[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fG-6kQzKxoe-bB-10Odjv4Y26K0kqjA9rXgQv5MUg-mI":3},{"locale":4,"topic":5,"relatedTrends":50},"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":47},"Human limitations and input quality in operational AI agents","human-limitations-and-input-quality-in-operational-ai-agents",[],"ai-engineering","AI Engineering & LLM Ops","⚙️","GB",[12],"ai-engineering-GB",100,"spiking",false,"2026-05-27T02:57:36.146Z",[20,26,32,37,42],{"title":21,"url":22,"domain":23,"snippet":24,"content":25},"\"The agent can only work with what you give it\": one engineer's take on human-built AI","https:\u002F\u002Fitwire.com\u002Fguest-articles\u002Fguest-opinion\u002Fthe-agent-can-only-work-with-what-you-give-it-one-engineers-take-on-human-built-ai","itwire.com","Tom Howe argues that AI agents can only work with the inputs humans provide, emphasizing where operational AI still depends on human judgment and context.","Advertisement\n\nTom Howe | Published 27 May 2026\n\n!\n\nGuest Opinion\n\n**GUEST OPINION:** Tom Howe has seen his share of internet firefights — he still remembers keeping Disney+ afloat the night WandaVision launched. These days, as Director of Insights Engineering at Hydrolix, he spends a lot of time thinking about how AI fits into real-world operations, and just as importantly, where it doesn’t.\n\nI caught up with him to talk about what AI gets right, what teams keep getting wrong, and why human input still matters more than ever.\n\n**You’ve watched generations of tech come and go. What’s the pattern you see repeating?**\n\nEvery new wave promises a silver bullet. Years ago, it was sprawling 5,000-line Perl scripts designed to fix everything, until they became unreadable and unmaintainable. Now, it’s system prompts. Teams are stuffing every bit of tribal knowledge into a prompt, hoping the model will just figure it all out. But it doesn’t work that way. If you feed it junk, you get junk back. The only difference now is that the machine sounds more confident doing it.\n\n**What’s the real work when it comes to AI?**\n\nThe model is just a component. The real work is the scaffolding around it. At , we use AI across ops, product, and engineering, but none of it functions without humans deciding\n\n[Content truncated...]",{"title":27,"url":28,"domain":29,"snippet":30,"content":31},"Agentic AI Liability in Autonomous Supply Chain Decisions: Identifying and Preventing Legal Risks","https:\u002F\u002Fwww.foley.com\u002Finsights\u002Fpublications\u002F2026\u002F05\u002Fagentic-ai-liability-in-autonomous-supply-chain-decisions-identifying-and-preventing-legal-risks\u002F","foley.com","Agentic AI refers to an artificial intelligence system designed to achieve specific goals with minimal human supervision. Unlike traditional AI models that...",null,{"title":33,"url":34,"domain":35,"snippet":36,"content":31},"30+ Industrial AI Agents to Watch","https:\u002F\u002Faimultiple.com\u002Findustrial-ai-agents","aimultiple.com","Industrial AI agents address the limitations of siloed data by autonomously integrating and deriving actionable insights from IoT, controls systems (e.g....",{"title":38,"url":39,"domain":40,"snippet":41,"content":31},"I Looked Deeper Into OpenLedger. The Architecture Changed My View On AI Agents","https:\u002F\u002Fwww.binance.com\u002Fen-NG\u002Fsquare\u002Fpost\u002F325509729958689","binance.com","People keep framing AI agents like the whole story is speed. Faster reaction. Faster execution. Faster processing. The more OpenLedger kept pulling...",{"title":43,"url":44,"domain":45,"snippet":46,"content":31},"AI’s Impact on Industries","https:\u002F\u002Ftalentsprint.com\u002Fblog\u002Fai-transforming-industries","talentsprint.com","Discover how AI is revolutionizing key industries with innovation, automation, and smarter decision-making.",{"mentionsLast7Days":48,"mentionsLast30Days":48,"firstSeen":18,"lastSeen":18,"relatedEntities":49},5,[22,28,34,39,44],[51,54,58,62,67,71],{"topic":52,"slug":53,"score":15,"type":16,"country":12,"nicheIcon":11},"Mistral AI launches Vibe and builds industrial AI data centers","mistral-ai-launches-vibe-and-builds-industrial-ai-data-centers",{"topic":55,"slug":56,"score":57,"type":16,"country":12,"nicheIcon":11},"Forward Deployed Engineer role and AI engineering career path","forward-deployed-engineer-role-and-ai-engineering-career-path",99,{"topic":59,"slug":60,"score":61,"type":16,"country":12,"nicheIcon":11},"AI-driven execution and shared cybersecurity responsibility in enterprises","ai-driven-execution-and-shared-cybersecurity-responsibility-in-enterprises",95,{"topic":63,"slug":64,"score":65,"type":66,"country":12,"nicheIcon":11},"LinkedIn LLM-powered semantic search to understand intent over keywords","linkedin-llm-powered-semantic-search-to-understand-intent-over-keywords",88,"emerging",{"topic":68,"slug":69,"score":70,"type":66,"country":12,"nicheIcon":11},"Building RAG pipelines from minimal to corpus scale","building-rag-pipelines-from-minimal-to-corpus-scale",85,{"topic":72,"slug":73,"score":74,"type":66,"country":12,"nicheIcon":11},"LLM cheat sheet: models, prompting, RAG, and agents","llm-cheat-sheet-models-prompting-rag-and-agents",77]