Blog
Notes on building autonomous institute systems.
How we think about agent quality, permissions, cost per outcome and the infrastructure underneath.
· 6 min read
AI agents are not just faster automation
Traditional automation executes known steps. Agents decide which steps apply. The difference changes how you scope, test and govern the work — and it changes which parts of your admissions or exam process should stay fixed.
· 8 min read
Grounding agents in your own study material
Answer quality is an access-control problem as much as a modelling problem. We look at structuring notes, per-role permissions at query time and citation requirements.
· 5 min read
Cost per outcome beats cost per token
Token pricing tells you almost nothing about whether an agent is economical. Measure the cost of a resolved doubt, a collected instalment or a confirmed admission instead.
· 7 min read
Planning for exam-season traffic spikes
Student traffic is bursty and latency-sensitive. Capacity planning for accelerated inference looks different from batch workloads.
· 6 min read
Building an evaluation loop your faculty trust
Golden sets, tricky edge cases and structured faculty review turn subjective quality debates into a measurable release gate.
· 9 min read
Designing permissions for autonomous systems
An agent needs less access than you think. We describe a permission model based on explicit allowed actions rather than broad accounts.
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