OpenAI's new mid-tier model targets invoices, forms and desktop software at half the API price of its predecessor. Cheaper computer-use agents widen the business case, but cost per outcome and guardrails still decide whether they pay off.
Walmart is giving AI agents their own profiles, spending limits and an MCP gateway, while Amazon blocks them. The split shows why agent identity, not access, is becoming the central question in agentic commerce.
SAP posted its first €10 billion quarter, raised its cloud outlook and credited its Autonomous Enterprise AI strategy. The results show ERP cloud migration accelerating, but SAP still reports no numbers for its AI business.
The open source llm-d project reached 1.0 this week with features built for AI agents. We argue that shared, open inference infrastructure gives enterprises leverage over the cost that will decide whether agents pay off.
At the Open Compute Project Global Summit, Nvidia and AMD introduced open rack designs built around CPUs for AI agents. The shift changes what AI-ready infrastructure needs, and how much choice enterprise buyers will really get.
Cohere plans to acquire AISLE, whose AI found most of the flaws in curl's latest release, and form Cohere Cyber. The same week, Bank of America showed the cost of fixing what AI security tools find.
Perplexity open-sourced the sandbox that isolates its agents, cloud providers lined up to offer it, and the FTC questioned AI labs about safety incidents. Enterprises moving agents into production now need containment by design.
At an October 5 hearing, the four labs backed a federal standard while council members pushed third-party validation, a human shutdown requirement and 24-hour incident reporting. City officials raised their own doubts about enforcement.
In one week, Google, Microsoft, Meta and Sierra rolled out identity and containment tools for AI agents, while Wikimedia and Anthropic described AI models acting on their own. The enterprise question is shifting to who an agent is and how to stop it.
OpenAI's DevDay brought persistent agents that work in the background, specialist agents with their own corporate credentials, a cheaper GPT-6.1 Sol model and a computer-use Agents API. Pricing for business-grade agents is still unknown.
Anthropic's new mid-tier model nears Opus 5.5 on coding and office benchmarks at the same $2 and $10 list price as Sonnet 5. An independent test shows costs climb sharply at maximum effort.
AI agents can only act as fast as their data allows, says IBM's Ann Leach. She makes the case for the streamhouse, a real-time layer that complements the lakehouse, and points to Ferrari, which used live telemetry and AI to double daily app users.
Model choice isn't what sinks enterprise AI projects, says Kindo's Shan Zhou; systems, governance and cost are. He lays out five reasons pilots fail, why AI policies only work when enforced at the moment of action, and where self-hosted models fit in sovereign AI.
Most AI agent prototypes never become trusted production systems. LangChain Deployed Engineer Christine Manegan says the teams that succeed own their intelligence, from models and harness to evals and governance, and describes how an agent harness manages context, sub-agents and human-in-the-loop controls.
Meta is packaging its Muse agents, models and coding tools for businesses and has hired MongoDB's CEO to lead the effort. Its reach is real, but pricing, data commitments and agent trust remain open questions for enterprise buyers.
Data and AI leaders from Honeywell, Capital One, Synchrony, TelevisaUnivision and EXP Realty describe what it takes to move AI agents from prototype to production: real use cases, context engineering, agent memory, least-privilege access and auditable agent behavior.
AI has no control plane yet, leaving agents loose in brownfield data estates, argues NexusOne CEO Anu Jain. He lays out three questions every leader should answer, the case for one identity across all systems, and why companies should own their ontologies.
When an AI agent gets a refund policy wrong, the model often never saw the policy. Redis Senior Solution Architect Jessica Emmons breaks down context engineering, the four ways context fails, why basic RAG isn't enough and how context engines, agent memory and semantic caching help.
Deepfake fraud attempts have surged 21,000% in three years, according to TrendAI Global Deputy CISO Sundari Parekh. She breaks down the Arup and Ferrari cases, why defenses must run at machine speed and what security teams should do in the next 90 days.
General-purpose AI tools often win analytics pilots and then break down at scale, says WisdomAI's Geetesh Iyer. The missing piece, he argues, is managed enterprise context, and the people best suited to own it, AI context engineers, are already on data teams.
Building an AI agent is easy; running thousands of them is not. IBM's Ann Leach says agent sprawl creates security and cost risk, lists five questions an agentic control plane must answer and describes how Starlink coordinated procurement agents.
Matillion's Maia agent builds, tests and documents data pipelines from plain-language requests. Matillion's Arawan Gajajiva argues that data engineers' work is shifting from writing code to governing, planning and evaluating what AI builds.
Data quality is an input, not the goal, says Informatica's Peter Manta. What matters is whether AI agents make good decisions. He outlines six pillars of an agentic data contract and an experiment showing that even 10% duplicate data changed agent decisions.
An OpenAI research agent reached the internet through a gap in its sandbox, and AI leaders signed a voluntary safety pact at the White House. Vendor commitments help, but containing agents inside a business is the buyer's job.
Anthropic is spending $100 million to train 10,000 engineers inside customer companies, and Barclays is pushing Claude Code to half its developers. The people who turn models into production systems are now the scarce resource.
An OpenAI agent got past protections on a Services Australia statistics portal in June, and the government heard about it in September. The case shows why agent controls and vendor disclosure terms belong in every enterprise AI plan.
Anthropic cut Opus pricing by 20% and OpenAI halved the cost of its mid-tier models in launches hours apart, shifting the enterprise question from which model is smartest to which one does each job for less.
Harvey's gross margin reportedly swung from 50% to negative 50% as agent usage soared, until it moved work to a model built on open weights. Its peers are following, and enterprise buyers should ask what powers their tools.
Precisely's agent checks data quality scores before it acts and repairs failing data through governed pipelines. Coder runs coding agents in self-hosted, sandboxed workspaces with budgets, session logs and human-owned pull requests.
Cohesity launched a tool to restore AI agents and the systems they touch to a known-good state. Whatever buyers make of the product, no agent should get write access until its owners can show how to undo its work.
Claude now decides when a request needs an agent, and new Docs and Slides tools move it closer to office work. Enterprise IT gets 30 days' notice, but connector, autonomy and cost decisions should start now.
An AI system can look perfectly healthy and still be wrong. Leaders from SAP, Dynatrace and RaceTrac lay out what to measure beyond accuracy, how to catch drift and how observability creates the audit trail AI governance depends on.
At Dreamforce, Salesforce opened its data, workflows and permissions to outside AI assistants, starting with Claude. The interface is becoming a choice; the controls and context underneath are where enterprise leaders need to focus.
In one week, a European regulator described a personal data breach carried out through an AI agent, Google acknowledged a test that reached real companies and OpenAI detailed self-injected instructions. Agent risk is now an incident response issue.
Google's threat intelligence group documented a criminal campaign run largely by autonomous AI agents, plus attacks on AI API keys, models and coding assistants. Enterprises need faster response and tighter control of their own AI assets.
OpenAI's new Agents API lets companies rent the managed harness behind Codex instead of building their own. It could speed agents to production, but U.S.-only residency, no Zero Data Retention and lock-in risk call for care.
Carnegie Mellon's MOLE benchmark found most tested agents completed simulated insider attacks, and their refusals didn't predict behavior. Enterprises should give agents the identity, least-privilege access and monitoring they give privileged employees.
Snowflake's Tyler Richards used Cortex Code to build a dashboard and Slack alerts from written specs, while Precisely's Carmen Adame had an AI assistant assemble a property risk profile through an MCP server.
Launched at VMware Explore 2026, AgentMinder treats each AI agent as an enterprise identity and checks every action against its declared purpose. It marks agent governance moving from policy documents into the infrastructure layer.
Enterprise AI has moved from an era of speed to an era of control, says Fulcrum Digital founder Rajesh Sinha. He lays out a seven-layer sovereign AI architecture, why many projects fail at the inference layer and agent use cases in insurance, finance and manufacturing.
Fragmented systems, stale data and reactive governance hold back most AI projects, says IBM's Tim Richer. He identifies the use cases scaling fastest, how IBM runs agents on its own HR, IT and procurement systems, and client results from Ferrari and EY.
Leaders from Tokio Marine, Morgan Stanley and The Fedcap Group describe what happens after the first agent ships: a validation tax on human reviewers, workflows that fail more often than models, and proprietary context as the place where advantage builds.
Data lakes and lakehouses were built for human users, not agents that run around the clock and scale to thousands, says Teradata's Ben Sanders. He explains why pilots break in production, how missing context drives up cost and latency, and the six components of enterprise context.
AI agents will overreach, and traditional security controls weren't built for probabilistic software, says Rubrik's Cal Al-Dhubaib. He offers real examples of agent overreach, a three-part model of context-based control and three questions every team should ask before AI fails.
Anyone can build an AI agent now, says IBM's Sabtain Khan, but few enterprises get agents deployed and used. He argues agent sprawl is an operating model problem and that regular testing, feedback loops and a control plane help agents reach production.
Banks are using AI to rerun fixed chatbot workflows, not to act autonomously, according to Midships CEO Ajit Gupta. Checking each agent's purpose, objective, plan and API call, with signed human approvals, can keep autonomous agents on track, he says.
Salesforce's AI and data business grew more than 210% while total revenue rose 11%. Half of Agentforce bookings now come from usage credit top-ups, a shift that changes how CIOs should budget and govern agent spending.
OpenAI and more than 100 companies signed a letter warning of a surge in AI-enabled attacks, a day after OpenAI detailed how its test models broke into Hugging Face. The lesson for enterprises: govern your own agents like a new class of actor.
Salesforce and Anthropic unveiled Claudeforce, a two-way integration that brings 37 sales skills into Claude and makes Claude a default model across Slack and Agentforce. Enterprises gain governed access but face open questions on pricing and lock-in.
Salesforce's quarter undercuts the claim that frontier models will replace business software. But as interfaces move into AI assistants and pricing shifts to credits, enterprises should make sure they, not any one vendor, own their business context.
Google Cloud's new finance and legal editions of Gemini Enterprise put it alongside Anthropic and OpenAI in the vertical agent business. The real competition, and the real work for buyers, now sits in data connectors, permissions and evaluation.
Interim results from The Modern Data Company's survey of more than 540 data leaders show agent adoption racing ahead of data trust, context layers and accountability, the foundations agents need to reach production.
UiPath's new developer canvas lets popular coding agents design, run and govern whole business processes on its orchestration engine. It is a bet that coding agents will build on automation platforms, not replace them.
After its test models breached Hugging Face, OpenAI now pays roughly a fifth more compute to watch its most capable systems. A survey this week suggests many enterprises have not budgeted for agent containment at all.
Gartner says the cost of completing work with AI agents will climb more than fivefold through 2028 even as token prices fall. Snowflake, TrueFoundry and others spent the week pitching routing and budget controls in response.
Dynatrace will pay about $915 million for Arize, folding a leading AI evaluation and agent observability platform into mainstream IT monitoring as enterprises look for proof that production agents are working.
How do you give AI agents autonomy without losing control? Leaders from AT&T, SAP, Posit, Revefi and Rubrik point to policy layers outside the model, risk-tiered approvals, agent identities and end-to-end tracing, and warn that humans make poor review machines.
New research shows AI-written code passes security checks only about 56% of the time and AI patches often miss the root cause. Enterprises should measure secure output, not volume, and keep accountable humans on high-risk changes.
Four vendors are taking different routes to enterprise AI: Mistral AI with document OCR and voice models, SAS with detection of AI-generated forgeries, Fivetran with one dataset served to seven query engines and Precisely with location data for Claude via MCP.
At AMD's Advancing AI 2026, OpenAI and Anthropic said they are using their own models to build software for AMD GPUs, while Meta and Cerebras showed co-designed systems. The trend could make enterprise AI workloads more portable.
ServiceNow's second-quarter results show agentic AI moving into paid production workflows, with AI contract value above $1 billion, ninefold growth in agent deployments and a pricing model that is shifting toward usage.
Security firm Zenity says a since-fixed flaw in ChatGPT's Agent Builder let a single phishing click create and publish an agent with an employee's access to email, storage and chat apps, a new kind of insider threat.
A week of launches from Harness, GitLab, AvePoint, Teradata, Axonius and Squirro shows vendors treating AI agents like code and like assets: versioned, tested, deployed with approvals, inventoried and audited.
Booz Allen found federal agencies deploying agentic AI faster than they can secure it, with a quarter unsure who answers when an agent fails. The cheapest fix in AI governance is also the most neglected: a named owner for every agent.
Hugging Face disclosed on July 16 that an autonomous AI agent system broke into part of its production infrastructure through a malicious dataset. Public models were not tampered with, but the attack is a warning for enterprise AI pipelines.
Meta opened a paid developer API for Muse Spark and released version 1.1, built for coding and agentic tasks. Low pricing adds a new option for enterprise teams, but the preview raises questions about terms, support and lock-in.
Enterprises get ROI from AI they own, trained on their own data and deployed where they choose, argues Mistral AI's Aaron Delp. He outlines Mistral's approach to a first use case and pilot, plus the admin controls, workflows and skills in Mistral AI Studio.
Cognizant expanded its Google Cloud partnership, pledging to certify at least 10,000 professionals on Gemini Enterprise and deploy it to 100,000 employees this year. The move shows services firms retraining their own workforces before selling AI to clients.
OpenAI's GPT-5.6 arrived after a government-requested delay, in three tiers priced from $1 to $5 per million input tokens. The release turns model choice into a workload-by-workload cost decision and raises new questions about evaluation and release risk.
In one week, researchers described hijacking coding agents, a blog documented an autonomous agent attack, vendors launched agent gateways and federal experts warned zero trust is straining. The lesson: agents need identity, runtime controls and provenance.
OpenAI's new Daybreak program packages a restricted cyber model, an upgraded Codex Security plugin and a partner network that includes major security vendors and consultancies, changing how enterprises may encounter AI vulnerability research.
Google says its newest Gemini features for long-running agents will increasingly ship only on the Interactions API, which reached general availability with server-side state, background execution and managed agent sandboxes.
Agents that run in the background, wake on schedules and work until a goal is met are moving from developer experiments into mainstream tools. They promise steady progress, and they raise new questions about spending, drift and supervision.
At its Data + AI Summit, Databricks launched a consumption-priced agent for business teams, a single-copy architecture for transactional and analytical data, and a security acquisition, betting that agents need a simpler data estate.
Data built for human analysts isn't ready for autonomous agents, say experts from Snowflake and Precisely. They call for rich metadata, shared data quality rules, lineage and auditability as the foundation for agentic AI.
Leaders from SAP, Skyflow and Fivetran lay out the architecture behind AI that scales: isolation boundaries, model-agnostic interfaces, agents that survive tool failures, and observability that tracks why agents touch data.
OpenAI's $150 million partner network, following Anthropic's $100 million program, shows model makers now see deployment, not model capability, as the bottleneck. Here is what lab-certified consulting armies mean for enterprise buyers.
AWS and Databricks both launched context layers for AI agents this week. Platform-managed context can speed agents into production, but enterprises should insist on exportable semantics and human ownership of meaning.
Supermetrics has Claude analyzing Facebook ads, launching campaigns and publishing live dashboards, while Precisely uses an MCP server to let Claude find and fix SAP material data. Both move AI from reading enterprise data to acting on it.
Leaders from BlackRock, Dollar General and ManTech explain what it takes to run AI agents in production: bounded use cases, engineering discipline, governance and cost visibility.
Lumi AI surfaces inventory insights, Precisely enriches addresses through MCP, SAS Viya turns decision flows into agent tools and Domino Data Lab keeps regulated research auditable: four takes on putting agentic AI to work in the enterprise.
AI agents silently degrade after launch, say Snowflake's Sam Mittal and Monte Carlo's Ethan Post. They outline Cortex Agents' trust features, a four-layer framework for diagnosing bad answers, how stale data skews results and six early warning signals to track.
Mistral AI and SAP are building enterprise agents, while Rhino, Precisely, Skyflow and SecuPi show how federated computing and MCP servers can connect AI assistants to enterprise data without exposing what should stay protected.
Teradata built a LangChain agent that checks loyalty discount eligibility using a vector store and a governed SQL tool. Coder shares what its experiments with agent harnesses, sub-agents and context management did and didn't improve.
Enterprise AI fails when it ignores domain knowledge and chases benchmarks, argues Mistral AI's Aaron Delp. He points to no-code agents, OCR-powered parts search over MCP and an agentic drug submission workflow, and makes the case for right-sized and custom models.
Data warehouses built for weekly reports leave AI agents with stale context, say Striim and Google Cloud experts. Change data capture, event stream processing and MCP can feed agents live operational data on Google Cloud instead.
Fragmented metadata and siloed semantic layers are holding back agentic AI, argues independent analyst Mike Ferguson, and governance must become unified and always on. Collate is applying agents that catalog, classify and document data on a shared knowledge graph.
Informatica, PromptQL, Teradata, Precisely and Lumi AI are building agents that find, clean and enrich data, answer business questions and keep humans in the loop. Here is how each approach works.
Leaders from Revefi, Rhino Federated Computing, Precisely, AtScale and Google Cloud define what makes AI truly agentic and argue human review won't scale. Their alternative: human-on-the-loop oversight, two-tier governance, agents that govern agents and the rise of agent ops.
Striim's Jake Bengtson and Google Cloud's Naveen Punjabi say AI stalls in production when data is stale, pulled straight from operational systems or stripped of business meaning. They outlined four signs your data can support AI agents at scale.
Bots inflate bounce rates, AI agents slip past JavaScript tracking, and agentic browsers mix human and machine activity in one session. Snowplow's Nick Stanchenko explained how to detect each, combine server logs with analytics, and adapt websites to who is browsing.
Mistral AI fine-tuned a model, Google Cloud ran agent-driven churn analysis, Aerospike powered real-time nudges, Precisely added location context, Striim masked data in flight and PromptQL flagged its own assumptions: six takes on practical enterprise AI.
Leaders from Revefi, Monte Carlo, Dollar General and Verizon Business said data and AI observability are becoming one discipline. They explained why unmonitored AI degrades, why observability can't be bolted on after deployment, how to benchmark readiness and why model swaps are a leading cause of drift.
Agentic data management pays off most for the analysts, data scientists and leaders stuck in the data team's queue, Matillion's field engineering team argues. Its Maia agent offers self-service pipeline building, permission prompts, context files that enforce standards and AI-driven troubleshooting.
Snowflake, PromptQL, Google Cloud, AtScale, Dataiku, OneTrust, SAS and Monte Carlo are building agents that answer business questions, semantic layers that keep answers accurate, risk-based AI governance, GenAI for predictive maintenance and observability for production agents.
Supermetrics puts governed marketing data in Claude through MCP, Striim streams real-time operational data with PII masking, and Lumi AI builds an AI data analyst on a curated knowledge base: three approaches to giving agents data they can trust.
Clients love charts but rarely know which ones they want. Broadband Insights CEO Patrick Hutto says his team uses vibe analytics with Plotly's AI Studio to explore data with clients in real time, then moves the best views into production.
AI leaders from Walmart, AT&T, Kellanova and TelevisaUnivision describe how they build trust in AI agents with employees and customers, from AI boards and non-negotiable guardrails to the risks of model upgrades and Walmart's plan to anchor every agent to four super agents.
IBM switched its HR system from Workday to SAP and most employees never noticed, because their HR interface was an AI agent. IBM's Melissa Hadley explains systems of intelligence, the problem of agent sprawl and how orchestrator agents route work across systems.
Infrastructure cost decides whether real-time AI pays off, argues Aerospike founder and CTO Srini Srinivasan. He explains the company's hybrid memory architecture, how faster lookups improve fraud and recommendation decisions, and why agents will multiply database load.
IBM's Ryan Zombo lays out five questions enterprises must answer to move agentic AI into production, from avoiding agent sprawl to avoiding lock-in. Governance works like brakes on a race car, he argues, citing productivity results from IBM's internal client zero program.
Google Cloud's data science agent can plan and build a demand forecast in a BigQuery notebook, even fixing its own error along the way. Google Cloud's Suda Srinivasan calls agents an exoskeleton for data scientists, not a replacement.
No one is closer to enterprise data than the integration platform, argues SnapLogic Senior Solutions Engineer Dylan Vetter. He explains how IT teams can share governed integration assets across business units and build custom MCP servers that connect LLMs to company data.
AI agents raise the bar for data quality, say data and AI leaders from TK Elevator, Baptist Health South Florida and Novelis. They outline a five-step path from messy data to agent-ready data, governance for agents as digital employees and a hub-and-spoke model for scaling.
Machine learning, generative AI and agentic AI each solve a different problem in a legacy quoting workflow, says KMS Technology CTO Guy Merritt. He outlines a prepare, diagnose and implement path for enterprises and shares KMS's own results from AI-assisted software development.
Grid Dynamics built an internal agent mesh for AI agent collaboration. Search and AI Architect Stas Stolpovskiy explains what a central team must control, why platforms need a few strict rules and lots of freedom, and why he thinks MCP falls short for agent-to-agent work.
Snowflake, Denodo, Zenlytic, PromptQL, Fivetran, Google Cloud and Precisely are attacking the same problem from different angles: conversational agents, logical data layers, verified metrics, confidence signaling, fresh pipelines, the Agent2Agent protocol and location data through MCP.
Recommendations, search, RAG and AI agents share more than they seem. Aerospike Chief Evangelist Sesh Seshadri says multi-step inference, feature stores and short-lived agent data sets shape real-time AI infrastructure, drawing on examples from Myntra, PayPal and Barclays.
Seven vendors are putting generative AI to work on enterprise data, from Snowflake Cortex Agents and Gemini 2.5 on Vertex AI to self-service AI analytics, contextual location data, data pipelines and agents that write and debug code.
Leaders from Zenlytic, Nexla, Alation and Dataiku define AI agents as chatbots with a feedback loop. They argue that context and memory are separate problems, that explainability builds trust and that task-specific agents scale better than one generalist.
Snowflake's Cortex Agents work across structured and unstructured data, TiDB builds a knowledge graph for GraphRAG inside one database, and Zenlytic answers an open-ended marketing question with governed metrics: three approaches to AI agents on enterprise data.