Maven AGI

Six new G2 badges just landed for Spring 2026. Five High Performer placements across Customer Service Automation, AI Customer Support Agents, AI Agents, Agentic AI, and Mid-Market AI Customer Support Agents. And Best Meets Requirements for Mid-Market AI Customer Support Agents. G2 only gives that badge to one product per category, the one with the highest score on whether the product actually delivers what buyers need. That score comes from verified customers answering a single question: does this product meet your requirements? In a category that added 12 new products this quarter alone, earning the top score on that question says something specific. Best fit for what mid-market CX teams actually need to get done. The reviews behind these badges describe what that looks like in practice. Integration in days instead of months, AI agents that resolve issues instead of deflecting them, and consistent behavior across every channel without rebuilding workflows. Every badge earned through real customer reviews on G2. Full breakdown on the blog, link in comments.

A year ago, Gartner predicted 80% autonomous customer service resolution by 2029. Then three months later, they predicted 40% of those projects would be canceled by 2027. Both numbers are probably right. The destination hasn't changed, but most of the roads people are taking don't get there. The biggest culprit isn't the technology — it's what Gartner started calling "agent washing." Only about 130 out of thousands of vendors claiming agentic AI are actually delivering it. The rest are relabeled chatbots. We mapped everything that's changed since that original prediction — the corrections, the market data, and what separated companies on track from the ones heading for the cancellation pile. Link in comments.

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50% of customers say they've successfully resolved a support issue without talking to a human. And 57% say AI-powered self-service frustrates them. Same study, both true. The overlap is the interesting part. Even among people who've had a successful AI interaction, enough of them have also had a terrible one that the overall sentiment is still negative. One good experience doesn't erase the memory of being trapped in a self-service loop that couldn't understand what you were asking. When we look at what separates the good from the bad, it almost always comes down to whether the AI was designed to actually resolve something or just intercept the request. A knowledge base search dressed up as a conversation will frustrate people every time. An agent that pulls up your account, understands the context, and takes action to fix the issue in under two minutes — that earns trust. The 7-point spread between "it works" and "it frustrates" is where CX teams are winning and losing customers right now, and most of them don't even know which side they're on.

Three major acquisitions in the AI CX space in the last six months, all chasing the same thesis — ticket routing isn't enough anymore, and the real value is in autonomous resolution. What's telling is that the acquirers are all legacy helpdesk companies. They built enormous businesses on the assumption that organizing and routing tickets is the core job. Now they're buying their way into a fundamentally different model where the AI resolves the issue before a ticket ever gets created. That transition is going to take longer than the press releases suggest. Merging two codebases and two data models is at least a year of work before it shows up as something meaningfully better in your instance. And during that year, the platforms that were built for resolution from the start will keep compounding their lead. For CX leaders currently on a legacy helpdesk, there's a window right now — probably 12 to 18 months — where you get to choose your own AI layer before your vendor makes that choice for you. Worth thinking about whether you want to be on the deciding end or the receiving end of that transition.

Financial services customers don't want to wait. When a transaction is blocked, an account is frozen, or a payment goes missing — they need answers immediately. The traditional approach: deflect to a human or give a vague, compliance-safe response. Both frustrate customers and overwhelm teams. The better approach: AI agents that actually resolve the inquiry. We broke down exactly how this works in financial services — covering fraud scenarios, regulatory complexity, fragmented knowledge systems, and high-stress customer moments. And crucially: how to do all of it without breaking compliance. Read the full breakdown → link in the comments.

Enterprise AI that guesses is enterprise AI that fails. Every response from Maven's agents is grounded in verified, company-approved data. Apify's Website Content Crawler automates knowledge ingestion across 20+ internal systems, keeping our RAG pipeline current without manual effort. Read the full case study from the Apify team!

There's a persistent belief in enterprise AI that if you just make the model smarter, the governance problems solve themselves. Better reasoning leads to better judgment, which leads to fewer mistakes. Multi-sided platforms expose why that logic breaks down. When a courier asks about a restaurant's fulfillment metrics, a smarter model doesn't refuse to answer because it suddenly understands platform trust dynamics. It gives a more articulate answer with data it should never have retrieved. The fix is architectural, not inferential. Eligibility has to be enforced before the model reasons, not after. That's the core argument in our new guide on designing AI agents for multi-sided platforms.

Voice AI crossed a threshold this year, but the conversation went somewhere unexpected: the basic principles of user experience. The early web taught us make it simple, easy to understand, and to reduce the steps it takes to do something. With voice, we're not talking about better IVR menus. We're talking about conversations so natural that customers treat AI agents like humans. Voice is having a moment, but the future isn't a single modality. The best UX is AI that's just present — on your laptop, in your car, at the park. Same context, same memory, same outcomes. No more starting over every time you switch channels. That's the shift our CEO Jonathan Corbin and CTO Sami Shalabi unpacked with John Furrier on TheCUBE x NYSE Wired. Watch the full conversation 👇 https://lnkd.in/eksUGxFq

Maven AGI is an official partner of AI Agent Conference 2026, May 4–5 in New York City. Earlier this year, AI Agent Conference named Maven to The Agentic List 2026 — one of nine companies selected in the Customer Experience Agents category, based on enterprise adoption and production performance rather than fundraising volume. That recognition reflects what we hear from customers every week: the gap between demo-ready AI and production-grade AI is where most vendors disappear. Our CEO Jonathan Corbin will be on stage for the panel "Deploying Agents in the Real World" — covering what it actually takes to move autonomous agents from proof-of-concept to enterprise-scale operations with governance, auditability, and measurable resolution rates. If you're building or evaluating AI agents for customer-facing workflows, this is the right room. Over 100 speakers and 2,000 attendees, all focused on what's working in production. cc: Simon Chan