Governing AI Agents: Start With an Authority Map
Before choosing an agent framework, decide what an agent may read, propose, execute and never touch. A practical method for drawing that boundary.
Author profile
Articles by Dr. Rohit
Dr. Rohit co-founded AARHIT Systems on 5 October 2017 and serves as its Director and Chief Technology Officer. He writes on technology strategy, AI architecture, agents, evaluation and AI governance, drawing on the engineering and delivery work of the AARHIT Systems team.
Profile
- Role
- Co-Founder, Director and Chief Technology Officer
- Organisation
- AARHIT Systems
- Articles
- 8
- Topics
- Agentic AI, Generative AI, Automation, Machine Learning, AI Governance, Emerging Technologies
Editorial standards
How our articles are written and reviewed
He is accountable for corporate strategy, organisational growth, client relationships, technology strategy, engineering standards and architectural governance. Major solution designs pass through technical reviews that he leads, and he oversees the company's security and AI practices. Dr. Rohit is responsible for corporate strategy, technology direction, solution architecture and engineering governance. The articles below draw on that work.
Articles are written to be useful, accurate and specific. They draw on established methods, recognised frameworks and practical engineering experience, and they avoid claims that cannot be supported.
Where external research or frameworks are referenced, sources are listed. Articles do not describe named client engagements. Statistics are used only when they come from a cited, reliable source.
Articles
Published articles
How to Evaluate a Retrieval-Augmented Generation System
A RAG assistant can fail at retrieval, at reasoning or at answering. Evaluating each stage separately is the fastest way to improve quality.
RPA, AI Workflows or Agents? Choosing the Right Level of Automation
Agents are not always the answer. A decision guide for matching each process step to rules, robotic automation, AI workflows or agentic execution.
Why Random Splits Mislead: Validating Models the Way They Will Be Used
A model can score brilliantly in testing and disappoint in production because the test did not resemble real use. How to design validation that tells the truth.
Risk Tiering: Proportionate AI Governance That Teams Will Follow
Heavy review for every AI use drives teams around governance. Tiering uses by risk lets low-risk work move quickly and focuses scrutiny where harm is possible.
Edge or Cloud? Deciding Where AI Inference Should Run
Latency, connectivity, privacy, model size and fleet management all pull the decision in different directions. A structured way to weigh them.
Prioritising AI Use Cases: A Scoring Approach That Survives Reality
Workshops produce long lists of AI ideas. A transparent scoring model balances value, feasibility, risk and readiness so the first projects are the right ones.
Five Questions to Ask Before Any AI Project
Before choosing models or vendors, five plain questions reveal whether an AI idea is ready, what it needs and how it could go wrong.
Next step
Want to discuss an article with our team?
Get in touch with questions or to explore how the ideas apply to your organisation.
Contact AARHIT Systems
Or write to care@aarhit.com