ibl.ai Agentic AI Blog

Insights on building and deploying agentic AI systems. Our blog covers AI agent architectures, LLM infrastructure, MCP servers, enterprise deployment strategies, and real-world implementation guides. Whether you are a developer building AI agents, a CTO evaluating agentic platforms, or a technical leader driving AI adoption, you will find practical guidance here.

Topics We Cover

Featured Research and Reports

We analyze key research from leading institutions and labs including Google DeepMind, Anthropic, OpenAI, Meta AI, McKinsey, and the World Economic Forum. Our content includes detailed analysis of reports on AI agents, foundation models, and enterprise AI strategy.

For Technical Leaders

CTOs, engineering leads, and AI architects turn to our blog for guidance on agent orchestration, model evaluation, infrastructure planning, and building production-ready AI systems. We provide frameworks for responsible AI deployment that balance capability with safety and reliability.

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ibl.ai Platform Updates — Week of January 30, 2026

Jaione AmigotJanuary 30, 2026
Premium

Weekly platform update for the week of January 30, 2026, covering new features across Data Manager, ibl.ai, and skillsAI—including MCP Analytics, Search MCP, RBAC Enrollment Managers, Team Management, Groups, Agent Editor, External Credentials, and Code Interpreter.

Here's this week's platform update: A quick summary of new features across Data Manager, ibl.ai, and skillsAI, plus a highlight from our ongoing collaborations and reflections on AI's role in education.


Highlighted Features

MCP Analytics MCP

Enable agents to query platform analytics directly through MCP, supporting data-driven conversations around usage, performance, and outcomes.

Search MCP

Allow agents to perform structured searches across indexed content and platform data using MCP, improving retrieval accuracy and contextual responses.

RBAC Enrollment Managers

Grant users the ability to invite and enroll learners into programs and courses without full administrative access.

Learner Management

Control who can view, manage, and organize learners within the platform.

Team Manager

Allow users to create and manage teams they own, including adding and organizing members.

Team Sharing

Enable controlled sharing of teams across users and roles to support collaboration without over-permissioning.

Bulk Team Management

Efficiently manage large groups of learners and teams through bulk actions and uploads.

Groups

Use Groups to organize users at scale and apply permissions consistently across skillsAI and ibl.ai.

Agent Editor

Assign users the ability to create, edit, and manage agents without granting broader administrative privileges.

Additional Updates

External Credentials

Support external badges and credentials to recognize learning achievements beyond the platform.

Code Interpreter

Students can run Python code, spreadsheets, and more directly in chat to analyze data, generate charts, and explore concepts interactively, without leaving the conversation.

Reflections

Campus leaders share why endless proofs of concept aren't enough — and how avoiding pilot fatigue is key to moving AI strategy into action.

AI equity and infrastructure: building access that supports all learners and ensures institutional AI tools serve the entire campus community.

Students as agents and builders: how role-based access control (RBAC) unlocks secure, scalable participation in AI-powered workflows.

Highlighted Partnerships

We're proud to partner with Union Theological Seminary, advancing a values-driven collaboration that explores how AI can support ethical, mission-aligned learning and teaching. This work centers on empowering faculty and learners with tools that respect community values and foster human-centered inquiry across theological and liberal arts contexts.

Related: What Is an Enterprise LLM Platform? The One You Own · Why AI Agent Infrastructure Matters More Than the Model You Choose

Why does owning the AI stack matter?

ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.

  • You own all the code and the data

    Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.

  • Model-agnostic

    Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.

  • No per-seat pricing

    Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.

  • Deploy anywhere

    Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.

1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.

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Mikel AmigotDecember 15, 2025

ibl.ai Weekly Update — Week of November 14, 2025

Weekly platform update for the week of November 14, 2025, featuring the Analytics & Insights Dashboard, Auto-Retraining Datasets, One-Click In-Chat File Uploads, Smart Agent Defaults, Database Acceleration, Media-First Chat, and Flagged Prompts governance—plus a partnership spotlight on Fort Hays State University.

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ibl.ai Weekly Update — Week of November 7, 2025

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See the ibl.ai AI Operating System in Action

Discover how leading universities and organizations are transforming education with the ibl.ai AI Operating System. Explore real-world implementations from Harvard, MIT, Stanford, and users from 400+ institutions worldwide.

View Case Studies
Work with our team

Pilots, deployment, and full ownership

Most enterprise engagements are one-time, not subscriptions. You integrate ibl.ai with your own data, deploy it on your own infrastructure, and the engineering hours scale with the work — so the price tracks the scope, not your headcount.

Start here

Pilot

from $15K

fixed scope · fixed timeline

A time-boxed proof of value on your real data — not a slide deck.

Best for: Teams that want to see ibl.ai working before committing.

  • Deployed on your infrastructure or our cloud
  • 1–2 production agents wired to a slice of your data
  • One integration (LMS / SIS / SSO / data source)
  • Weekly working sessions with our engineers
  • Pilot fee credits toward a full engagement
Scope a pilot
Most common

Integration & Deployment

$25K – $80K

one-time · not a subscription

Full deployment integrated with your data and systems. Engineering hours scale with scope.

Best for: Organizations rolling ibl.ai out across a department, campus, or business unit.

  • Platform deployed in your VPC, on-prem, or air-gapped
  • Integrated with your data + identity (SSO / SAML)
  • Multiple custom agents built to your workflows
  • Engineering hours proportional to scope
  • You own the data · run any LLM you choose
Plan a deployment
Full ownership

Codebase Transfer + Custom AI Engineering

Six figures

perpetual license · you own the stack

We transfer the full source code. You own and self-host the entire platform — outright.

Best for: Government, defense, and enterprises that require perpetual ownership and sovereignty.

  • Complete source-code transfer + perpetual license
  • Dedicated AI engineering team on your roadmap
  • Custom agents, models, and integrations to spec
  • Air-gapped capable · zero vendor lock-in
  • Family-owned, New York–based long-term partner
Talk about ownership
You own the code and data Run any LLM — Claude, GPT, Gemini, Llama Family-owned & operated from New York, NY