arXiv:2605.26321v1 Announce Type: new Abstract: AI agents are beginning to complete valuable, long-horizon business operations tasks, but training and evaluation environments for enterprise work still struggle to balance realism, verifiability, and scale. Environment and task creation frequently suffers from a failure mode we call artifact drift: when instructions, environments, oracles, and verifiers are created by loosely coupled processes, they frequently disagree on what a task requires, producing environments that are unsolvable, reward-hackable, or inconsistent. We introduce Anchor, a ta
The rapid development and deployment of AI agents in complex business operations are revealing core challenges in their reliable and verifiable function, making robust benchmarking critical.
Reliable benchmarking and mitigation of 'artifact drift' are essential for scaling AI agent deployments and ensuring their trustworthiness and effectiveness in enterprise settings.
The introduction of Anchor proposes a structured approach to generate consistent and verifiable benchmarks, potentially accelerating agent development and adoption by addressing a key failure mode.
- · AI Agent Developers
- · Enterprises Adopting AI Agents
- · AI Testing & Evaluation Platforms
- · Companies with Poorly Verified AI Agents
- · Manual Testing Processes
Improved reliability and increased adoption of AI agents across various business operations.
Faster iteration cycles and more competitive development in the AI agent ecosystem due to standardized evaluation.
The acceleration of fully autonomous enterprise workflows, leading to significant productivity gains and shifts in labor requirements.
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Read at arXiv cs.AI