2. Background and Motivation¶

AI agents are no longer just passive tools responding to prompts—they're becoming autonomous actors capable of making decisions, generating actions, and accessing sensitive systems. As this shift accelerates, so too does the urgency to ensure these agents are governed by clear, enforceable constraints.

The Problem: Ambiguity and Assumption¶

In today’s agent systems, most behavior is inferred from prompts or loosely defined goals. There are few guardrails to enforce what data an agent can see, what actions it can take, or how far its authority should reach. This ambiguity is dangerous.

The Microsoft white paper "Taxonomy of Failure Modes in Agentic AI Systems" highlights this systemic fragility. From scope creep to silent delegation errors, agents are already making decisions outside intended boundaries—often undetected until after harm occurs.

A Critical Shift: From Vibes to Verification¶

Without structured constraints, AI agents operate on implicit trust. That’s not sustainable. As agent use grows across enterprise, education, healthcare, and government, the need for verifiable delegation—where humans define what agents are allowed to do, and agents can prove they stayed within bounds—becomes non-negotiable.

Failures like EchoLeak underscore the cost of inaction. But the deeper truth is: most LLM agent failures don’t go viral. They silently leak, misroute, or hallucinate—undetected and unaudited.

Why Dokugent Exists¶

Dokugent was built as a direct response to these failures. It draws from lessons in software security, CI/CD, and DevSecOps to give AI systems the same rigor we apply to human-coded software. When agents can act, they must also be accountable.

Dokugent gives developers a way to pre-declare scope, simulate execution, verify boundaries, and cryptographically certify trusted plans—before an agent ever runs in production.