๐ ๐ฃ๐๐ ๐ ๐ ๐๐ ๐๐ด๐ฒ๐ป๐๐ ๐จ๐ป๐ฑ๐ฒ๐ฟ ๐ ๐๐ถ๐ป๐ฎ๐ฟ๐ ๐ฆ๐ฒ๐ฐ๐๐ฟ๐ถ๐๐ ๐ฉ๐ฒ๐๐ผ
AI tools give you power. Few give you a brake. Agents write code. They touch data. They make decisions. The main question is who to trust with these actions.
I use three rules:
Explicit laws. I use a fixed set of laws. They are public and the same for every agent. I do not hide rules in prompts.
Binary security veto. An audit layer checks every delivery. It returns a pass or block verdict. There is no middle ground. Bugs reach production when you accept pending issues. It is clean or it does not ship.
Audit trail. Every call leaves a record. It shows the agent and the decision. You verify. You do not trust.
I use niche specialists. Each has a constitution. All follow the same laws and veto. The system routes your needs to the right agent.
Governance has a cost. It adds latency and code. It is overkill for toys. It is required for real money and data. I prefer the brake.
How do you make AI agents predictable in production?
Source: https://dev.to/alex_gonzaga_342705c8b706/i-put-my-fleet-of-ai-agents-under-a-binary-security-veto-heres-why-4d3a Optional learning community: https://t.me/GyaanSetuAi