AfterQuery Becomes Agent Readiness Layer

This points to AfterQuery moving from a niche training vendor into the quality control layer for enterprise agents. The important shift is from selling one off benchmark or data projects to sitting in the loop wherever a model touches real software, whether that is an API, an MCP server, a terminal, or an internal database. That makes AfterQuery useful across many model stacks, not tied to one lab or one app.

• AfterQuery already sells the three pieces this layer needs, training data, RL environments, and evals. Its environments are built around real workflows, like a model using tools in the right order inside a Docker container, browser, or business system, then getting scored on whether the task actually worked.
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• The market is moving in the same direction. Scale launched RL Environments in February 2026 and said nearly half of new data training projects now involve these environments, which shows tool rich agent training is becoming a mainstream budget line, not a research side project.
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• Enterprise demand is broadening beyond frontier labs. McKinsey found 62% of organizations are experimenting with AI agents, but only 23% are scaling them, which creates room for vendors that can harden agents before deployment across cloud apps, internal tools, and governed enterprise systems.
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The next step is for agent readiness to become an always on layer in enterprise AI delivery. If AfterQuery can package its bespoke work into reusable connectors, environment templates, and validation workflows, it can expand from high touch lab engagements into a broader control point for how companies train, test, and ship agents across software stacks.