessions that survive long tasks, sandboxes that contain untrusted work, streaming that shows customers progress, artifacts that can be reviewed and audited: an AaaS team has to get this plumbing from somewhere, whether by building it, assembling it from parts, or consuming it as a service.
The buy side of that decision is agents as a service in the infrastructure sense, and the mechanism behind it has a name: harness as a service (HaaS). The provider operates the harness layer that makes agents dependable, and the digital-worker product keeps its focus where its differentiation lives: the workflow, the domain knowledge, and the customer.
Taxonomy
AaaS and HaaS: one supply chain
In the business sense, agent as a service is what the end customer buys: a finished digital worker. Harness as a service is what the builder buys: managed infrastructure that runs agent harnesses. The two connect, because delivering agents as a service at the infrastructure level is exactly what a HaaS does.
HarnessRouter is agents as a service in the infrastructure sense, and it is the world's first unified interface for agent harnesses: one API through which any product, including a digital-worker AaaS product, runs Codex, Claude Code, or Hermes in managed, isolated sessions with streaming and reviewable artifacts. The harness is a request parameter, so a team can route each task class to whichever configuration wins on measured cost, quality, and latency.
FAQ
Agent as a service: common questions
What is agent as a service (AaaS)?
Agent as a service is a delivery model where AI agents are provided as a service rather than run on your own infrastructure. In the infrastructure sense, that means agents consumed through an API on managed sessions, the model HarnessRouter provides. In the business sense, it means finished agents sold as digital workers, with pricing that follows usage or outcomes rather than seats.
What is the difference between SaaS and AaaS?
SaaS sells software that humans operate, classically billed per seat. AaaS sells agents that do the work themselves, with billing experiments moving toward tasks, agent time, or outcomes. Analysts describe AaaS as the successor stage to SaaS, and Gartner projects 40% of enterprise applications will feature task-specific agents by the end of 2026.
What is the difference between AaaS and HaaS?
They overlap by design. A HaaS delivers agents as a service at the infrastructure level: managed execution of agent harnesses, with sessions, sandboxes, streaming, and artifacts. The business sense of AaaS, finished agents sold as digital workers, is the product layer built on top. A digital-worker company is a natural customer of the layer, and the layer itself is agents as a service to its own users.
What infrastructure does an AaaS product need?
Durable sessions for long-running work, sandbox isolation, progress streaming, artifact storage and review, retries and recovery, and a way to compare harness and model configurations on cost, quality, and latency. Teams either build that stack or consume it through a harness-as-a-service layer such as HarnessRouter, which is free to build on, with credits only consumed when tasks run.
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