A Biased Way to Pick an Agentic AI Platform for Your University

Let’s be honest up front: this guide is biased. We build agentic platforms for campuses, so of course we have opinions. But those opinions come from hundreds of conversations with CIOs, teaching & learning leaders, and program heads across higher ed. If you’re evaluating platforms (including ours), use the checklist below. If your short list scores well here, you’re on the right track—bias and all.

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Model Freedom (and the Math That Pays for It)

• Question: Are you locked into a single vendor’s per-seat plan, or free to route to multiple LLMs at developer prices?

• Why it matters: Per-seat SaaS feels simple until you multiply it. Per-seat AI tools commonly run ~$20/user/month. A university with 50,000 learners is staring at roughly $12,000,000/year—for one general assistant. A platform approach that calls providers at developer rates (think ~$0.25 per million tokens tier for many tasks) shifts the economics dramatically. Now you’re paying for usage, not for logins, and you can choose the right model (OpenAI, Gemini, Claude, etc.) per task based on cost, quality, and latency.

• Bias showing: Platforms like ours treat LLMs as swappable reasoning engines and do cost routing so you don’t have to.

Data Ownership & Deployment Control

• Question: Does the platform run on-prem or in your cloud, with your keys, your network, your audit trail?

• Why it matters: The most powerful AI use cases need context—student major, course enrollment, progression signals, approved resources. That context shouldn’t be synchronized into a black-box SaaS. Running in your environment (or a private tenant you control) lets you personalize responsibly, meet FERPA and data residency requirements, and keep first-party telemetry.

• Bias showing: We ship code and support campus-owned deployments because trust and governance are table stakes.

Education-Native Plumbing (LTI, xAPI, NRPS/AGS)

• Question: Can you embed assistants in Canvas/Brightspace/Blackboard/Moodle via LTI 1.3/Advantage, pass grades with AGS, and emit xAPI to your LRS/warehouse?

• Why it matters: If students have to leave the LMS, adoption drops. If IT can’t see first-party telemetry, evidence gets fuzzy. Education standards keep learning in-flow and analytics research-ready.

• Bias showing: We’re nerds for LTI and xAPI because plumbing > promises.

Extensibility: “Best of All Worlds” Features

• Question: Can the platform aggregate capabilities across providers (e.g., Code Interpreter from OpenAI, multimodal/screen share from Gemini) behind one API/SDK?

• Why it matters: The feature arms race is real. Your faculty shouldn’t wait on vendor roadmaps to use the latest capability.

• Bias showing: We unify tools behind one API so your teams can build once and inherit new features as they land.

Pedagogy & Policy Controls (Not Just a Textbox)

• Question: Can faculty shape the assistant’s pedagogy (Socratic vs. directive), scope sources, set safety policies (pre- and post-generation checks), and add disclaimers?

• Why it matters: Adoption hinges on instructor agency. If teaching teams can’t tune tone, scaffolding, or domain boundaries, they won’t trust or use the tool.

• Bias showing: We treat prompts, policies, and guardrails as first-class product—because “please trust the default” is not a strategy.

One Backbone, Many Use Cases

• Question: Does the platform cover advising, tutoring, content assistance, and operations on the same backend (RBAC, multi-tenancy, memory, analytics), or does every new use case require a new contract?

• Why it matters: Campuses don’t want ten AI tools with ten bills. A single backbone reduces support surface area, consolidates spend, and speeds up governance.

• Bias showing: Our bias is for a single platform that spans tutoring, advising, content creation, and workflow automation—because budgets.

Memory That Respects Governance

• Question: Is there a persistent, structured learner profile (program, courses, progress cues, accommodations) that the assistant can reference—only within bounds you set?

• Why it matters: Personalization without memory is just wordsmithing. But memory without governance is a risk. You want both.

• Bias showing: We keep memory in your tenant, scoped by policy, and make it readable/writable through a unified API.

Analytics You Can Defend

• Question: Do you get first-party analytics: engagement by cohort, topic understanding, representative transcripts, cost per session, and cost-to-learning views?

• Why it matters: Leaders need to show outcomes, not anecdotes. The formula we see working: engagement (who/when) × understanding (what/how) × cost (efficiency).

• Bias showing: We instrument everything with xAPI and surface program-level to transcript-level views.

Developer Experience That Actually Ships

• Question: Is there a unified API + SDKs (web, Python) so your teams can build agentic apps quickly?

• Why it matters: Your roadmap shouldn’t hinge on a vendor’s Professional Services queue. A great platform unlocks your builders.

• Bias showing: We publish SDKs/docs and keep the backend stable so campus teams can ship.

People: Faculty Enablement & White-Glove Support

• Question: Will the vendor train faculty, offer 1:1 sessions, and help translate “wish lists” into working mentors and workflows?

• Why it matters: Culture change beats feature lists. Real enablement accelerates adoption and surfaces better use cases.

• Bias showing: We invest heavily here because tools don’t transform teaching—people do.

The Biased Checklist (TL;DR)

• Model freedom & cost routing (developer pricing > per-seat)

• On-prem / your cloud (own the data & keys)

• LTI/xAPI/NRPS/AGS (education-native plumbing)

• Feature aggregation (best of all LLM worlds)

• Pedagogy & safety controls (faculty agency)

• One backbone, many use cases (tutoring/advising/content/ops)

• Governed memory (personalize responsibly)

• Defensible analytics (first-party evidence)

• Unified API/SDKs (teams actually ship)

• Real enablement (adoption beats hype)

If a platform checks these boxes, you’ll have something sustainable, governable, and cost-effective—even if it isn’t ours. If you want to see how we check them, we’re happy to be (transparently) biased together. Don’t just take our word for it, visit https://ibl.ai/contact to try it first hand!