Agent Governance Toolkit: what it is and why runtime enforcement is the missing layer
The design argument is straightforward: pre-deployment testing evaluates agent behavior against test cases
The Governance Gap: What Harvey Misses
Autonomous agents are changing legal
Death by Dialogue: The Case for Killing the Legal Chatbot
Legal work needs density, not dialogue.
When AI Acts Before You Decide
The Fiduciary Illusion
Microsoft's Argos and the verification layer AI agents actually need
The framework trains AI agents to be right for the right reasons — not just right by coincidence. For AI governance, that distinction is everything.
AI agents fail because they don't know what "customer" means at your company
AI agents fail because nobody defined what "customer" means in your business. Ontology infrastructure provides semantic guardrails that technical controls alone can't deliver.
Not all AI agents carry the same legal risk
Not all AI agents carry the same legal risk. Your governance framework should distinguish between reflex agents, learning agents, and multi-agent systems — because the liability profile is fundamentally different. https://www.databricks.com/blog/types-ai-agents-definitions-roles-and-examples
Agentic AI doesn't need better models — it needs a data constitution
Agentic AI's real failure point isn't the model — it's the data pipeline. When agents act autonomously on corrupted data, output guardrails can't save you. Your data needs a constitution, not better prompts.
