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How enterprises design, govern and scale AI agents that plan, reason and act.
Investors punished ServiceNow over a half-point bookings miss and fears that AI agents will gut enterprise software. The AI Enterprise Magazine Team argues agents raise the value of the control layer, with two big conditions.
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
Analysis · Data & AI Infrastructure
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
Opinion · AI Governance & Trust
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
News · Observability & Data Quality
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
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.
By The AI Enterprise Magazine Team ·
