AI-Driven Observability for Trustworthy Agentic AI
Agents return a clean 200 OK and still be wrong, unsafe, or expensive. Why agentic AI needs a new observability layer — LLM-as-judge, safety metrics, and the four lifecycle stages.
#AI Governance
AI Governance provides the organisational and technical structures for responsible AI deployment. These articles cover AIGP certification preparation, governance framework implementation, GDPR compliance for AI systems, and the policy-as-code patterns that make governance auditable and enforceable.
5 posts tagged with ai governance. ← All posts
The Right-to-Explanation Handler: GDPR Article 22 as a...
Build a GDPR Article 22 compliant explanation endpoint in Go that turns audit logs and eval stores into regulator-friendly answers for AI decisions.
Studying for the AIGP? Here's a Reference Implementation...
Studying for the IAPP AI Governance Professional credential? Here's an open-source Go codebase that demonstrates ~70% of the body of knowledge in working code.
AIGP body of knowledge — a Go engineer's reading map
IAPP's AI Governance Professional certification covers a body of knowledge worth knowing whether you certify or not. The mapping from BOK to working Go code for the engineer who wants to understand AI governance practically.
The board policy is not a slide — it's a YAML file
The bank's board approves an AI policy. The policy exists as a slide deck nobody reads. The risk team's actual operational policy is what's in the code. Closing that gap is the FREE-AI Rec 14 win.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.