Field note
The execution layer is the security boundary for AI agents
AI agent security changes when agents move from conversation into action. The boundary is no longer only the prompt. It is the execution layer.
Agents change risk when they can act
A chat assistant can produce a bad answer. An agent connected to tools can call APIs, query data, update records, trigger workflows, and affect customer systems. That changes the review boundary.
Prompt injection remains important, but it becomes one input into a larger system risk: what the agent can do after it receives an instruction.
Prompt security is not enough
Production agents need review at the points where intent becomes execution. That means tools, permissions, data scopes, approvals, audit logs, and operational guardrails.
The question is not only whether the agent can be tricked. It is what a tricked, confused, or over-permissioned agent can reach.
Review the execution layer before production
A practical security review maps how the agent behaves, which MCP integrations and tools it can use, what data it can access, where human approval is required, and whether audit logs can explain what happened after the fact.
That is the path from opaque agent behavior to production readiness: make the execution surface visible, prioritize the risks, and tighten the controls before deployment.