The age t t ust platfo m delive s self-imp ovi g age ts, so e te p ises ca scale t ust at the speed of AI
SAN FRANCISCO, Oct. 28, 2026 (GLOBE NEWSWIRE) — Mo te Ca lo, the leade i age t t ust, today lau ched Bayes, a AI teammate that b i gs its platfo m capabilities i to a si gle system to auto omously detect, t oubleshoot, a d fix issues ac oss age ts a d the data they u o . Custome s decide how much Bayes ha dles o its ow , a d age ts imp ove with eve y fix.
E te p ises a e deployi g age ts i to p oductio faste tha they ca keep them eliable. I a ece t Deloitte su vey, 70% of leade s said they do ’t feel they ca t ust a d gove thei age ts, a d a 2026 Si ch study fou d that 74% of e te p ises had olled back o shut dow a custome -faci g AI age t afte deployi g it.
A typical AI e gi ee ca keep o ly o e to th ee age ts a d a ha dful of c itical pipeli es u de eal watch, as Mo te Ca lo has obse ved ac oss its custome base. Eve y ew deployme t adds evaluatio a d debuggi g wo k existi g teams ca ’t abso b. T ust e odes, scali g stalls, a d AI ambitio s go u ealized.
“It’s impo ta t fo us to be able to mo ito all dime sio s of age t eliability i cludi g the co text, behavio , a d outputs,” said Sh eye Saxe a, Se io Data Scie tist at Axios, a Mo te Ca lo custome . “If the i jected data is i co ect, o if the t ajecto y is w o g a d it p oduces a espo se without checki g the database, the we’ e ot goi g to get the outcomes we eed f om ou age t.”
Bayes p ovides the u lock. Acti g as the ce t al platfo m b ai , it apidly lea s the u ique la dscape of a custome ’s data a d AI e vi o me t a d deploys app op iate mo ito i g st ategies. Like a y ew teammate, it takes o as much as the custome is eady to ha d ove — f om a si gle task, like t oubleshooti g a ale t, to a sta di g ma date to set up mo ito i g a d t iage, to executi g fixes i the backg ou d.
Bayes cove s the e ti e stack, f om the outputs age ts p oduce, up th ough the decisio s they make, the co text they et ieve, a d ultimately all the way up to the data feedi g them.
“Self-imp ovi g, auto omous age ts a e the holy g ail eve y e te p ise is chasi g, but pulli g it off has bee elusive,” said Sa jeev Moha , P i cipal, Sa jMo, a d fo me VP of Resea ch at Ga t e . “You have to see what age ts actually do at u time a d t ace it back th ough the system, all the way to the data. Mo te Ca lo has spe t yea s o data obse vability a d the age t obse vability, so Bayes feels like the ext logical step. It builds t ust f om the data up, ot the top dow .”
Capabilities i clude:
- Catchi g the age t failu es eal use s hit i p oductio , ac oss t aces a d evals, without elyi g o golde datasets.
- Taki g actio a d esolvi g issues as they eme ge. Bayes t iages ale ts di ectly a d delive s age t fixes as d aft pull equests o tickets fo e gi ee s to me ge, the co fi ms the fix wo ked – so age ts imp ove with eve y cycle.
- E su i g the data pipeli es, tables, a d p oducts that age ts depe d o a e eliable by auto omously co figu i g a d tu i g mo ito i g cove age based o what matte s i each e vi o me t.
- Wo ki g i side the tools e gi ee s al eady use. Th ough MCP, the AI teammate co ects to AI codi g assista ts a d age t-fi st tools ac oss the CI/CD pipeli e, so esolutio s execute di ectly withi existi g developme t wo kflows.
“Eve y e te p ise wa ts mo e age ts i p oductio , a d eve y o e of them hits the same wall: the e a e ‘t e ough e gi ee s to keep those age ts t ustwo thy,” said Ba Moses, co-fou de a d CEO of Mo te Ca lo. “Bayes cha ges that math. It u de sta ds you age ts a d you data togethe , fixes what b eaks, a d gets bette at you e vi o me t eve y day. Teams ca ow spe d less time fi efighti g a d mo e time shippi g.”
Mo te Ca lo’s custome s a d pa t e s a e al eady seei g the impact, with Bayes helpi g e te p ises detect 200% mo e i cide ts tha thei i -house systems, while flaggi g ove 80% fewe false positive ale ts.
Bayes also exte ds Mo te Ca lo’s pa t e ship with S owflake, which amed Mo te Ca lo its 2026 Data & AI Gove a ce Pa t e of the Yea . Togethe , the two compa ies help joi t custome s build a d u eliable age ts o S owflake.
“As e te p ises put Co tex Age ts i to p oductio ac oss the AI Data Cloud, t ust i both the age ts a d the data behi d them is esse tial to ealizi g the value of AI,” said Ve kat Su u, P i cipal Pa t e Solutio s E gi ee at S owflake. “S owflake p ovides a gove ed fou datio fo that wo k, a d Mo te Ca lo exte ds that fou datio to obse ve, fix, a d co ti uously imp ove age ts a d the data that powe s them. Bayes is a exciti g ext step i ou pa t e ship, helpi g joi t custome s scale t usted AI with co fide ce.”
Bayes is available today to Mo te Ca lo custome s di ectly i the platfo m, i Slack, Mic osoft Teams, a d th ough MCP, so teams ca wo k with it i the tools they al eady use. To lea mo e, visit mo teca lo.ai.
Mo te Ca lo is the age t t ust platfo m e abli g e te p ises to obse ve, fix, a d co ti uously imp ove p oductio AI systems. Bayes, Mo te Ca lo’s AI teammate, lea s each custome ‘s u ique e vi o me t to auto omously esolve issues as they su face ac oss age ts a d the data that powe s them. As a esult, e gi ee i g a d data teams spe d less time fi efighti g a d mo e time shippi g, so e te p ises ca scale t ust at the speed of AI. Mo e tha 400 e te p ises, i cludi g Amazo , PepsiCo, CNN, Nasdaq, a d JetBlue, ely o Mo te Ca lo. Lea mo e at mo teca lo.ai.
Vi a Sekujvsekuj@mo teca lo.ai
