Meta just rolled out a fresh feature on Threads called Ask Meta AI, and it's designed to boost adoption of Meta's chatbot while making life easier for users hunting down information about posts. Here's what's interesting: you can fire questions at the bot about literally any aspect of a post—whether that's summarizing the text, digging into embedded videos, or even requesting information Meta pulls from external sources. All without leaving the app.
Meta is rolling this AI-powered platform across its entire ecosystem in different forms. On Instagram, it powers image editing tools. On Threads, it works as an inline chatbot for individual posts. Users can accomplish a lot through simple, straightforward commands—no complex prompts required. Below is your step-by-step guide to getting the most out of Ask Meta AI on Threads.
How to Use Ask Meta AI on Threads
Step 1:
Open any post on Threads and tap the three-dot menu icon in the top right corner. Then select Ask Meta AI from the menu.
Step 2:
The AI chat interface will open and automatically display key information about the post—including text, images, videos, and other media. No input needed on your part. Meta serves up the essential details in your language, highlighting the most important points from the post.
Want deeper insights or have questions about video content? Just type your query and send it.
Step 3:
Meta AI extracts and analyzes video content for you. Everything comes back in your preferred language, even if the original post or video was created in a different language.
Need a condensed version? Simply ask Meta AI to create a summary of the key points.
What Can You Do With Ask Meta AI on Threads?
Ask Meta AI lets you get quick answers about any post directly within Threads. No need to copy-paste content into a separate chatbot. The real benefit here is convenience—everything happens in context.
The feature supports several practical uses:
Summarize lengthy posts when you don't have time to read the full text.
Break down confusing terminology or complex concepts mentioned in the post.
Get answers to follow-up questions related to the post's topic.
Receive additional context or background information for better comprehension.
Translate post content into another language.
Interpret images attached to posts, when AI image analysis is supported.
Description: Learn how to use Meta AI's new Ask Meta AI feature on Threads to get instant summaries and answers about posts.
Generative AI is evolving at breakneck speed, but here's the thing: the mathematical principles and architectural foundations behind large language models (LLMs) have actually been well-documented for years. A few years ago, understanding traditional NLP and RNNs was enough. Then Transformers arrived and reset the game entirely. Now, grasping how these models are built, trained, and deployed has become essential knowledge for AI engineers, data scientists, and anyone shipping AI-powered products.
If you're ready to move beyond just calling ChatGPT or Claude APIs—if you actually want to understand how a foundation model works—you need structured learning material, not scattered blog posts. Here are five highly-regarded books that will take you from theoretical foundations all the way through practical implementation of Large Language Models.
1. Build a Large Language Model (From Scratch) – Sebastian Raschka
The best way to understand a complex system? Build it yourself. That's the philosophy driving Build a Large Language Model (From Scratch) by Sebastian Raschka.
Rather than just explaining concepts, this book walks you through constructing, training, and fine-tuning an LLM from the ground up using PyTorch. Every step is detailed thoroughly, giving you hands-on exposure to core components like tokenization, embeddings, attention mechanisms, Transformer architecture, and training optimization techniques.
What's interesting here is the book comes with over 20 annotated Jupyter notebooks. You're not just reading—you're coding alongside, watching data flow through each layer of the Transformer network. Perfect for AI engineers and researchers who need to truly understand what happens at each computational step. The theory-plus-code combination is particularly powerful.
2. The Hundred-Page Language Models Book – Andriy Burkov
Not everyone has time to build models from scratch. If you need a comprehensive overview fast, The Hundred-Page Language Models Book by Andriy Burkov is your move.
The title's honest: it compresses the entire evolution of language models into roughly 100 pages without sacrificing technical accuracy. Burkov guides you from classic n-gram models through modern architectures like BERT and GPT. Mathematical concepts are explained clearly with visual diagrams and concise Python examples throughout.
Each topic—pretraining, attention, text generation—gets its own focused chapter. You won't feel overwhelmed. This is ideal for students or working professionals who want solid foundations without a massive time commitment. The breadth here is impressive for the page count.
3. Hands-On Large Language Models – Jay Alammar & Maarten Grootendorst
Once theory is solid, the next phase is applying LLMs to real problems. Hands-On Large Language Models is written exactly for that transition.
Jay Alammar built his reputation on crystal-clear Transformer visualizations. Maarten Grootendorst is a seasoned NLP specialist. Together, they created something balanced: theory meets practice.
The real standout? Over 250 illustrations. Concepts like attention heads and multi-layer Transformer architecture become intuitive when drawn well. The book also covers semantic search, dense retrieval, prompt engineering, and RAG (Retrieval-Augmented Generation) systems. You'll learn fine-tuning and deployment using open-source tools, especially the Hugging Face ecosystem. This one's perfect if you want to shift quickly from understanding LLMs to building actual AI applications.
4. Natural Language Processing with Transformers – Lewis Tunstall, Leandro von Werra & Thomas Wolf
Where the previous book leans visual and practical, Natural Language Processing with Transformers goes deeper on engineering and professional deployment.
All three authors work at Hugging Face. You're essentially reading the official manual for the most popular open-source AI ecosystem today.
Content covers step-by-step training and deployment of BERT, GPT, T5, and others. You'll learn data preparation, training, fine-tuning, and evaluation workflows. Real-world examples span healthcare, finance, and multilingual NLP. The real concern here is that without practical hands-on experience, you might hit walls. This book assumes you're ready to actually implement things in production contexts. Essential reference material for ML engineers building real AI products with Hugging Face.
5. The LLM Engineering Handbook – Paul Iusztin & Maxime Labonne
Training a model? That's only half the battle. The harder part: turning that model into a stable product serving thousands or millions of users. That's what The LLM Engineering Handbook addresses.
Unlike training-focused books, this is a practitioner's guide for deploying AI systems. It covers the entire LLM lifecycle from initial research through production operations.
Topics include prompt optimization, function calling, tool use, advanced RAG architectures, and large-scale deployment strategies. All presented from a practical, real-world angle. This book is perfect for developers moving beyond simple API calls toward building scalable, reliable AI applications. If you're facing actual production constraints, this is your reference.
Which Book Should You Start With?
These five books form a complete learning progression from foundational theory to practical deployment.
If you're new to the field, start with The Hundred-Page Language Models Book or Hands-On Large Language Models. Both build intuition quickly and give you the big picture. Once foundations feel solid, Build a Large Language Model (From Scratch) teaches you how Transformers actually work under the hood. Next, Natural Language Processing with Transformers gets you productive with industry-standard Hugging Face workflows. Finally, when you're shipping real AI products, The LLM Engineering Handbook becomes your guide for optimization and large-scale operations.
In AI, the people who stand out aren't those who know the most prompts. They're the ones who understand what's actually happening inside the model behind those prompts. Wherever you are in your AI learning journey, at least one of these five deserves a spot on your shelf.
Description: Build real LLM expertise with this curated reading list—from foundational theory to production deployment.
The days of data analysts manually writing hundreds of lines of Python to clean datasets, build visualizations, and generate reports are officially over. A new generation of AI tools is reshaping the entire workflow, automating tasks that once consumed hours of tedious, repetitive work.
Modern AI platforms do far more than just read and analyze spreadsheets. They automatically clean messy data, write and execute Python or SQL code, generate charts, spot trends, explain patterns, and build reusable analytical pipelines—all with minimal human intervention. What's interesting here is that analysts can now skip the grunt work entirely and focus on asking the right questions and extracting actionable insights.
Here are five standout AI tools reshaping data analysis for 2026. Whether you're working with Python, SQL, notebooks, databases, or local projects, these platforms can dramatically cut the time it takes to transform raw data into meaningful intelligence.
Deepnote – An AI-Powered Collaborative Workspace for Data Teams
Deepnote is a collaborative notebook platform with AI baked in from the ground up. At its core sits Deepnote Agent—an intelligent assistant that understands your entire project context, plans multi-step workflows, and automatically adds, edits, or removes Python, SQL, and text blocks to complete complex analytical tasks.
Beyond that, Deepnote packs in a suite of AI capabilities: automatic analysis generation, SQL query suggestions, code recommendations and fixes, data visualization, error detection and correction, and code explanation. Describe what you need in plain language, and the AI handles most of the heavy lifting right inside your notebook, with full ability to review or tweak results on the fly.
For enterprise users, Deepnote supports OpenAI-compatible models and offers Deepnote MCP, allowing popular AI tools like Codex, Claude, Cursor, and Visual Studio Code to connect directly with your notebook and active projects.
If you're working in teams or need a data environment where AI is deeply woven into your daily workflow, Deepnote is one of your strongest bets right now.
ChatGPT – Straightforward Yet Powerful Data Analysis
ChatGPT is probably the most accessible entry point for anyone new to AI-assisted data work.
Upload a CSV or Excel file and you can ask ChatGPT to clean your data, find trends, spot anomalies, run Python calculations, build tables, draw charts, and summarize findings in plain English—with virtually no coding required. The real convenience here is that non-technical users can accomplish serious analytical work in minutes.
In testing with a student burnout dataset, ChatGPT automatically constructed a complete dashboard with key metrics, visual charts, and a clear summary without requiring any manual code writing from the user.
That capability expands further with ChatGPT Work. Feed it data and your end goal, and it can analyze everything and produce reports, spreadsheets, presentations, or even a finished website.
OpenAI has also rolled out GPT-5.6 on ChatGPT, while ChatGPT Work subscribers can access newer models like Sol, Terra, and Luna depending on their plan. According to OpenAI, these new models significantly boost analytical depth and spreadsheet handling.
For quick data exploration, rapid visualization, and summary reports, ChatGPT delivers instant results and remains the go-to choice for speed and simplicity.
Claude Code – AI That Writes Code and Completes Full Analytical Workflows
Though not a dedicated data science platform, Claude Code proves surprisingly effective for Python-based analytical projects.
Drop your dataset into a project folder, open Claude Code's command interface, and ask the AI to inspect your data, write Python scripts, run the analysis, fix bugs, generate charts, and export a finished report. It handles the entire pipeline with minimal input.
In testing, Claude Code built a complete analysis script, executed it against a student burnout dataset, validated the results, and generated a 146-line Markdown report automatically.
The real strength of Claude Code is its ability to read files, edit code, and execute commands directly on your system, making the whole process feel almost hands-off.
Claude Code now exists as more than just a CLI tool—it's available as a desktop app, a Visual Studio Code extension (which previews changes inline), and a web version. The VS Code extension shows code edits in real-time, while the desktop app can manage multiple local and remote sessions simultaneously.
This is the right pick for large-scale data science projects, automated reporting pipelines, and building reusable analytical frameworks.
DataLab – An AI Notebook Built for Beginners
DataLab from DataCamp is a data analysis notebook with an integrated AI Assistant that works much like ChatGPT.
Connect your data source, describe what you need in natural language, and the AI generates Python, R, or SQL code, runs the analysis, visualizes results, and explains the findings.
In testing with Google BigQuery as the data source, a single simple question about ticket sales was enough for the AI to read the data, perform analysis, create a chart, and explain the outcome.
DataLab goes beyond chat—it also supports prompt-to-code generation, code editing, auto-completion, debugging, and code explanation. All results stay visible in the notebook so you can review, modify, and re-run as needed.
In many ways, DataLab mirrors Deepnote by combining collaborative notebooks, connected data sources, AI chat, code generation, and reporting in one workspace. The difference is that DataLab feels more approachable for learners navigating the DataCamp ecosystem.
VS Code with Codex – The Professional-Grade Data Analysis Environment
Codex now lives across multiple platforms: the ChatGPT desktop app, CLI, IDE extensions, and web interfaces.
But the most natural experience is using Codex directly inside Visual Studio Code.
Keep a notebook, Python scripts, CSV files, and reports all open in one project. Ask Codex to read the full context, edit your code, run analysis, fix errors, or extend your pipeline—all with brief commands. The Codex extension can use open files or selected code snippets as context, letting it propose more precise edits while showing changes alongside your original code.
This makes the entire workflow far more interactive. Instead of reading answers in a chat window, you get notebooks, scripts, charts, and reports living inside your project, ready for further refinement and development.
For data analysts or engineers already comfortable with VS Code, this is one of the most flexible and powerful AI solutions available today.
AI Is Fundamentally Reshaping How Data Gets Analyzed
AI has already transformed how data professionals work. Instead of manually building every step—cleaning data, writing charting code, drafting reports from scratch—analysts now provide data and goals, then let AI handle the repetitive work.
Need fast exploration or initial data discovery? ChatGPT is your shortcut. Want an AI-integrated notebook experience? Deepnote and DataLab deliver that seamlessly. Running large Python projects or automated reporting pipelines? Claude Code excels there. Already living in VS Code? Pairing it with Codex creates a seamless workflow where notebooks, scripts, data, and reports all live in one unified project.
But here's the critical reminder: as AI gets smarter, the most important principle never changes. Let AI speed up your work, but always verify the code, the math, and the conclusions yourself before making decisions based on your data.
Description: Discover the top AI-powered platforms transforming data analysis—from ChatGPT to Claude Code. Automate cleaning, coding, and reporting in 2026.
The most effective applications of Claude Fable 5 share one common thread: they're large, multi-step projects that would normally land on a team member's desk for days of manual work. Fable 5 is Anthropic's most powerful model—and its priciest—so burning through credits on quick summarization tasks or dashing off a short email is honestly wasteful. The teams getting real value out of this model hand it complex, messy problems and let it plan, execute, and validate its own work.
Below are 8 concrete jobs where Fable 5 demonstrates clear, measurable value for the price. Each example was executed directly in Claude Cowork or Claude Code.
Before diving into the list, here's the golden rule for cost efficiency: Think carefully about what you're using it for.
"Many people test Fable 5 on simple tasks like quick summaries or writing a short email. The output is good, but they burn through half their monthly allocation in one go."
If you're debating between Fable 5, Opus 5, or Sonnet 5 for a specific task, start by reviewing guidance on selecting the right Claude model for each task. That resource covers when frontier models make sense and when they don't. This article focuses on the flip side: the specific jobs where Fable 5 genuinely shines.
1. Audit Your Business Systems End-to-End
Fable 5 is famous for code testing, but it's equally powerful for business audits. One example tasked it with optimizing a lead-generation funnel and boosting conversion rates. It was given a web browser, a test email address, and access to email sending tools, community platforms, and the company's CRM system.
The model experienced the entire funnel as a real prospect would. It examined every email a new user receives, then proactively dug through the data warehouse for context and produced a comprehensive onboarding audit. It didn't just report findings—it drafted two new email sequences designed to lift conversion rates, ready for review and approval.
The same approach works for evaluating customer support team performance by submitting real support tickets, or testing employee onboarding by walking through it as a new hire would.
2. Build a Local Clone of Paid Software
Let's say you use a paid image compression tool that caps daily processing on the free tier. Instead of upgrading, ask Fable 5 (in Claude Code) to build a local version.
Here's what's clever: don't ask it to build the whole system at once. Instead, Fable 5 researches first. It uses the built-in browser in Code to open the original tool, uploads a test image to understand how it works, then creates a detailed build plan.
That plan is solid enough to hand off to a cheaper model for execution. The result? A local app that compresses images without limits and at high quality—complete with a right-click Finder shortcut that Fable 5 added on its own.
3. Redesign Poorly Formatted Presentations
Image processing is one of Fable 5's genuine strengths, and it leverages this to self-check its own work. One example gave Fable 5 an intentionally badly designed deck plus the company's brand kit. It redesigned the slides, but before handing them back, it screenshotted all 10 slides, analyzed each one, caught errors it had made, and spawned a new task to fix them.
The final output: a professional deck in two formats—a clean HTML version that follows the original style guide exactly, plus an editable PowerPoint. This self-validation capability is what separates frontier AI models from cheaper alternatives when dealing with image-heavy work.
4. Deploy Packaged Service Systems for Clients
If you run a service agency, you've probably built templated systems to deploy for every new client—maybe a comprehensive AI-forward SEO system with Airtable databases and n8n workflows. Instead of manually rebuilding it for each client, hand Fable 5 the template system and new client details, then let it deploy a custom copy.
Since this involved 7 workflows, it spun up 7 sub-agents—each building one process—then validated everything and drafted handoff docs. A complete, ready-to-run system for the new client from a single prompt. This is the kind of repetitive deployment work that's worth turning into a specialized skill.
5. Reconcile and Understand Messy Data
Revenue numbers from your accountant don't match data from your internal tools. Rather than manually reviewing hundreds of transactions, give Fable 5 exported data files from all 5 systems showing the discrepancy. It built a revenue reconciliation tool in Cowork and identified 3 specific causes—bugs that would've taken days to unearth manually.
The same process scales to any fragmented data scattered across tools: matching customer source attribution, auditing inventory against orders, anything. Just point it at raw data sources, ask a question, and ask it to prove the numbers.
6. Build Custom Internal Tools
The next task: build a hiring system using two paid tools—Airtable and Tally—plus the company's brand guidelines. In one execution, it created an Airtable database, set up a Kanban interface for candidate management, built an application form and careers page on Tally, then used Claude in Chrome to manually test each page (both desktop and mobile) to validate everything worked.
It even documented step-by-step instructions for the one manual step it couldn't automate through connectors, to complete the flow. The result: a fully custom, end-to-end hiring process built on existing tools.
7. Migrate Between Systems
Moving automation workflows from one platform to another is labor-intensive and error-prone. Fable 5 was tested in Claude Code to migrate a complex automation workflow to a new platform. It first researched the target platform's capabilities to understand how to rebuild the process.
It executed the migration successfully, generated a detailed report on what couldn't be converted directly, and rebuilt the entire workflow with all data fields populated correctly. This approach scales to handling dozens or hundreds of workflows in parallel.
8. Build a Complete Marketing Funnel from Scratch
This was the biggest task handed to Fable 5. Using Claude Code's "goal mode"—which lets Fable 5 keep working until a specific condition is met—the challenge was to build a skill set that generates a complete lead-gen campaign funnel from just one brief requirement summary.
The system proposed messaging angles, created image ad content using Python libraries, built and deployed a landing page through website tools, wrote asset delivery emails and conditional nurture sequences, connected everything with automation, and set up the entire campaign in Meta Ads Manager including audience targeting and lookalike audiences.
What's interesting here is that no human had to switch between 7 different platforms. Fable 5 owned the entire workflow—from strategy to execution to platform configuration—in a single run.
Description: Discover 8 real-world applications where Claude Fable 5's advanced capabilities deliver genuine ROI—from system audits to building complete marketing
Google Vids transforms your Google Slides presentations into finished videos remarkably quickly. Select the video conversion mode, and the tool automatically generates scripts, organizes content by scene, and adds AI voice-over narration. What's interesting here is how much time you save compared to manually editing video from scratch. This is a game-changer for converting lectures, training materials, reports, presentations, and product demos into professional videos without needing complex video editing software. In this guide, we'll walk through converting Google Slides into Google Vids videos complete with AI-generated scripts and narration, so you can create polished, shareable videos in minutes.
Important note: This feature currently generates English narration only, so keep that in mind for your projects.
Step-by-Step: Converting Slides to Google Vids Videos
Step 1: Start the Conversion
Open your presentation in Google Slides. Click File, then select Convert to video. You'll see all your slides listed.
Deselect any slides you don't want included in your video.
Step 2: Enable AI Features
At the top of the screen, check that AI-powered narration, scripts, background music, and transitions are activated.
Click Continue. Google Vids will generate AI narration scripts for each scene. You can delete any scripts you don't want, or manually enter custom narration for individual scenes.
Scroll down and choose whether to use narration alone or add an AI avatar character on screen. To swap avatars, click the pencil icon.
Browse the available AI presenters, select one, and hit Use this avatar to apply the change.
Step 3: Generate Your Video
Enable Animations AI to add motion effects, then click Create the draft video to generate your AI video from the slides.
Wait a moment while Google Vids processes. Your video will appear with narration, avatar characters, and animations in each scene.
You can customize scene transitions by clicking the transition icon and selecting a new effect from the Transition menu.
Adjust the AI avatar size and position on each scene. Click the avatar frame and drag to resize or reposition it as needed.
Step 4: Download Your Video
Once your video is complete, go to File and select Download to save it.
Best Practices for Converting Slides to Video
To get the best results from Google Vids, follow these guidelines:
One main idea per slide. AI analyzes each slide's content to generate narration. Keep each slide focused on a single topic or message rather than cramming in too much information.
Use clear, descriptive titles. Concise, relevant titles help AI understand your presentation structure, leading to more logical scripts and narration.
Write plain, straightforward text. Avoid abbreviations, unclear symbols, or excessive bullet points. AI might misinterpret context otherwise.
Choose relevant images. Illustrations that match your content make videos more engaging and reduce the need for post-production fixes.
Review and edit AI-generated content. Always check the scripts and narration AI creates. Adjust them to match your tone and audience.
Preview before sharing. Watch the full video start to finish before distributing it.
Frequently Asked Questions
Can Google Vids convert any Google Slides presentation?
Yes. You can use Convert to video on any Google Slides presentation. However, some animations or special layouts may not carry over perfectly after conversion.
Does Google Vids automatically create scripts?
Yes. After converting from Google Slides, Google Vids uses AI to write narration scripts based on each slide's content. You can edit the dialogue before finalizing the video.
Do I need to recreate the video if I edit my Google Slides?
Yes. Changes made to Google Slides after conversion don't automatically sync to the video you've already created. You'd need to reconvert if substantial edits are made.
Can I add images or video clips after conversion?
Yes. Once Google Vids generates your video, you can insert additional images, video clips, icons, background music, and transition effects to polish the final product.
Is the Convert to video feature free?
Feature access depends on your Google Workspace plan. If your account doesn't yet support Google Vids or AI features, the Convert to video option may not be available.
Description: Learn how to convert your Google Slides presentations into polished videos with AI-generated scripts, narration, and avatars in just a few steps.
Enterprise AI is no longer a future conversation. It's happening now. Microsoft Copilot, Salesforce Einstein GPT, and Google Workspace AI are live, deployed, and actively reshaping how work gets done at major organizations. Yet here's what's emerging from 2026 CIO reports: the tools are ready. The organizations using them? Not so much.
Three Platforms, Three Operating Strategies
Microsoft Copilot—baked directly into Microsoft 365—has become the most discussed enterprise AI tool on the market. Its appeal comes from specific workflow features: automatic meeting summaries, intelligent draft suggestions, and context-switching reminders that reduce friction. For operational and knowledge workers, these add up to measurable time savings per user per week.
Google is running a parallel strategy within Workspace, embedding AI into document management and workflow automation. Enterprise users are seeing real time savings, especially in document-heavy functions like legal, compliance, and procurement. Salesforce's Einstein GPT tackles something different: customer-facing teams. It automates customer interactions and generates personalized responses at scale, letting sales and support teams handle higher volume without proportional headcount increases.
What's interesting here is that these three platforms aren't really competing for the same customer within a single organization. Copilot is the IT infrastructure and productivity play. Workspace AI is collaboration and operations. Einstein GPT is revenue operations and CRM. It's normal for large enterprises to deploy all three simultaneously—which is exactly why AI budget management has become a top CIO priority.
Governance and Trust: The Real Challenge CIOs Face
Beneath the product momentum sits a more complicated operational reality. Employee distrust has emerged as a named barrier to AI scale—separate from technical readiness. Many organizations have deployed AI tools that go underutilized because workers doubt the output quality, lack clarity on accountability, or worry about job displacement. This isn't a communication problem. It's an adoption problem, and it directly crushes ROI on AI spending.
A separate CIO Dive report citing CompTIA research shows the AI skills gap persists even as consumer AI tool adoption spreads. Workers use consumer-grade AI products, but that familiarity doesn't translate to enterprise-level competency. Here's what matters for procurement and operations leaders: buying platform licenses doesn't guarantee workforce capability. Training programs, role-specific support, and certification paths are becoming purchasing considerations alongside the software itself.
On the cost side, Gartner tells CIOs that end-user AI spending will spike dramatically. Contract management, AI architecture governance, and continuous vendor oversight are the three most effective levers for budget control. Multi-year enterprise deals priced by user count can hide actual consumption costs—especially when AI workloads run automatically and create unpredictable usage spikes.
Banking Shows What Real Adoption Looks Like
The financial services sector is where the clearest operational proof is accumulating. Executives at Bank of America, Citigroup, and JPMorgan Chase are all reporting the scale of AI adoption underway and measurable operational impact. Bank of America upgraded its internal customer service tool, EricaAssist, using generative AI. This deployment model—AI augmenting employees rather than replacing them—is what most large enterprises are pursuing.
What's notable about banking's willingness to disclose AI-driven operational changes is precisely that financial services faces some of the strictest compliance and audit requirements in any industry. When regulated organizations report that AI is driving operational change rather than just promising efficiency gains, it signals the technology has cleared internal risk review and legal scrutiny at organizations that set high standards.
Agentic AI and the Cloud Application Sprawl Problem
Beyond productivity tools, a new challenge is forming around agentic AI. CIO Dive, citing Unisys research, reports that technology leaders expect AI agents to become critical for managing cloud application sprawl—but few organizations have moved beyond pilot deployments. For CIOs managing hundreds of SaaS applications, the promise is compelling: autonomous AI agents monitoring, rationalizing, and optimizing cloud usage in real time. The gap between expectation and actual deployment remains wide.
That gap matters operationally because cloud sprawl itself is both a cost and security risk. If agentic AI stays in indefinite pilot mode, organizations pay the cost of sprawl and the cost of endless AI experimentation simultaneously. The real concern is that CIOs are watching this space closely but moving cautiously—consistent with the broader pattern of carefully scaling AI across enterprises in 2026.
The practical lesson for procurement and IT leadership is this: enterprise AI is no longer a single-vendor problem. It's a portfolio management challenge spanning productivity platforms, AI CRM, agentic cloud tools, and the governance infrastructure needed to connect them. Gartner's advice to prioritize contract structure and AI architecture before scaling deployment is the most necessary action in the near term for any CIO whose AI budget is growing faster than their governance maturity.
Description: Enterprise AI has arrived. Microsoft Copilot, Salesforce Einstein, and Google Workspace AI are reshaping work—but organizations aren't ready.
Ask a chatbot to find hotels in London, and you'll get a list to browse yourself. Ask an AI agent the same question, and it autonomously checks availability, compares prices across platforms, books your room, and sends a confirmation email. That gap between "providing information" and "taking action on behalf of humans" defines Agentic AI—and it's become one of the most discussed topics in enterprise AI strategies heading into 2026.
Here's the catch: Agentic AI isn't a single technology. It's a collection of different technical components working together. Misunderstand any one of them, and you'll build agents that lose conversation history, can't use external tools, or work flawlessly in demos but crash the moment they hit production.
The numbers tell a sobering story. Roughly 88% of AI agent projects never make it to production, and the culprit isn't weak language models—it's development teams lacking solid foundational knowledge. What's interesting here is that most failures stem from overlooked technical fundamentals rather than raw model capability.
1. Tool Use and Model Context Protocol (MCP)
An LLM on its own can only generate text. To query databases, call APIs, read files, or send emails, it needs a bridge to the outside world—and that's where Tool Use comes in. Traditionally, each service required engineers to write a custom integration layer. Scale that across dozens of AI applications and hundreds of tools, and you're drowning in boilerplate code that becomes a maintenance nightmare.
Anthropic tackled this problem by introducing Model Context Protocol (MCP) in late 2024. Within a year, OpenAI, Google, Microsoft, and other major tech companies adopted it. By end of 2025, it transferred to the Linux Foundation to become an open industry standard. But here's what really matters: MCP standardizes how AI agents communicate with external tools.
Instead of writing a custom SDK for each service, an agent reads an MCP server's capability list and sends requests using a unified JSON-RPC format. Suddenly, your agent can work with GitHub, Slack, Notion, databases, or thousands of other services through a single protocol. Yes, MCP adds token overhead and latency compared to direct API calls—a real tradeoff to consider.
But if you're managing OAuth, handling multi-tenant permissions, or want non-technical users to connect AI to external tools, MCP is close to the optimal choice.
2. Memory and Context Engineering
By default, each LLM call is completely isolated. Once it answers, the model forgets the entire conversation unless you feed back the chat history. That's fine for simple chatbots, but it's a major blocker for agents that need to work across multiple days, handle ongoing customer support across sessions, or manage projects spanning weeks.
This is why Memory has become an independent architectural component rather than just an extension of context windows. In modern systems, a dedicated Memory layer automatically extracts important information during conversations, stores it in a Vector Database organized by user, session, or agent, and retrieves relevant memories using semantic search, keyword matching, or entity matching when starting a new session. The agent then incorporates these memories into the LLM's context before generating a response.
The user experience feels like the AI remembers them, even though it's really performing an intelligent retrieval step before each response. Platforms like Mem0, Zep, and Letta have become popular alternatives to building custom memory systems in-house. Running parallel to Memory is Context Engineering.
The AI community has shifted focus from Prompt Engineering to strategically selecting, compressing, and organizing information fed to the model. A large context window doesn't guarantee smarter AI if that information lacks curation. What determines output quality isn't the volume of context—it's the quality.
3. Planning and Reasoning Loop
Unlike chatbots that answer once and exit, AI agents must constantly plan, act, observe results, adjust strategy, and repeat. This cycle is the critical difference between conversational AI and autonomous working AI.
The concept originates from ReAct (Reasoning + Acting) research published by Google and Princeton University in late 2022. Rather than pure reasoning or pure action, the model alternates between them in a Thought → Action → Observation → Thought cycle.
Modern Agentic AI frameworks have expanded this pattern with automatic retry logic when tools fail, self-correction, task decomposition into subtasks, and result verification at each step. Instead of accepting "analyze the market" as a single request, the AI breaks it into data collection, competitor classification, information synthesis, and report generation.
The real concern is that this is where production errors flourish most. Without proper controls, agents can burn through tokens rapidly, enter infinite loops, or gradually drift from their original objective. Planning Loops aren't just reasoning techniques—they're infrastructure components that need monitoring and optimization like any other software system.
4. Multi-Agent Orchestration
A single AI agent always hits limits on how much information it can process simultaneously. Feed it an entire codebase, lengthy research papers, and complex business rules all at once, and it quickly loses focus on what matters. That's why Multi-Agent architectures became a major trend in 2026.
Instead of one agent handling everything, the system distributes work across specialized AI agents coordinated by an Orchestrator Agent. Each Sub-Agent focuses narrowly on one task with its own context, making both processing faster and outputs more accurate. Recruiting platform Fountain demonstrated this by applying Multi-Agent architecture to candidate screening, accelerating evaluation by ~50%, cutting onboarding time by ~40%, and compressing the entire hiring process from weeks to under 72 hours.
Today, LangGraph earns top marks for production systems thanks to checkpoint management and state handling. CrewAI suits projects needing rapid role-based deployment. AutoGen remains popular in research environments. Beyond MCP, Google introduced Agent2Agent (A2A) protocol to standardize how multiple AI agents communicate. Where MCP connects AI to tools, A2A lets agents from different frameworks discover each other's capabilities and collaborate on shared tasks.
5. Evaluation, Observability, and Guardrails
This section rarely gets the attention it deserves, yet it determines whether an agent survives in the real world. Statistics show 88% of Agentic AI projects never reach production, while successful ones deliver average ROI of 171%. The difference isn't which model you chose—it's how thoroughly you engineered the technical process.
First comes Observability: the ability to track everything your agent does—which tools it calls, what steps it takes, what it observes, where errors occur. Platforms like LangSmith are becoming the standard for logging this entire lifecycle, making debugging far easier when things break.
Then comes Evaluation, which measures output quality. If Observability answers "what happened," Evaluation answers "did the AI actually do it right?" More platforms now auto-generate evaluation criteria and score agents across multiple dimensions instead of just flagging pass/fail.
Finally, Guardrails are safety controls layered throughout the AI's lifecycle—input validation, reasoning monitoring, internal state tracking, tool call authorization, and output assessment. These don't replace humans but help organizations understand precisely what to check and when. Gartner predicts over 40% of Agentic AI projects could be abandoned before end of 2027 due to rising costs, unclear business value, and missing risk controls. Most of these failures could be caught early with proper investment in Evaluation, Observability, and Guardrails from the design phase onward.
None of these five concepts works well in isolation. If you're starting fresh, you don't need to master all five before writing any code. Pick a well-defined task, connect a single agent to MCP for tool access and a basic memory layer, watch how it reasons through a few real runs, and only introduce multi-agent coordination when a single agent genuinely can't handle the scope. Start evaluating from day one—don't wait for failures to force it.
Description: Master 5 core concepts of Agentic AI: Tool Use, Memory, Planning Loops, Multi-Agent systems, and production safeguards.
OpenAI has officially launched OpenAI Presence, a new enterprise-grade AI agent platform built to help organizations deploy dependable conversational and voice agents across customer-facing and internal workflows. This marks a strategic shift for OpenAI into business automation—the company is essentially saying: we're not just building general-purpose AI tools anymore. They're offering enterprises a proven framework for embedding conversational AI into mission-critical operations.
OpenAI Presence Launches: A Purpose-Built AI Agent Platform for Enterprises
According to OpenAI's official announcement, Presence provides organizations with the infrastructure needed to build, deploy, and manage AI agents capable of handling complex customer service interactions and streamlining internal processes. The platform is designed specifically to address long-standing enterprise concerns around reliability, security, and regulatory compliance when deploying AI solutions.
What Exactly Is OpenAI Presence?
OpenAI Presence isn't a single feature—it's a comprehensive managed platform for building and running controlled conversational AI agents designed for critical business workflows. Think of it as a complete ecosystem that combines advanced language models with organizational policy controls, system integrations, and continuous improvement loops built right in.
What's Actually New Here for Businesses?
Presence positions itself as a comprehensive suite rather than a standalone tool. Organizations can use it to create AI agents capable of handling both voice phone interactions and text-based conversations. What's interesting here is that it's been purpose-built for enterprise requirements—meaning it includes security protocols, compliance frameworks, and integrations with existing business systems out of the box.
The key capabilities include:
Deploy voice and chat agents across multiple channels simultaneously
Enterprise-grade security controls and compliance management
Workflow automation for customer support and internal operations
Integration with existing business infrastructure
Customization options for industry-specific use cases
Two Primary Focus Areas: Customer Service and Internal Workflows
The platform targets two distinct use cases. For customer-facing applications, Presence enables businesses to deploy AI agents that handle support requests, process inquiries, and manage complex multi-turn conversations. These agents can operate across phone systems, chat interfaces, and other customer communication channels.
On the internal side, the platform automates workflows that streamline employee support, IT help desk functions, HR requests, and other operational processes. This dual focus positions Presence as a flexible solution for organizations trying to cut operational costs while maintaining service quality. The real concern is whether companies can actually trust AI to handle sensitive internal workflows—OpenAI is betting they can.
Entering a Competitive AI Agent Market
OpenAI's entry into the enterprise AI agent space comes as more organizations actively search for reliable automation solutions. The company emphasizes that Presence is "battle-tested," meaning it has undergone real-world testing and validation with enterprise customers before public launch. This isn't a beta or experimental release—it's a confident statement about readiness.
The platform competes in a growing market alongside enterprise AI solutions from Microsoft, Google, and specialized vendors. However, OpenAI's track record with GPT models and existing relationships through ChatGPT Enterprise could provide a competitive edge in deployment reliability and model performance.
Security and Reliability: The Enterprise Priority
According to OpenAI, Presence prioritizes reliability and security—non-negotiable factors for enterprise adoption. While specific technical details remain undisclosed in the initial announcement, the platform is said to include control mechanisms that let organizations maintain governance over AI agent interactions, protect sensitive customer data, and ensure compliance with industry regulations.
This emphasis on enterprise reliability reflects lessons learned from ChatGPT Enterprise deployments and addresses common barriers to AI adoption in heavily regulated industries like healthcare, finance, and government services.
Why This Matters
OpenAI Presence represents a significant strategic shift—moving beyond general-purpose AI tools toward specialized enterprise infrastructure. For businesses exploring AI agent deployment, this platform offers a potential path to automation powered by OpenAI's language models. Organizations should evaluate Presence against existing solutions based on integration requirements, industry compliance needs, and specific workflow automation goals. The bigger picture? Enterprise AI agents are transitioning from experimental technology to production-ready infrastructure. OpenAI just made sure everyone knows they're in the game.
Description: OpenAI officially unveils Presence, an enterprise AI agent platform designed to deploy reliable conversational agents across customer service and inte
