AI agents are a powerful way to make software tests faster, more efficient, and more scalable.
But they can also make the process very expensive. Between token costs, API fees, agents’ CPU and memory consumption, and more, the costs of agentic testing can add up to the point that they exceed the price of testing the old-fashioned way: using automation frameworks with tests that engineers write and execute manually.
It doesn’t have to be this way. The cost of agentic testing varies widely depending on exactly how QA teams take advantage of AI agents, and which parts of the testing lifecycle in which they feature. By being strategic about where and how agents play a role in the testing process, it’s possible to maximize their impact while minimizing their cost.
The economics of AI agents in software testing: An overview
Deploying AI agents to help write, execute, respond to, and/or manage tests can incur costs due to a variety of factors:
- The token fees incurred when agents interact with LLMs.
- Other API costs (beyond token fees) prompted by connections to LLMs.
- The cost of the human time devoted to setting up and managing agents.
- The human cost of having to deal with mistakes that agents make due to issues like hallucinations or poorly written prompts.
- The CPU, memory, networking, and storage costs associated with hosting AI agents.
- The costs of software tools (such as agent meshes) that teams may choose to deploy alongside agents themselves.
The variety of costs here show why agentic testing can become so pricey. Not only do agents consume tokens in the course of their multi-step exchanges with LLMs, they also exact human and infrastructure-related expenses that are necessary to support agentic software testing.
The fast and easy solution to optimizing agentic testing costs
Agentic testing can be exorbitant, but it doesn’t have to be. The key to reducing costs is to avoid using agents when they’re not necessary.
For most teams, this means using traditional types of automation tooling to handle tasks like test orchestration and management. While agents can do these things, they don’t add anything to the process that test orchestration platforms don’t already provide.
Meanwhile, agents can and should be used for tasks like helping to write tests. They may also be useful for troubleshooting and updating failed tests, especially in cases where it’s truly necessary to iterate across multiple fixes. For simpler troubleshooting workflows, however, AI agents are often overkill.
So, the “one simple trick” for keeping agentic costs in check is using AI agents only where they add value in end-to-end testing workflows. When carrying out testing workflows, QA engineers should also leverage test automation frameworks like Playwright, arguably the most powerful framework available today.
But Playwright is just the automation engine, and Leapwork Play complements Playwright and AI agents with an orchestration layer for managing Playwright-based validation. Teams can use AI-assisted authoring while keeping test execution deterministic, with no model call at runtime. Play also supports importing existing Playwright tests and repositories, so teams can build governance, reuse, and audit trails around their existing investment without rewriting it.
