# Case Study — EduAccess Agent ID

## Context
Education aid opportunities exist, but application readiness frequently breaks at the information-navigation layer. This project addresses that operational gap with a narrowly scoped AI agent MVP.

## Problem Statement
Students facing financial pressure often fail to convert search effort into application action because opportunity information is fragmented, eligibility language is difficult to parse, and process prioritization is unclear.

## Product Hypothesis
If users receive concise, context-aware recommendations with rationale and immediate next steps, then more users can progress from intent to application-ready behavior.

## Scope
- Single use case: education-aid navigation for budget-constrained students.
- Single delivery channel: API-first assistant runtime.
- Portfolio standard focus: reliability, clarity, and evaluation traceability.

## Solution Design
The runtime uses a planner-retriever-reasoner loop:
1. parse user intent and enforce input guardrails,
2. retrieve candidate aid options from curated data,
3. return prioritized recommendations and action checklist,
4. expose limitations and trace for transparent behavior,
5. fallback to safe guidance when retrieval fails.

## Key Decisions and Tradeoffs
- Narrow scope was chosen over broad feature surface to maximize quality under event time constraints.
- Curated retrieval was chosen over web-scale connectors to guarantee deterministic behavior for demo and validation.
- Structured outputs were prioritized to improve trust and interoperability.

## Validation Approach
Validation is scenario-based with five repeatable paths:
- happy path,
- ambiguous input,
- retrieval timeout,
- no data match,
- correction flow.

The evaluation script provides status, confidence, and latency indicators for each path.

## Results Summary
- API tests pass on health, metadata, and task endpoint behavior.
- Scenario suite demonstrates resilience under ambiguity and retrieval failure.
- Outputs remain structured and traceable across all tested conditions.

## Limitations
- No production-grade source federation yet.
- No persistent storage layer yet.
- Relevance scoring is baseline and not user-calibrated.

## Next Step Focus
- verified source expansion,
- ranking quality improvements,
- persistent observability and trend analysis.