Morgan Stanley said in a note Wednesday that the rise of consumer AI agents such as Meta's Muse could benefit communications and software vendors long seen as vulnerable to automation, rather than threaten them. Analyst Elizabeth Porter argued that agents like Muse and Instinct, personal assistants that complete tasks on users' behalf, could expand the volume of interactions businesses field, cushioning contact-center vendors whose seat-based models have been under pressure from AI. She wrote that consumer agents lower the effort required to engage a business while enterprise AI lowers the cost of responding, potentially expanding communications volumes and usage-based monetization. Porter believes Twilio offers the broadest exposure, benefiting from the messages and calls an agent initiates to complete a task, such as authentication texts, reservation confirmations and follow-ups, on top of its existing usage and software-attachment model. For contact-center names such as NICE, Five9 and RingCentral, she said higher volumes could slow the risk of seat reductions while automation priced by interaction expands, and among web-presence providers she sees Wix as better placed than GoDaddy, with Shopify the most direct beneficiary in e-commerce software. Porter cautioned this is a stronger medium-term bull case rather than a near-term catalyst, and said she is wary of extrapolating upside into third-quarter results.
Cequence Finds Meta's Muse AI Agent Hit Over Half of Studied Customers in Two Weeks
Cequence Security said it detected traffic matching Meta's Muse personal AI agent at more than half of the customers it studied within two weeks of the agent's September 8 launch, and most of those businesses would not have known because Muse presents itself as an ordinary Chrome browser rather than identifying as an AI agent. Cequence analyzed traffic across customers in financial services, retail, travel, software and other sectors from September 1 to September 24, 2026, and found that at the median customer, Muse traffic grew nearly sixfold within about two weeks of first appearing. At financial institutions, Muse logged in to customer accounts and completed multi-factor authentication on users' behalf with confirmed successful sign-ins, while a small share of its sessions ended in a completed purchase. In late September, a travel and hospitality customer's security team began blocking nearly one in five Muse requests, targeting only the ones that looked abnormal, while Muse itself remained allowed. Cequence launched Agent Trust, a new capability in Cequence Application and API Protection available today, which verifies agents that present an identity and catches the ones that don't, pairing identity verification with behavioral detection. Agent Trust is immediately available to Cequence customers as part of Application and API Protection.
Artificial Intelligence › Agentic AI & Autonomous Workflows ▼Technology
Cybersecurity & Digital Trust › AI Security & Agent Guardrails ▼Technology
Cybersecurity & Digital Trust › Identity & Access Management ▼Technology
Cequence Security · Technology · Positive Cequence launched Agent Trust, a new capability in its Application and API Protection suite, directly tied to the Muse agent detection findings.
META · Technology · Neutral Meta's Muse AI agent launched Sept 8 and was detected across over half of studied customers, but the article frames it as a security/visibility concern rather than a clear positive or negative for Meta.
FICO Study Finds Only 5.2% of Tech Leaders Confident Explaining AI Decisions
FICO released its 2026 State of Responsible AI study, conducted with Corinium Global Intelligence, finding that only 5.2% of surveyed technology leaders are very confident explaining their AI-driven decisions. The survey of 1,004 senior technology, risk, and data leaders found that 71.5% of customer-impacting decisions are still made without AI, and that 37.3% of respondents are neutral or not confident at all in explaining AI decisions. Respondents most often cited AI model development standards, at 71.9%, as the responsible AI practice that would make the biggest difference, followed by interpretable model architectures at 67.9%. While 85.1% said AI has met or exceeded their ROI expectations, only 8.2% of organizations have a single shared AI deployment platform fully in place, and just 2.4% of tech leaders have broadly deployed agentic AI across customer-facing use cases, with 71.5% still in early exploration or pilot phases. FICO chief analytics officer Scott Zoldi said most organizations can tell you where and what their AI decided but far fewer can tell you why, adding that auditability and explainability are nonnegotiable as regulators and customers scrutinize AI use in decision-making.
Artificial Intelligence › AI Applications & Copilots Technology
Artificial Intelligence › Agentic AI & Autonomous Workflows ▼Technology
FICO · · Neutral FICO released its own 2026 State of Responsible AI study; the survey findings are about the industry, not a company-specific financial or product development.
Corinium Global Intelligence · · Neutral Corinium Global Intelligence is named only as FICO's research partner for the study, with no independent development affecting it.
Google Cloud and Mysten Labs to jointly develop VAA, a proof-of-behavior platform for AI agents
Mysten Labs, the developer of Sui, announced on October 6 a joint development plan with Google Cloud. The two will build Verifiable Agent Arbiter, or VAA, a platform that uses blockchain to prove that AI agents acted within the scope of the authority granted to them. VAA links the operations an agent is permitted to perform, its actual actions, and their outcomes into a record, so that counterparties and auditors can verify the record independently of the system that ran the agent. Instructions given to the AI, its outputs, its use of external tools, and decisions based on internal rules are stored privately in customer-managed Google Cloud storage, while cryptographic proofs corresponding to the records are stored on Walrus, a data storage platform, with Sui handling the management and linkage of those proofs. In addition to handling disputes in business-to-business transactions and investigating the causes of suspicious operations, the plan also includes integration with Sui's mechanism for AI agents to pay usage fees for external services under the x402 payment standard.
ThinkingAI Opens Agentic Growth Platform to Game Studios With Plans From $0
ThinkingAI, which builds autonomous growth software for consumer and gaming companies, has opened its analytics, engagement and agent stack to game studios that buy software with a card instead of a procurement cycle, offering four self-serve plans priced from $0 to $999 a month. The Free tier costs $0 and includes 2 million events a month, 1 project, 1 seat, 12 months of data retention and $10 in Agent Credits for the first month, with no credit card required. Starter costs $199 a month for 5 million events, 2 projects, 5 seats, 24 months of retention and $25 a month in Agent Credits, while Pro costs $399 a month for 10 million events, 2 projects, 10 seats, 48 months of retention and $50 a month in Agent Credits. Team costs $999 a month for 30 million events, 3 projects, 15 seats, 60 months of retention and $100 a month in Agent Credits, and an Enterprise tier is sold by agreement with sales for larger studios with compliance or data residency requirements. Annual billing saves 20%, paid plans are billed through Stripe and can be cancelled at any time, and a studio that outgrows its event allowance moves up a plan inside the product with no re-instrumentation and no migration. The plans follow ThinkingAI's Sept. 16 launch of the Agentic Engine and draw on 10 years of behavioral data across more than 1,500 enterprises and 8,000 applications, including Sega, Krafton, Habby, Century Games, Xiaomi, Yostar, TCL and FunPlus, and are being announced at the ThinkingAI Agentic Game Growth Summit in Istanbul, co-hosted with Mobidictum.
Artificial Intelligence › Agentic AI & Autonomous Workflows ▲Technology
Artificial Intelligence › AI Applications & Copilots ▲Technology
Artificial Intelligence › AI Tooling, Data & MLOps ▲Technology
Cloud & Digital Infrastructure › Data Platforms & Analytics Technology
ThinkingAI · Demand · Positive ThinkingAI opened its agentic growth platform to game studios with self-serve plans from $0 to $999/month, expanding adoption of its product.
Agentic Commerce Rails Near Completion as Merchant Trust Lags
The protocol stack for autonomous agent-to-merchant commerce is now effectively complete, but merchant adoption remains fractured and consumer trust lags far behind the infrastructure. Four layers now underpin machine-led spending: the Stripe Machine Payments Protocol, launched in March 2026, provides a programmable policy layer for stablecoin, fiat, and BNPL transactions; the Google Universal Commerce Protocol set an open standard for agentic commerce in January 2026; and by June 2026 Mastercard launched Agent Pay for Machines with more than 30 launch partners including Adyen, Coinbase, and Stripe. Capital flows have surged alongside these rails, with Visa stablecoin settlement reaching an annualized $20 billion by September 2026, a 15x increase year-over-year, while Stripe stablecoin card volume hit $1.2 billion in the same month. Merchants, however, have split into three incompatible postures: QVC has integrated agents into its supply chain and catalog distribution, sending catalogs to OpenAI and Google; Kate Spade lets agents browse inventory but blocks them at the point of purchase, citing fraud and inventory management risks; and Amazon and eBay have moved to block AI agents entirely, even as Shopify makes Muse the default agent interface for consumers. The core barrier is trust rather than technology, with an NMI survey finding that only 3% of US adults trust AI agents to complete purchases on their behalf, and the VML Tomorrow's Commerce 2026 report showing one-third of active AI users refuse to authorize agents to spend their money. The bottleneck has thus shifted from the protocol layer to the trust layer, leaving a high-capacity system operating at a fraction of its potential throughput.
Digital Finance & Tokenization › Payments Modernization & Rails ▲Demand
Artificial Intelligence › Agentic AI & Autonomous Workflows Demand
Digital Finance & Tokenization › Stablecoin Issuers & Distribution ▲Demand
MA · Technology · Positive Mastercard launched Agent Pay for Machines with over 30 launch partners, advancing its agentic commerce infrastructure.
V · Demand · Positive Visa stablecoin settlement reached an annualized $20 billion by September 2026, a 15x year-over-year increase, showing growing use of its rails.
ADYEN.AS · Demand · Positive Adyen is named as one of the 30+ launch partners for Mastercard's Agent Pay for Machines.
COIN · Demand · Positive Coinbase is named as one of 30+ launch partners for Mastercard's Agent Pay for Machines, giving it a role in agentic commerce payment rails.
Atlassian Launches Forward Deployed Engineering Program for Enterprise AI
Atlassian Corporation announced the launch of its Forward Deployed Engineering program, which embeds senior applied AI engineers directly into customer environments to deploy production-ready AI solutions. The program targets a gap between AI adoption and business impact: while 85% of knowledge workers use AI, only 6% of executives can point to clear, organization-wide ROI, according to Atlassian's report Leading with Context: Lessons from Atlassian's AI Journey. Avani Prabhakar, Chief People and AI Enablement Officer at Atlassian, said the company is seeing 50% higher AI adoption in engaged accounts as a result of embedding engineers with customers. In Atlassian testing, grounding AI agents in the Teamwork Graph produced answers that were 44% higher quality while using 48% fewer tokens. Expedia used the program to archive 600,000 pages across Confluence and save approximately 3,200 hours annually, with engineering teams now running 10,000+ agent invocations each month across 50 live projects, while Reddit is partnering with the engineers to accelerate its enterprise AI transformation. Engagements typically run 12 weeks, and Atlassian is also releasing The Atlassian Forward Deployed Engineering Playbook.
Artificial Intelligence › AI Applications & Copilots ▲Technology
Artificial Intelligence › Agentic AI & Autonomous Workflows ▲Technology
Artificial Intelligence › AI Tooling, Data & MLOps ▲Technology
TEAM · Technology · Positive Atlassian launched a Forward Deployed Engineering program embedding AI engineers to deploy production-ready enterprise AI solutions, with customer wins like Expedia and Reddit.