Agentic AI

AI Agent Sprawl: Why Enterprises Need an Agentic Control Plane

IBM's Ann Leach explains how an agentic control plane governs AI agents' access, behavior and cost across clouds and vendors at enterprise scale.

AI Deepfake Social Engineering: How Enterprises Can Defend Against It

TrendAI's Sundari Parekh explains how AI agents speed up deepfake fraud and social engineering, and the defenses security teams need now.

From Shadow AI to Sovereign Agents: Why AI Governance Must Run at Runtime

Kindo's Shan Zhou explains why AI pilots fail, why policies must be enforced at runtime, and how to test, audit and cost-manage AI agents.

AI Agents in the Enterprise: Context, Control and Getting to Production

Leaders from Honeywell, Capital One, Synchrony, TelevisaUnivision and EXP Realty share how to give AI agents context, access and oversight.

What Is Context Engineering? A Field Guide to the AI Context Window

Redis's Jessica Emmons explains context engineering, the four most common context failures, why RAG falls short, and how context engines work.

The Agentic Data Contract: Six Data Rules for AI Agent Decision Quality

Informatica's Peter Manta explains why data quality alone isn't enough for AI agents and the six pillars of an agentic data contract.

Agentic Data Engineering: How Matillion's Maia Builds Data Pipelines

At COLLIDE 2026, Matillion's Arawan Gajajiva showed how the Maia AI agent plans, builds and tests data pipelines with a human in the loop.

The AI Context Engineer: How the Modern Data Team Is Changing

WisdomAI's Geetesh Iyer explains why AI analytics pilots fail at scale, why semantic layers aren't enough, and the rise of the AI context engineer.

Mission-Critical AI Agents: The Architecture That Gets Them to Production

LangChain's Christine Manegan explains why production AI agents need an owned harness, evals, observability, middleware and governance.

Data and AI Control Plane: Why AI Agents Need One Identity and Boundaries

NexusOne CEO Anu Jain on why AI agents need one identity, complete context and hard boundaries, and why enterprises should own their ontology.

Streamhouse Architecture: Real-Time Data for Smarter AI Agents

IBM's Ann Leach explains why stale data limits AI agents, what a streamhouse is, how it complements the lakehouse, and how Ferrari used real-time data.

AI Trust Gates and Sandboxed Coding Agents: Precisely and Coder Demos

Precisely demoed an MCP trust gate that blocks agents from acting on low-quality data. Coder showed coding agents running in governed remote workspaces.

From Automation to Autonomy: Trusted Agentic AI Controls for Banks

Midships' Ajit Gupta explains why banks are stuck in AI automation and how purpose, intent and action checks let agents act safely.

Real-Time Data for AI Decisions: Lessons From IBM's Client Zero

IBM's Tim Richer on why stale data stalls AI, the three use cases scaling fastest, and lessons from IBM's internal Ask IBM agents.

Sovereign AI Architecture: 7 Layers for Production-Ready AI Agents

Fulcrum Digital founder Rajesh Sinha explains a seven-layer sovereign AI architecture and why inference routing makes or breaks production AI.

AI Agent Overreach: Why Enterprises Need Context-Based Controls

Rubrik's Cal Al-Dhubaib explains how AI agents overreach, why static security controls fall short, and how context-based controls help.

AI Agents Need Context, Not Just Data: Why Data Lakes Fall Short

Teradata's Ben Sanders explains why data lakes built for humans fail AI agents, and the six kinds of context agents need to deliver ROI.

Governing AI Agents at Scale: Testing, Feedback and a Control Plane

IBM's Sabtain Khan explains why building AI agents is the easy part, and how testing, feedback loops and a control plane get them adopted.

Enterprise AI Agents: Validation Tax, Context and Scaling Past the Pilot

Tokio Marine, Morgan Stanley and Fedcap leaders on scaling AI agents: the validation tax, human on the loop, enterprise context and when to use workflows.

AI Observability: How to Build Trustworthy AI in Production

SAP, Dynatrace and RaceTrac leaders explain AI observability: evaluations, drift detection, token costs, rollbacks and audit trails.

Agentic AI Demos: Snowflake Cortex Code and Precisely's MCP Server

Snowflake demos spec-driven development with Cortex Code, and Precisely shows an AI assistant pulling location risk data through MCP.

Agentic AI Governance: Where Humans Belong in the Loop at Enterprise Scale

AT&T, SAP, Posit, Revefi and Rubrik leaders on governing AI agents: policy layers outside the model, risk-tiered approvals, tracing and least privilege.

AI Demo Day: What Mistral AI, SAS, Fivetran and Precisely Showed

Four enterprise AI demos: Mistral document and voice AI, SAS forgery detection, Fivetran's one-copy data lake and Precisely location data.

Agentic AI With Mistral AI Studio: Why Enterprises Should Own Their AI

Mistral AI's Aaron Delp explains how to pick a first agentic AI use case, run a 3-4 week pilot and build governed workflows in Mistral AI Studio.

Agentic-Ready Data: Why AI Agents Need Metadata, Not Just Clean Data

Snowflake and Precisely experts explain why data built for dashboards fails AI agents, and how metadata, semantic layers and lineage make it agent-ready.

Scaling AI Architecture: How to Build AI Systems That Grow Reliably

SAP, Skyflow and Fivetran leaders explain the isolation, modularity, evaluation and governance patterns that take AI from proof of concept to scale.

AI Workflows in Claude: Supermetrics and Precisely Demo Read-Write AI

Supermetrics and Precisely demo Claude workflows that analyze ad performance, launch campaigns and fix SAP master data through an MCP server.

Agentic AI Demos: Lumi AI, Precisely, SAS Viya and Domino Data Lab

Agentic AI in Action Q2 recap: Lumi AI, Precisely, SAS Viya and Domino Data Lab demo proactive analytics, MCP data enrichment and auditable agents.

AI Agent Observability: Six Early Warning Signals for Production Agents

Monte Carlo and Snowflake on AI agent observability: why agents degrade after launch, a four-layer trust framework and six early warning signals to track.

AI Demo Day Q2: Mistral AI, SAP, Rhino, Precisely, Skyflow and SecuPi

Six 10-minute enterprise AI demos: Mistral AI agents, SAP Joule agents, federated AI, location data via MCP, PII tokenization and AI access control.

How to Build AI Agents With MCP Tools: Teradata and Coder Live Demos

Teradata builds an agent that reasons over policies and tables via governed MCP tools; Coder shares agent harness, sub-agent and context lessons.

Building Enterprise AI Agents With Mistral: Right-Sized, Custom Models

Mistral AI's Aaron Delp demos no-code agents, OCR with MCP search and an FDA submission workflow, and explains model right-sizing and Forge.

Agent-Ready Data Architecture: Streaming Real-Time Data to AI Agents

Striim and Google Cloud explain why batch warehouses fall short for AI agents and how CDC, event streaming and MCP deliver fresh context on Google Cloud.

Semantic Intelligence: Why AI Agents Need Always-On Data Governance

Analyst Mike Ferguson and Collate explain why fragmented metadata stalls agentic AI and how unified, agent-assisted governance and knowledge graphs help.

Agentic AI in Action: Five Live Demos of AI Agents on Enterprise Data

Informatica, PromptQL, Teradata, Precisely and Lumi AI showed AI agents finding, cleaning and analyzing enterprise data in 10-minute live demos.

Agentic AI in the Enterprise: From Human in the Loop to Agent Ops

Revefi, Precisely, AtScale, Google Cloud and Rhino leaders explain what makes AI agentic, why human in the loop won't scale, and how to govern agents.

Four Signs Your Data Is Ready for AI: Real-Time, Governed, Event-Driven

Striim and Google Cloud explain four signs of AI-ready data: current state, derived not accessed, preserved business meaning and event-driven design.

How AI Agents and Bots Break Web Analytics, and How to Fix the Data

Snowplow's Nick Stanchenko explains how bots skew metrics, why AI agent traffic is invisible to analytics, and how to handle agentic browser sessions.

AI Demo Day Q1: Mistral, Google Cloud, Aerospike, Striim and More

Mistral AI, Google Cloud, Aerospike, Precisely, Striim and PromptQL showed agents, churn analysis, real-time nudges, location context and data masking.

Data Observability vs. AI Observability: Why They Must Converge to Scale AI

Revefi, Monte Carlo, Dollar General and Verizon Business leaders explain data vs. AI observability, inference tables, data downtime and model drift.

Agentic Data Management: How AI Agents Let Analysts Self-Serve Data

Matillion's team shows how the Maia agent lets analysts and data scientists build governed pipelines and fix failures without waiting on data teams.

GenAI Demo Day Q4: Snowflake, PromptQL, AtScale, Monte Carlo and More

Snowflake, PromptQL, Google, AtScale, Dataiku, OneTrust, SAS and Monte Carlo demoed AI agents, semantic layers, AI governance and agent observability.

Custom MCP Servers and the Composable, Agentic Enterprise Platform

SnapLogic's Dylan Vetter explains how integration platforms prepare data for AI, democratize integration with governance and build custom MCP servers.

Vibe Analytics for EDA: Turning Weeks of Chart Requests Into Minutes

Broadband Insights CEO Patrick Hutto explains how vibe analytics with Plotly AI Studio speeds exploratory data analysis, mockups and self-service.

Agent Mesh: How to Build an Enterprise Platform for AI Agent Collaboration

Grid Dynamics' Stas Stolpovskiy explains the agent mesh: what to control centrally, top-down vs bottom-up platforms, and why A2A beats MCP there.

Five Questions Enterprises Must Answer to Scale Agentic AI, per IBM

IBM's Ryan Zombo explains how agents, trusted data and centralized governance take enterprise AI to production, and shares IBM client zero results.

AI-Ready Data for AI Agents: Ownership, Curation and Governance

Leaders from TK Elevator, Baptist Health and Novelis explain how to govern agents, curate messy data and scale AI past pilots, at COLLIDE 2025.

High-Performance Data Infrastructure for AI: Aerospike's Cost Case

Aerospike CTO Srini Srinivasan explains how hybrid memory cuts AI infrastructure costs and why agentic AI multiplies real-time database load.

Orchestrator Agents and Systems of Intelligence: IBM on Future of Work

IBM's Melissa Hadley explains systems of intelligence, agent sprawl and how orchestrator agents in watsonx Orchestrate route work across HR, sales and IT.

Data Science Agents in BigQuery: Agentic AI for Data Scientists

Google Cloud's Suda Srinivasan demos a data science agent building a bike-share demand forecast in BigQuery and explains what agents can't replace.

The AI Maturity Journey: Machine Learning, GenAI and Agents in One Workflow

KMS Technology CTO Guy Merritt explains how ML, generative AI and agents fit one workflow, and why AI coding gains vary from 3x to a slowdown.

Trusted AI at Scale: Walmart's Super Agents, AT&T's AI Board and More

Leaders from Walmart, AT&T, Kellanova and TelevisaUnivision discuss building trust in AI agents, governance boards, model upgrades and agent sprawl.

Agentic AI Showcase: Supermetrics, Striim and Lumi AI Demo Data for Agents

Supermetrics, Striim and Lumi AI demo how AI agents get trusted data: marketing data via MCP, real-time CDC with masking, and an AI data analyst.

GenAI Demo Day Q3: Snowflake, Denodo, Zenlytic, PromptQL and More

Snowflake, Denodo, Zenlytic, PromptQL, Fivetran, Google Cloud and Precisely demo AI agents, semantic context, A2A and MCP servers in 10 minutes each.

One Real-Time Data Layer for Predictive, Generative and Agentic AI

Aerospike's Sesh Seshadri explains multi-step inference, why recommendations, search and RAG share one pipeline, and the data agentic AI needs.

GenAI Demo Day Q2: Snowflake, Google, Precisely, Zenlytic and More

Seven GenAI demos: Snowflake Cortex Agents, Gemini 2.5 on Vertex AI, Precisely Data Graph, Zenlytic, Fivetran, Dataiku and Zerve's data science agent.