3. Problem Statement¶

As AI agents become embedded in real workflows—from customer service bots to code assistants to financial analyzers—they increasingly operate beyond the bounds of predictable logic. Most rely on implicit rules, opaque memory states, and loosely scoped prompts. This creates an environment where small misalignments in design can lead to major security or compliance failures.

Key Challenges¶

• Scope Ambiguity: Agents often process inputs far beyond their intended domain, leading to silent overreach or data leaks.
• Lack of Auditability: When something goes wrong, teams struggle to reconstruct the agent’s decision-making path.
• Reactive Security: Most teams discover agent problems post-deployment, through user complaints or security incidents—not proactive validation.
• Behavioral Drift: Agents change behavior between versions, but without a plan boundary or changelog, it's difficult to tell what changed or why.

These problems aren’t theoretical. Microsoft’s whitepaper “A Taxonomy of Failure Modes in Agentic AI Systems” highlighted systemic gaps in how we validate agent behavior and enforce runtime trust boundaries.

Consequences¶

• Hard-to-debug errors in production environments.
• Loss of sensitive data through unintended context exposure.
• Erosion of user trust in autonomous or semi-autonomous systems.
• Delayed deployment cycles due to extended QA and security review loops.

A modern agent framework must move beyond hope-based governance. We need verifiable boundaries, pre-deployment safety checks, and transparent delegation trails—before agents touch real users or data.