AI agents are multiplying as corporations adopt the technology in record numbers. Smarter underlying models, better tool use, and improved multi-agent collaboration have pushed agents to evolve beyond impressive demos into practical technology that companies marshal in production environments. But the job’s not finished. As companies deploy more agents, more often, and against longer tasks, the plumbing that provides their AI ephemera with the required information is buckling. Here’s the problem: AI agents are sending waves of queries against company data, creating concurrency issues and exposing just how difficult it can be to ensure a company’s AI-legible information is fresh, served only when relevant, and quickly available.Join the live conversation: On September 24 at 12 p.m. Eastern/9 a.m. Pacific, Whit Walters, Field CTO and Lead Analyst at GigaOm and author of Defeating the Integration Tax report, joins Bonnie Chase, Director of Product Marketing at Vespa.ai, to discuss what happens when retrieval architecture meets that workload. And crucially, they will explore in this live conversation what changes when a team rebuilds it as a unified layer instead of a fragmented one.Register for our free event on September 24 REGISTER NOW FOR THIS WEBINARWhat's your biggest challenge with AI agent retrieval?*Please selectLatency degrades when multiple agents retrieve at onceResults go stale or drift under concurrent agent loadFragmented stack: separate vector DB, ranking, servingHaven't hit these problems yet but want to prepareJust exploring retrieval architecture for agentsEmail *First Name *Last Name *Job Role *Please selectAI Researcher / Research ScientistArchitectBusiness Development/Marketing/SalesCommunity Manager / Developer AdvocateData Analyst / Business Intelligence AnalystData EngineerData ScientistDeveloper / Software EngineerDevOps EngineerEducator / InstructorEnthusiast/HobbyistFounder / Entrepreneur / Investor / VCIT management, including CIO/CISO/CTO/CDOMachine Learning EngineerMLOps Engineer / Infrastructure EngineerProduct ManagerSecurity / Privacy ProfessionalStatistician / Quantitative AnalystStudentSysAdmin/Operations/SRETechnical WriterUX / UI Designer / Data Visualization SpecialistJob Level *Please selectC-LevelFounder/OwnerVP/DirectorManager/SupervisorMid Level or Senior Individual ContributorEntry Level or Junior Individual ContributorFreelancer/ContractorEducator (Teacher, Instructor, Professor)Student/InternOtherN/AIndustry *Please selectAdvertising/MarketingAerospace/AviationAgricultureAutomotiveBiotech/PharmaceuticalBusiness Services (accounting, consulting, etc.)Computers/Information TechnologyConstructionEducationFacilities/Service IndustryFinance/Financial Services (banking, insurance, etc.)GovernmentHealthcareHuman ResourcesLegalLife sciences (biotech, pharmaceuticals, etc.)ManufacturingMediaNon-profitReal EstateRetail/Consumer GoodsTelecommunicationsTransportation/LogisticsTravel/Hospitality/EntertainmentUtility/EnergyOrganization *Country *Please selectUnited StatesCanadaUnited KingdomAfghanistanAlbaniaAlgeriaAndorraAngolaAntigua and BarbudaArgentinaArmeniaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBhutanBoliviaBosnia and HerzegovinaBotswanaBrazilBruneiBulgariaBurkina FasoBurundiCabo VerdeCambodiaCameroonCentral African RepublicChadChileChinaColombiaComorosCongoCongo (Democratic Republic)Costa RicaCote d’IvoireCroatiaCubaCyprusCzech RepublicDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEswatiniEthiopiaFijiFinlandFranceGabonGambiaGeorgiaGermanyGhanaGreeceGrenadaGuatemalaGuineaGuinea-BissauGuyanaHaitiHondurasHungaryIcelandIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKiribatiKorea (North)Korea (South)KuwaitKyrgyzstanLaosLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMauritaniaMauritiusMexicoMicronesiaMoldovaMonacoMongoliaMontenegroMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew ZealandNicaraguaNigerNigeriaNorth MacedoniaNorwayOmanPakistanPalauPalestinePanamaPapua New GuineaParaguayPeruPhilippinesPolandPortugalQatarRomaniaRussiaRwandaSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth SudanSpainSri LankaSudanSurinameSwedenSwitzerlandSyriaTaiwanTajikistanTanzaniaThailandTimor-LesteTogoTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTuvaluUgandaUkraineUnited Arab EmiratesUruguayUzbekistanVanuatuVatican CityVenezuelaVietnamYemenZambiaZimbabweState *Phone NumberRegisterBy registering, you consent to The New Stack’s Privacy Policy, Terms of Useand to receiving email communication from The New Stack and our event partner. 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Google famously handles tens of thousands of search queries every second; how difficult can it be to serve agents the information that they need when we’ve solved the human version of the same problem? It’s no small challenge, and it’s why retrieval engineering is a labor category you’ll hear more about in coming quarters.So, why is the problem worse with AI? Agents don’t ask a single question. They may retrieve data, reason against it, and then go back for more context. That doesn’t sound too complicated, until we recall that companies often stitch multiple systems together to provide their agents with required information. In practice, that means fusing vector databases, ranking tools, and serving layers into a single hybrid retrieval system that serves ever more agentic queries.Worse, when several agents ping the same cobbled-together architecture at once, relevance drift becomes a real issue. You might do all the work to get your company or team up and running with agents, only to see the effort fail because of stale data, generic answers, or even truncated results as retrieval plumbing stumbles.Your AI agents can’t scale successfully if they get dumber the more agents you deploy. So join the conversation on September 24, where we’ll break down how you can solve your retrieval engineering woes.What you’ll take away:Why agent workloads create a fundamentally different retrieval challenge than added concurrency aloneThe specific failure modes at agent scale — latency stacking, stale context, relevance driftWhy fragmented retrieval stacks amplify those failuresWhat a unified retrieval architecture looks like in practiceThe post Want to scale AI agents without breaking anything? Retrieval engineering is the answer. appeared first on The New Stack.