AI Agents for Education
FISTA Solutions builds education AI agents with academic integrity designed in: Socratic tutors that guide without producing graded work, rubric-aligned feedback drafting for instructors, practice item generation from approved material, accessibility support, and administrative question answering.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What can AI agents do in education?
Education agents tutor with questions and hints scoped to course material, draft rubric-aligned feedback quoting the student's own work, generate practice items from approved content, draft alt text and plain-language versions, and answer administrative questions from published policy.
- 01
Socratic tutor agent
Guides with questions and hints scoped to the course, refusing to produce the graded artifact itself.
Learning - 02
Feedback drafting agent
Drafts rubric-aligned feedback quoting the student's work for each point, for instructor review.
Assessment - 03
Content and item generation
Generates practice items and variants from approved materials, with subject-matter review before release.
Content - 04
Accessibility support agent
Drafts alt text, captions, and plain-language versions of materials for human verification.
Access - 05
Administrative question agent
Answers student and staff questions from published policy with citations, escalating discretionary cases.
Admin
Requirements
What guardrails do education agents need?
Education agents interact with students and touch protected records, so guardrails cover integrity, privacy, and equity: tutors refuse graded work, instructors approve feedback, student data stays protected, and outputs work with assistive technology.
| Guardrail | Why it matters here | How FISTA implements it |
|---|---|---|
| Academic integrity | A tutor that writes the essay defeats the purpose. | Refusal behavior for graded artifacts, tested adversarially, with hint escalation instead of answers. |
| Instructor authority | Grades and feedback are the instructor's responsibility. | Draft-only output with instructor review and editing before anything reaches a student record. |
| Student privacy | Education records are protected. | Role-scoped retrieval, minimal data exposure, retention limits, and audit logging on record access. |
| Equity of access | AI support must not widen gaps. | Accessible interfaces, multilingual support, and behavior tested across reading levels and assistive technology. |
| Age-appropriate safeguards | Younger learners need additional protection. | Content filters, disclosure of AI use, escalation on wellbeing signals, and COPPA-aware data handling where applicable. |
Where AI fits
Which education workflow should you automate first?
Start with the administrative question agent or feedback drafting. Both save measurable staff time, neither interacts with a learner unsupervised, and they establish the retrieval and review patterns a tutor will need.
- 01
1. Answer administrative questions
Policy and process questions from published sources save staff hours with low risk.
- 02
2. Draft instructor feedback
Rubric-aligned drafts quoting student work, reviewed and edited by the instructor.
- 03
3. Generate practice content
Items and variants from approved material, reviewed by subject-matter experts.
- 04
4. Support accessibility
Alt text, captions, and plain-language drafts that humans verify before publication.
- 05
5. Pilot the tutor
In a low-stakes course, with integrity behavior tested adversarially before wider use.
Cost and timeline
How much does an AI agent for education cost, and how long does it take?
Cost is driven by LMS integration, content readiness, and evaluation of tutoring behavior; timeline by academic calendar and institutional review. FISTA does not quote blind: the scoping call returns an agent design and a calendar-aware plan.
Integrity evaluation is the real work in tutoring agents. Testing refusal behavior against adversarial student prompts, across subjects and phrasings, is what separates a usable tutor from an essay generator.
Institutional review and the academic calendar set the schedule. FISTA produces the privacy and accessibility documentation those reviews require during delivery and sequences releases against term boundaries.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI agent into production?
FISTA delivers agents in four gated phases: a discovery sprint that picks the workflow and writes the agent specification, a design that names tools, permissions, and approval points, a build with an evaluation harness and shadow runs on real work, and a production release with traces, dashboards, and rollback.
- 1
Select and specify
Choose the workflow with a measurable outcome, map its systems and edge cases, and write the agent spec with success metrics.
OutputAgent specification, golden test set
- 2
Design the guardrails
Tool inventory with least-privilege scopes, approval gates, escalation paths, data handling, and the evaluation plan.
OutputTool and permission matrix
- 3
Build and shadow-run
Implement tools as MCP servers or connectors, iterate against the evaluation harness, and run in shadow mode on live inputs.
OutputShadow-mode results, eval scores
- 4
Release and observe
Graduated rollout, full traces, cost and quality dashboards, on-call runbook, and a change process that re-runs the evals.
OutputProduction agent with SLOs
Why FISTA
Why choose FISTA Solutions to build your education agents?
FISTA builds education agents with integrity behavior tested rather than prompted, instructor authority preserved, and accessibility applied to generated output. Work is contracted through a US entity with full IP assignment.
Education specifics
- Refusal to produce graded artifacts is tested adversarially, not assumed from an instruction.
- Feedback and generated content are drafts; instructors and subject-matter experts approve before use.
- Student record access is role-scoped, minimal, and logged, with retention enforced.
- Generated materials meet accessibility standards and are verified before publication.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What teams ask before deploying agents.
Straightforward guidance for evaluating scope, fit, and the next step.
01Will an AI tutor just do the homework?
Not if it is built correctly. FISTA implements refusal behavior for graded artifacts with hint escalation instead, and tests it against adversarial student prompts across subjects rather than trusting a system instruction.
02Can agents grade student work?
They draft rubric-aligned feedback quoting the student's work; the instructor reviews, edits, and assigns the grade. Grading authority stays with the instructor and the record shows who decided.
03How is student data protected?
Retrieval is role-scoped and minimal, access is logged, retention is enforced, and data flows are documented for institutional privacy assessments. Student content is not used to train external models.
04Does this work inside our LMS?
Yes, through LTI 1.3 so the agent appears in the course context with roster and grade integration, rather than as a separate tool students must be told about.
05How long until a pilot?
An administrative or feedback agent can pilot within weeks; tutoring pilots follow integrity evaluation and institutional review, and are sequenced to term boundaries.
Continue exploring
Related capabilities
Scoped in writing before you commit
Support learners without doing their thinking for them.
Bring the course, the feedback load, or the administrative queue. The scoping call returns an agent design, an integrity plan, and an estimate.