AI agents are becoming core to modern software. They're being embedded into products to automate workflows, assist users, and make decisions. But there's a problem: most implementations treat security as an afterthought.
This isn't just a technical oversight—it's a fundamental architectural mistake that becomes increasingly expensive to fix as systems scale.
The Problem with Bolted-On Security
When AI agents are added to existing systems, security typically follows one of two patterns:
- Pattern 1: Shared Credentials - Agents run with the same permissions as the application, accessing everything the app can access.
- Pattern 2: Manual Guards - Developers hand-code permission checks into agent logic, creating maintenance nightmares.
Both approaches fail at scale. The first creates massive security exposure. The second creates technical debt that compounds with every new feature.
What Security-First Means
A security-first architecture for AI agents means:
- Isolation by default - Each agent operates in its own trust zone with explicitly defined boundaries.
- Declarative permissions - What an agent can access is defined in configuration, not scattered through code.
- Automatic enforcement - The harness enforces permissions—developers don't need to remember to check.
- Auditable actions - Every agent action is logged with context for compliance and debugging.
How Lumina Approaches This
This is exactly why we built Teals Lumina as an agent harness rather than just an agent framework. The harness wraps agent execution with security controls that are enforced automatically.
With Lumina, you define what an agent can access through declarative configuration. The harness handles enforcement. Your agents operate within trust zones that you define—and they physically cannot access resources outside those zones.
This isn't about adding security after the fact. It's about building on a foundation where security is structural—not optional.