Knowledge workers are using AI agents like Claude Code to create multi-layered research. The AI first generates several deep-dive reports on individual topics, then creates a meta-analysis by synthesizing those initial AI-generated reports, enabling a powerful, iterative research cycle managed locally.

OpenAI announced goals for an AI research intern by 2026 and a fully autonomous researcher by 2028. This isn't just a scientific pursuit; it's a core business strategy to exponentially accelerate AI discovery by automating innovation itself, which they plan to sell as a high-priced agent.

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A key capability is creating skills that continuously search the web, Reddit, and X for the latest techniques on a topic. The agent then incorporates this new knowledge to improve its future outputs and stay current.

Teresa Torres created a system using Python scripts and Claude to automate her research workflow. The script searches preprint servers like arXiv for keywords daily, and Claude then generates detailed summaries of saved papers, delivering a "research digest" directly to her to-do list each morning.

Vercel builds internal AI agents and tools, like an Open Graph image generator, to automate tasks that were previously bottlenecks. This not only increases efficiency but also serves as a critical dogfooding process, allowing them to innovate on their core platform by building the tools their own teams need.

The most effective way to use AI is not for initial research but for synthesis. After you've gathered and vetted high-quality sources, feed them to an AI to identify common themes, find gaps, and pinpoint outliers. This dramatically speeds up analysis without sacrificing quality.

A key strategy for labs like Anthropic is automating AI research itself. By building models that can perform the tasks of AI researchers, they aim to create a feedback loop that dramatically accelerates the pace of innovation.

The era of prompt engineering is ending. The future is proactive AI agents working in the background to surface critical information. These agents will automatically monitor for and alert teams to competitor launches, new patent filings, and regulatory changes, shifting from a manual 'pull' to an automated 'push' model of intelligence.