\section{Conceptual Example: OPT in an Agentic Development Workflow}
\label{sec:opt-agent-example}

To illustrate the practical role of OPT in agentic AI systems, we consider a
particular scenario: an autonomous development agent tasked with constructing
and maintaining a production scheduling system under dynamic constraints.

\subsection{Step 1: Goal Intake and OPT--Intent Declaration}

The agent receives the following high-level goal:

\begin{quote}
Design a system to optimize production schedules under variable supply,
equipment downtime, and priority constraints.
\end{quote}

The agent proposes the following OPT--Intent:

\begin{quote}\ttfamily
INTENT-OPT = Sch/Sym \\
INTENT-GOAL = minimize-production-delay \\
INTENT-CONSTRAINTS = deterministic, explainable, real-time \\
INTENT-RISKS = combinatorial-explosion
\end{quote}

The agent explicitly selects search (\Sch) for combinatorial optimization and
symbolic reasoning (\Sym) for constraint enforcement, while avoiding learning
mechanisms to preserve determinism and explainability.

\subsection{Step 2: Implementation and OPT--Code Observation}

During implementation, the agent integrates:
\begin{itemize}
  \item A heuristic search planner,
  \item A rule-based constraint validator,
  \item A neural network model for predicting machine failure.
\end{itemize}

The resulting OPT--Code is:

\begin{quote}\ttfamily
OPT = Sch/Sym/Lrn; \\
Rep = permutations + rules + predictive-model; \\
Time = iterative + online-adjust
\end{quote}

\subsection{Step 3: Drift Detection}

Comparison with OPT--Intent reveals mechanism drift:
\begin{itemize}
  \item \Lrn was introduced,
  \item Determinism constraint may no longer hold,
  \item Risk profile has changed.
\end{itemize}

The agent flags this deviation and evaluates whether predictive learning
violates declared governance constraints.

\subsection{Step 4: Mechanism-Guided Error}

Suppose the system exhibits unstable schedules under rare supply patterns.
Given the OPT--Code, the agent attributes the issue primarily to the
learning-based failure predictor (\Lrn), potentially due to distributional
shift.

\subsection{Step 5: Constraint-Preserving Repair}

The agent proposes two alternatives:

\begin{enumerate}
  \item Replace the neural predictor with symbolic failure rules (\Sym),
  \item Retain \Lrn but update OPT--Intent and governance constraints.
\end{enumerate}

The first option preserves the original intent. The second requires explicit
authorization.

\subsection{Step 6: Verified Repair}

If the symbolic replacement is adopted, the new OPT--Code becomes:

\begin{quote}\ttfamily
OPT = Sch/Sym
\end{quote}

Alignment with OPT--Intent is restored, and mechanism drift is resolved.

\subsection{Discussion}

This example illustrates how OPT provides:
\begin{itemize}
  \item Mechanism-aware planning,
  \item Explicit drift detection,
  \item Targeted error diagnosis,
  \item Governance-compatible repair,
  \item Structured traceability.
\end{itemize}

Importantly, OPT does not constrain the agent’s architecture. Instead, it
provides a stable abstraction layer that connects design commitments,
implementation choices, and remediation strategies.

