Agents

I've written about how agents need supervision frameworks that match their autonomy level, how privacy law struggles when agents operate persisten…

AI generates contract provisions faster than you can review them. Creation isn't the bottleneck.

The worst case: prompt injection tricks your agent into handing over its own credentials. Attackers bypass the AI entirely and access your systems with the agent's full authority.

Agents give you power—the autonomy and flexibility to handle ambiguous or dynamic tasks. Workflows give you control—the structure, reliability, and traceability you need for predictable, auditable processes.

Agents asking for too many permissions is bad. Fake servers stealing data is worse. But the real nightmare? Prompt injection that tricks your agent into handing over its own credentials.

AI agents can do real work or generate chaos. The difference isn't capability—it's human judgment.

The real constraint on agentic AI isn't model capability—it's governance infrastructure. Organizations treat agentic platforms as LLM deployment vehicles when they need complete enterprise systems with guardrails, evaluation layers, and audit mechanisms built in.

G2 data shows 60% of companies have AI agents in production with under 2% failure rates—contradicting MIT predictions of 95% project failure. For legal teams, this means governance frameworks can't wait for academic consensus when systems are already deployed.