Agentic GRC Platform
Multi-agent governance, risk, and compliance platform that automates policy analysis and audit-ready reporting.
- Role
- Software-AI Engineer
- Timeline
- 2025 — Present
Overview
Project summary and scope.
An agentic platform designed to help teams navigate governance, risk, and compliance workflows with less manual overhead. The system orchestrates specialized agents for policy ingestion, gap analysis, and structured reporting — turning scattered compliance inputs into audit-ready outputs.
My Contributions
Key areas of ownership and delivery.
- Designed agent roles and orchestration flow for policy alignment and gap analysis
- Implemented backend services for document ingestion and structured output generation
- Defined data models for policies, controls, findings, and audit artifacts
- Built placeholder dashboards for reviewer workflows and export-ready reports
- Documented architecture and evaluation approach for stakeholder review
Core Features
Primary capabilities delivered in this project.
- Multi-agent policy ingestion and classification pipeline
- Control framework alignment with explainable gap findings
- Audit-ready report generation with export placeholders
- Reviewer dashboard for validating agent outputs
- Configurable workflow stages for different compliance contexts
Architecture
How the system is structured at a high level.
The system follows a layered architecture: ingestion services normalize policy inputs, an orchestration layer routes tasks to specialized agents, a persistence layer stores policies and findings, and an API layer exposes review and export endpoints. Agent outputs are validated through schema-constrained responses before surfacing to users.
Impact
Outcomes and value delivered.
- Designed agent workflows to reduce manual compliance review cycles
- Created structured outputs that support audit-ready reporting placeholders
- Established a reusable architecture pattern for GRC automation prototypes
Challenges
Constraints and difficulties encountered during delivery.
- Balancing agent autonomy with compliance-grade traceability requirements
- Handling ambiguous policy language without overconfident outputs
- Designing review flows that keep humans in the loop for high-stakes decisions
Tradeoffs
Key decisions and the reasoning behind them.
- Chose modular agents over a single monolithic prompt for maintainability, at the cost of higher orchestration complexity
- Prioritized explainable intermediate outputs over fastest end-to-end automation
- Used placeholder integrations for external GRC systems to accelerate MVP delivery
Future Improvements
Next steps that would strengthen or extend this work.
- Add human-in-the-loop approval gates with full audit logging
- Integrate with enterprise identity and document management systems
- Expand evaluation benchmarks for policy alignment accuracy
- Support multi-framework control mapping out of the box
Problem and Solution
Problem
Compliance and GRC teams often work across fragmented policy documents, manual review cycles, and inconsistent reporting formats. Translating policies into actionable controls and gap analyses is slow, error-prone, and difficult to scale.
Solution
Built a multi-agent workflow that ingests policy inputs, aligns them against control frameworks, surfaces gaps, and generates structured compliance outputs. The platform combines backend orchestration, LLM-assisted reasoning, and review-friendly dashboards.
Tech Stack
Technologies used across this project.
- Python
- FastAPI
- LangChain
- PostgreSQL
- AWS
- React