Workflow chains
AuraScore 83/100

Automated Research Grant Compliance and Intake Chain Architecture Plan

Design an automated multi-agent workflow for academic grant intake, policy cross-referencing, and proposal validation.

Use this template when planning an automated grant management pipeline for university sponsored project offices. It outlines an agent workflow that inspects proposals against agency guidelines, internal compliance rules, and budget limits.

Template

Role: Senior Research Operations Solutions Architect with expertise in higher education administration and intelligent process automation.

Context

  • Funding Agency: {{funding_agency}}
  • Solicitation Guidelines Document: {{solicitation_guidelines}}
  • University Compliance Policy: {{institutional_policies}}
  • Grant Proposal Package: {{proposal_components}}
  • Target Workflow Framework: {{orchestration_framework}}
  • Governance and Audit Criteria: {{audit_requirements}}

Task

Formulate a robust operational plan for an automated multi-agent grant compliance chain that systematically ingests, parses, checks, and validates complex academic grant submissions against external funder mandates and internal institutional rules.

Method

  1. Specify the document ingestion agent that parses {{proposal_components}} into discrete sections (abstract, narrative, bio-sketches, budget, data management).
  2. Construct the sponsor guideline analysis agent that extracts strict constraints and requirements from {{solicitation_guidelines}}.
  3. Design a cross-referencing compliance agent to verify page limits, font sizes, formatting, and mandatory section completeness.
  4. Build an institutional compliance agent that evaluates human subjects, animal care, and export controls against {{institutional_policies}}.
  5. Develop a financial audit agent to check budget line items, indirect cost rate calculations, and sub-award documentation.
  6. Architect an aggregation and synthesis agent that compiles findings into a unified pre-flight compliance scorecard.
  7. Detail state-transition handling, exception routing, and audit log generation within {{orchestration_framework}} according to {{audit_requirements}}.

Constraints

  • MUST automatically halt processing and notify human administrators if non-waivable regulatory violations are detected.
  • MUST NOT allow financial components to pass without exact arithmetic and fringe-benefit verification.
  • All compliance decisions must cite specific paragraph references from {{solicitation_guidelines}}.
  • Personally Identifiable Information (PII) within biographical documents must be redacted during agent processing.

Output format

Provide a technical workflow plan structured into:

  • Phase 1: Intake Parsing & Section Segmentation Architecture
  • Phase 2: Parallel Compliance Verification Nodes (detailing sponsor, institutional, and budget check agents)
  • Phase 3: Aggregation Logic, Risk Scoring, & Escalation Triggers
  • Phase 4: Implementation Roadmap & Security/Audit Governance Length must be between 700 and 1000 words.

Self-review

  • Does the plan isolate budget mathematical checks from text formatting verification?
  • Are institutional and funder compliance steps executed with clear fallback paths?
  • Does the design guarantee an audit trail for every automated acceptance or rejection?
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

ai-agents
agents-workflows
education-research
grant-management
compliance-automation
research-ops