SmartDuke Technologies
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Solution·AI agents·Education

AI agents for Education.

Production-grade agents engineered for education and EdTech.

In brief

SmartDuke builds ai agents for education and EdTech — systems that autonomously complete multi-step workflows that previously required human orchestration. AI tutors fundamentally change the economics of personalized education. Per-student tutoring cost drops by orders of magnitude — but only if the AI is actually grounded and pedagogically sound. Our approach: Eval coverage on every loop, observability into every tool call, and explicit guardrails around action surfaces before the agent ever touches production.

Why education and EdTech are doing this now

The problems
we keep solving.

AI tutors fundamentally change the economics of personalized education. Per-student tutoring cost drops by orders of magnitude — but only if the AI is actually grounded and pedagogically sound.

01 / 03

Personalized learning at scale is expensive

Per-student tutoring is the gold standard but cost-prohibitive at any meaningful scale.

02 / 03

Assessment generation is repetitive busy-work

Creating quizzes, exams, and exercises that align with curriculum is high-effort and low-leverage.

03 / 03

Curriculum adaptation requires constant manual work

Adapting content to grade level, language, and learning style is rarely scoped into product timelines.

Use cases

Three things we'd build
first.

Concrete starting points for ai agents in education and EdTech. We pick the one with the highest leverage and the cleanest measurement story.

  1. Idea 01

    Personalized tutor agent that adapts to student level, surfaces hints, and tracks misconceptions over time

  2. Idea 02

    Assessment-generation copilot that produces quiz questions calibrated to curriculum and difficulty

  3. Idea 03

    Content-adaptation system that translates and adjusts existing materials for new audiences

Outcome metric → Engagement time per student, assessment quality, and learning-outcome correlation
How we engineer it

Production-grade,
from day one.

We use planner-executor, ReAct, or supervisor-worker loops depending on the problem shape — and resist multi-agent orchestration when a single-agent loop solves it.

01 /04

Evals before launch.

Every loop, tool call, and structured output is graded with a frozen test set and an explicit rubric. Failed evals block the deploy.

02 /04

Telemetry from day one.

Traces, latency budgets, token costs, and error rates wired up before the first user touches the system.

03 /04

Guardrails as architecture.

Input validation, output verification, escape hatches, and human handoff paths designed in — not bolted on after incidents.

04 /04

Boring stack on the edges.

Cutting-edge model in the middle. Reliable infrastructure around it. Stability where it earns its place.

Common failure modes we engineer against
  • ×Runaway loops on ambiguous inputs
  • ×Tool-call failures invisible to standard APM
  • ×Context truncation as conversations grow
  • ×Hallucinated actions when grounding is weak
LangGraphOpenAI o-seriesClaude Sonnet 4.6Langfuse
FAQ · 04

Common questions.

01

How long does it take to build ai agents for Education?

Discovery is one week. A working prototype (Spark) is 2–3 weeks. Full production Build for education and EdTech typically runs 8–12 weeks depending on data complexity, integrations, and compliance scope. We commit to a precise timeline at quote stage.

02

What does pricing for ai agents typically look like?

Every engagement is scoped to outcomes, not hours. Discovery starts in the low four figures. Spark and Build are priced per project. Embed retainers are monthly. We return a quote within 24 hours of inquiry.

03

Can you take over an existing agents project that's stalled?

Yes — it's a common engagement. We review what's there, tell you honestly what stays and what we'd rebuild, then ship it to production. Education engagements often start this way.

04

What's different about your approach to ai agents?

Eval coverage on every loop, observability into every tool call, and explicit guardrails around action surfaces before the agent ever touches production. We hold the same engineering bar across every engagement, regardless of industry — but the specifics for education and EdTech are tuned to your trends and pain points.

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