arXiv:2606.16432v1 Announce Type: new Abstract: User instructions are often underspecified because humans rely on implicit assumptions about the surrounding environment. For large language model (LLM) agents operating in information-rich digital and physical environments, these assumptions cannot be inferred from the instruction alone; they must be recovered from the current state of tools, data, interfaces, and observations. Effective execution therefore requires agents to identify missing context, ground it in observed evidence, and carry it forward into subsequent actions. We show that curr
The rapid advancement in large language models has exposed the limitations of static prompts, making explicit contextual grounding a crucial next step for practical agent deployment.
Improving AI agents' ability to understand and utilize context from digital and physical environments is critical for them to autonomously perform complex tasks and integrate into real-world workflows.
Agents will move beyond simple instruction following towards more robust, adaptive, and context-aware operation, reducing the need for constant human oversight and intervention.
- · AI developers
- · Automation software providers
- · Digital platforms with complex interfaces
- · Industries adopting autonomous agents
- · Purely prompt-based AI solutions
- · Tasks requiring manual repetitive data interpretation
- · Workflows dependent on human-in-the-loop contextualization
AI agents become significantly more capable of handling novel and underspecified tasks.
An acceleration in the deployment of AI agents across various industries, replacing manual white-collar workflows.
The definition of 'work' fundamentally shifts as agents assume more complex, context-dependent responsibilities, leading to new economic models and skill demands.
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