\section{Tool Support for OPT-Aware Agentic Systems}
\label{sec:opt-agent-tooling}

While OPT is fundamentally a conceptual taxonomy, its utility is enhanced by
tooling that supports classification, verification, and alignment analysis.
In agentic AI systems, such tooling enables partial automation of mechanism-
aware reasoning and governance.

\subsection{OPT Classification and Verification Tools}

Automated classifiers may infer OPT--Code from source code, architectural
descriptions, or execution traces. Verification tools can then assess syntactic
validity, semantic consistency, and completeness of OPT--Code expressions.
These tools support both static analysis and runtime introspection.

\subsection{OPT--Intent and Alignment Evaluation}

OPT--Intent declarations provide a reference against which agent behavior can
be evaluated. Tooling that compares OPT--Intent with observed OPT--Code enables
the detection of mechanism drift and unplanned changes in operative premises.
Such comparisons are particularly valuable in long-running or self-modifying
agentic systems.

\subsection{LLM-Supported Reasoning}

Large language models can assist in OPT classification, intent proposal, and
alignment evaluation when guided by structured prompts. Importantly, OPT
constrains these models to reason explicitly about operative mechanisms, reducing
the risk of category errors and unexamined defaults.

\subsection{Integration into Agentic Workflows}

OPT-aware tools may be invoked as part of planning, evaluation, or repair
phases in agentic workflows. By exposing mechanism-level information to the
agent, these tools enable more disciplined planning, more targeted remediation,
and more transparent reporting.

\subsection{Governance and Auditability}

Finally, OPT tooling supports governance by producing durable, machine-readable
records of mechanism choices and changes over time. These records can be used
for internal review, external audit, or regulatory compliance without requiring
access to proprietary model internals.