Agents on Rails
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.
Here's the thesis buried in this Forbes piece: the real constraint on agentic AI isn't model capability—it's governance infrastructure. Organizations treat agentic platforms as LLM deployment vehicles, when they should be thinking about complete enterprise systems with guardrails, evaluation layers, and audit mechanisms built in.
The author frames this through a trust problem. Probabilistic systems produce different outputs from identical inputs, which makes traditional testing approaches fall apart. Operations teams worry agents will behave differently in production than in testing. Security teams fear probabilistic variation might occasionally violate policies. That uncertainty blocks deployment.
The solution—persona-based guardrails—addresses what I've written about before with adaptable governance: you need control mechanisms that account for autonomous reasoning. Each persona includes permissions, action boundaries, data handling rules, escalation protocols, and temporal constraints. That's your governance stack for agents that can plan and execute independently.
What matters for product and legal teams is the recognition that prompt templates alone won't solve this. They add some determinism to probabilistic models, but you still need observability, evaluation agents validating decisions before execution, and human-in-the-loop for high-risk actions.
Organizations that build this infrastructure before scaling can deploy faster with fewer compliance incidents. Trust becomes the limiting factor for AI operations—and trust requires platform maturity, not just better models.
Shailesh Manjrekar
