[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fC_E2yZ33MLWb5WUy2sjM7QykUjY-gZF_YyhKXWOcB8Y":3},{"locale":4,"topic":5,"relatedTrends":55},"en",{"topic":6,"slug":7,"canonicalSlug":7,"topicAliases":8,"nicheKey":9,"nicheName":10,"nicheNameEn":10,"nicheIcon":11,"country":12,"countries":13,"agentKey":14,"score":15,"type":16,"isFresh":17,"isPublic":17,"detectedAt":18,"sources":19,"evidence":52},"Perplexity Search as Code: sandboxed Python agent pipelines","perplexity-search-as-code-sandboxed-python-agent-pipelines",[],"ai-engineering","AI Engineering & LLM Ops","⚙️","DE",[12],"ai-engineering-DE",100,"spiking",false,"2026-06-09T02:59:11.677Z",[20,26,32,37,42,47],{"title":21,"url":22,"domain":23,"snippet":24,"content":25},"Perplexity Search as Code: Agents Write Search — 85% Fewer Tokens","https:\u002F\u002Fwww.abhs.in\u002Fblog\u002Fperplexity-search-as-code-85-percent-tokens-agent-api-june-2026","abhs.in","Perplexity launched Search as Code, letting AI agents write sandboxed Python search pipelines that cut tokens from 288K to 43K on a CVE-hunt benchmark.","Abhishek Gautam](https:\u002F\u002Fwww.abhs.in\u002Fwho-is-abhishek-gautam)·June 8, 2026·11 min read\n\n!\n\nQuick summary\n\nPerplexity replaced fixed search API calls with sandboxed Python pipelines agents write themselves — 288K tokens down to 43K on a CVE hunt benchmark.\n\nRead next\n\n*   \n*   \n\n**Perplexity** launched **Search as Code (SaC)** in early **June 2026** — an architecture where **AI agents write Python search pipelines** in a **sandbox** instead of chaining **fixed API tool calls**. On a **200 high-severity CVE** case study, Perplexity reports **100% accuracy** at **42,900 tokens** vs **288,700 tokens** for its old pipeline — an **~85%** reducti\n\n[Content truncated...]",{"title":27,"url":28,"domain":29,"snippet":30,"content":31},"Perplexity lässt KI-Agenten ihre eigene Websuche programmieren, um Tokens zu sparen und Präzision zu erhöhen","https:\u002F\u002Fthe-decoder.de\u002Fperplexity-laesst-ki-agenten-ihre-websuche-programmieren-um-tokens-zu-sparen-und-praezision-zu-erhoehen\u002F","the-decoder.de","Perplexity stellt mit \"Search as Code\" eine neue Architektur vor, bei der KI-Modelle ihre Suchabläufe als Python-Code schreiben, statt eine fertige Such-API...",null,{"title":33,"url":34,"domain":35,"snippet":36,"content":31},"Perplexity's \"Search as Code\" lets AI models write their own search pipelines instead of calling fixed APIs","https:\u002F\u002Fthe-decoder.com\u002Fperplexitys-search-as-code-lets-ai-models-write-their-own-search-pipelines-instead-of-calling-fixed-apis\u002F","the-decoder.com","Perplexity's new \"Search as Code\" architecture dumps rigid search APIs and lets AI models write their own search routines in Python. By letting the agent...",{"title":38,"url":39,"domain":40,"snippet":41,"content":31},"Perplexity Lets AI Agents Write Their Own Search Code","https:\u002F\u002Fwinbuzzer.com\u002F2026\u002F06\u002F07\u002Fperplexity-lets-ai-agents-write-their-own-search-code-xcxwbn\u002F","winbuzzer.com","Perplexity's Search as Code lets AI agents generate Python search workflows, but claimed token savings and benchmark gains still need outside validation.",{"title":43,"url":44,"domain":45,"snippet":46,"content":31},"Perplexity Search as Code Lets AI Models Write Their Own Search Pipelines","https:\u002F\u002Fopentools.ai\u002Fnews\u002Fperplexity-search-as-code-ai-models-write-search-pipelines","opentools.ai","Perplexity Search as Code lets AI models write custom search pipelines in Python, cutting token usage by 85 percent and outperforming competitors on...",{"title":48,"url":49,"domain":50,"snippet":51,"content":31},"Perplexity release \"Search as Code\" architecture, lets AI models write their own search pipelines instead of calling fixed API","https:\u002F\u002Fvoice.lapaas.com\u002Fperplexity-release-search-as-code-architecture-lets-ai-models-write-their-own-search-pipelines-instead-of-calling-fixed-api\u002F","voice.lapaas.com","Perplexity has officially launched Search as Code (SaC), a paradigm shift in how artificial intelligence models interact with the web.",{"mentionsLast7Days":53,"mentionsLast30Days":53,"firstSeen":18,"lastSeen":18,"relatedEntities":54},6,[22,28,34,39,44,49],[56,59,62,66,71,75],{"topic":57,"slug":58,"score":15,"type":16,"country":12,"nicheIcon":11},"Anthropic Managed Agents on Claude as managed execution layer","anthropic-managed-agents-on-claude-as-managed-execution-layer",{"topic":60,"slug":61,"score":15,"type":16,"country":12,"nicheIcon":11},"Leading AI chip makers for cloud GPU inference workloads","leading-ai-chip-makers-for-cloud-gpu-inference-workloads",{"topic":63,"slug":64,"score":65,"type":16,"country":12,"nicheIcon":11},"Best low-code and no-code AI tools of 2026","best-low-code-and-no-code-ai-tools-of-2026",95,{"topic":67,"slug":68,"score":69,"type":70,"country":12,"nicheIcon":11},"Gumloop no-code platform for building production AI agents","gumloop-no-code-platform-for-building-production-ai-agents",88,"emerging",{"topic":72,"slug":73,"score":74,"type":70,"country":12,"nicheIcon":11},"Business guide to the 10-layer AI master stack for 2026","business-guide-to-the-10-layer-ai-master-stack-for-2026",87,{"topic":76,"slug":77,"score":78,"type":70,"country":12,"nicheIcon":11},"AMD beats Q1 2026 expectations with strong results","amd-beats-q1-2026-expectations-with-strong-results",84]