I. Agentic AI, what makes it legally different
A. Autonomy, persistence, and multi-step execution
B. Risk from tool integration and permissions
C. How "confirm before acting" can fail
II. Lessons from OpenClaw and Moltbook
A. Operational failure: action without meaningful confirmation, containment challenges
B. Content streams creating attacker-influencable input channels
C. Durable governance requirement
III. Cybersecurity and data security considerations
A. Agent identity, isolation, privileges, credential management
B. Data classification for risk control
C. Incident response planning and best practices
IV. Misrepresentation, fraud, and downstream harms
A. Hallucination-driven misrepresentation and resulting losses
B. Fraud vectors: agent-mediated installs, credential capture, and impersonation
C. Litigation and evidentiary issues: logs, attribution, and "who approved what"
V. Governance and oversight expectations
A. Role definition: legal, privacy/security, procurement, and product ownership
B. Approval gates and escalation triggers
C. Board and executive reporting, documenting decisions
VI. Contracting and deployment guardrails
A. Vendor diligence regarding model/agent scope and versioning, allowed data use, audit rights
B. Treating "skills"/extensions as part of supply chain management
C. Allocating risk: provisioning security obligations, indemnity, limitations, and incident cooperation
VII. Practitioner takeaways
