agentic era
Beyond Market Intelligence keeps agentic era in one place: 3 stories so far. The section currently leads with “How AI agents are reshaping the data scientist's toolset”, “How engineering leaders are shaping production systems for an agentic future”, and “Explore how AI agents are reshaping the tools we use to build interfaces.”. Jupyter Notebooks gave data scientists a linear home for a linear workflow. The QCon San Francisco 2026 lineup reads like a blueprint for the next era of engineering. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every agentic era story on Beyond Market Intelligence, newest first.
How AI agents are reshaping the data scientist's toolset
Jupyter Notebooks gave data scientists a linear home for a linear workflow. But that workflow is changing. As one data scientist notes, LLMs now write most of the code. The real bottleneck isn't execution anymore, it's orchestration. Why organize work around code cells when prompts can drive results? The ipynb abstraction may be holding us back. For those building agentic workflows, our piece on building guardrails for safer AI data access offers practical guidance on structuring these new pipelines.
How engineering leaders are shaping production systems for an agentic future
The QCon San Francisco 2026 lineup reads like a blueprint for the next era of engineering. Airbnb, OpenAI, Netflix, and Honeycomb are sending practitioners, not visionaries, to share how they build production systems as AI agents take on more responsibility. This is not theory. It is the messy, practical work of evolving infrastructure under real load. For deeper context on the talent shifts shaping this world, see our coverage of how OpenAI parts with three researchers over data handling concerns.
Explore how AI agents are reshaping the tools we use to build interfaces.
ArrowJS is asking a question worth pausing on: if AI agents are writing more of our interfaces, why are we still rendering them like it's 2019? This piece digs into whether ArrowJS actually fits the agentic era or just adds another layer of complexity. It's a practical, grounded take, and it pairs well with our earlier look at how LLMs navigate token space. Both point to the same truth: the tools we choose shape the systems we build.