Despite the seemingly insatiable appetite for AI among boards and C-suites, the current supply of agentic AI models, platforms and products far exceeds demand, argued Gartner analysts. The business and technology insights company anticipates that agentic AI markets will consolidate in the short term as hype and fear of missing out (FOMO) give way to fundamental economics.The losers of consolidation will be undifferentiated AI companies and their investors. The winners will be capital-rich incumbents with the resources to acquire promising technologies and talent, Gartner predicted. “While we see early signs of market correction and consolidation, product leaders should recognize this as a regular part of the product life cycle, not a sign of inevitable economic crisis,” said Will Sommer, senior director analyst at Gartner. “Over the longer term, consolidation will enable industry leaders to develop agentic products that meet the technical and business requirements of customers who are presently struggling to adopt AI agents. Product leaders can view this correction similar to others in energy and technology as a market transitional period in which business models are forced to calibrate to transformational technologies, explained Gartner analysts. “The impending agentic AI market correction is distinct from speculative bubbles fueled by systemic financial engineering, fraud or policy,” Sommer said. “At this point, the underlying product, agentic AI, is sound, and the current market correction, where markets rationalize and consolidate, is a regular part of the product life cycle. “However, a ‘speculative bubble’ could still form if investment becomes detached from agentic AI’s intrinsic potential to deliver tangible and commensurate economic value,” he continued. Large tech companies have already been acquiring smaller, specialized AI firms, signaling the start of the market correction phase, said Gartner. A provider of cloud-native application protection and extended detection and response (XDR), Uptycs announced that in the weeks following the debut of its Juno AI Analyst, it has already been deployed by major automotive manufacturers, banks and enterprises, replacing first-generation solutions from legacy security vendors focused on compliance over verifiable protection. Unlike standard “AI co-pilots” that summarize alerts from disparate tools, Juno is an agentic investigator backed by five pending patents, said Uptycs. Juno was designed to solve the primary barrier to AI adoption in cloud-native application protection platforms (CNAPP): Trust. “Most of the time, uncertainty rules in the world of cybersecurity,” said Ganesh Pai, CEO of Uptycs. “Juno’s evidence-based approach uses AI to replace opaque ‘black box’ answers with transparent, verifiable reasoning grounded in real telemetry, so security teams can trust what they’re seeing and act with confidence.” Juno utilizes a “Glass Box” approach, said Uptycs. It does not just generate answers; it executes deterministic SQL queries against a purposebuilt Unified Multi-Cloud Ontology, a massive, normalized schema of more than 3,000 tables and 150,000 columns of security telemetry. This architecture allows Juno to take “surgical sips” of data, retrieving precise, raw evidence to prove its findings, rather than ingesting a firehose of noise that leads to hallucinations, explained the company. “Enterprise adoption, meanwhile, validates that the market is shifting from “chatbot” novelties to structural engineering solutions,” said Uptycs executive. “Juno addresses the cybersecurity professional’s aspiration to leverage a conversational interface, interact with their own data and harvest insights, explanations and recommendations,” added Srinivas Tummalapenta, CTO, IBM CyberSecurity Services. Agentic AI Correction Looms Uptycs Seeks to Break ‘AI Hallucination’ Cycle AI & AUTOMATION The AI Adoption Gap Source: Gartner (October 2025) If an AI bubble were to bust, how would it impact your organization’s Investment strategy? Among C-suite repondents Source: Accenture Which, if any, of the following have you done to improve the outputs of Generative AI tools in your company? 7% 31% 16% 35% 10% Significantly decrease investments (20% or more) Significantly decrease investments (up to 20%) No changes Somewhat increase investments (up to 20%) Significantly increase investment (20% or more) Training staff to better use AI Encouraged teams to share tips and examples for effective use of AI Used AI agents to improve outputs Use process intelligence to improve analysis Used Document AI/intelligent document processing to improve the outputs Set up regular check-ins to review AI use Considering alternative AI tools Used Retrieval augmented generation (RAG) to improve the outputs Asked staff to manually check and correct the outputs Scaling back or removing AI tools that weren’t working We got rid of all AI tools and stopped using AI completely 50% 43% 36% 35% 35% 33% 28% 25% 25% 20% 18% AGI Neurosymbolic AI Ambient AI Agentic AI AI Adoption Gap Time AI Innovation Race (Providers) AI Outcome Race (Customers) AI Agents GenAI Traditional AI (ML, NLP) 8 CHANNELVISION | WINTER 2026
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