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.
Insights
Practical frameworks, evaluation methods and implementation guidance from the AARHIT Systems team. Written to be useful to technology leaders, practitioners and decision-makers.
Before choosing an agent framework, decide what an agent may read, propose, execute and never touch. A practical method for drawing that boundary.
A RAG assistant can fail at retrieval, at reasoning or at answering. Evaluating each stage separately is the fastest way to improve quality.
Agents are not always the answer. A decision guide for matching each process step to rules, robotic automation, AI workflows or agentic execution.
When people trust AI outputs too readily, a human-in-the-loop becomes a rubber stamp. Interface and process choices that keep review meaningful.
Many AI proofs of concept end in a demo and a shrug. A short charter written before the build turns them into decisions.
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.
Most computer vision inspection problems are solved, or doomed, by lighting, camera placement and data collection long before model architecture is chosen.
Most AI projects that stall do so because of data. Twelve questions to answer before committing budget, and what to do when the answers are uncomfortable.
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.
Editorial approach
Articles are written by our founders, Dr. Rohit and Dr. Aarzoo, drawing on the work of our research, engineering and delivery teams.
Next step
Our team is glad to discuss your specific situation, whether it concerns agents, evaluation, automation, data or governance.