103 lines
3.1 KiB
TeX
Executable File
103 lines
3.1 KiB
TeX
Executable File
\section{Conceptual Example: OPT in an Agentic Development Workflow}
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\label{sec:opt-agent-example}
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To illustrate the practical role of OPT in agentic AI systems, we consider a
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particular scenario: an autonomous development agent tasked with constructing
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and maintaining a production scheduling system under dynamic constraints.
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\subsection{Step 1: Goal Intake and OPT--Intent Declaration}
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The agent receives the following high-level goal:
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\begin{quote}
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Design a system to optimize production schedules under variable supply,
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equipment downtime, and priority constraints.
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\end{quote}
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The agent proposes the following OPT--Intent:
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\begin{quote}\ttfamily
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INTENT-OPT = Sch/Sym \\
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INTENT-GOAL = minimize-production-delay \\
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INTENT-CONSTRAINTS = deterministic, explainable, real-time \\
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INTENT-RISKS = combinatorial-explosion
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\end{quote}
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The agent explicitly selects search (\Sch) for combinatorial optimization and
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symbolic reasoning (\Sym) for constraint enforcement, while avoiding learning
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mechanisms to preserve determinism and explainability.
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\subsection{Step 2: Implementation and OPT--Code Observation}
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During implementation, the agent integrates:
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\begin{itemize}
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\item A heuristic search planner,
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\item A rule-based constraint validator,
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\item A neural network model for predicting machine failure.
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\end{itemize}
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The resulting OPT--Code is:
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\begin{quote}\ttfamily
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OPT = Sch/Sym/Lrn; \\
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Rep = permutations + rules + predictive-model; \\
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Time = iterative + online-adjust
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\end{quote}
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\subsection{Step 3: Drift Detection}
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Comparison with OPT--Intent reveals mechanism drift:
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\begin{itemize}
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\item \Lrn was introduced,
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\item Determinism constraint may no longer hold,
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\item Risk profile has changed.
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\end{itemize}
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The agent flags this deviation and evaluates whether predictive learning
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violates declared governance constraints.
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\subsection{Step 4: Mechanism-Guided Error}
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Suppose the system exhibits unstable schedules under rare supply patterns.
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Given the OPT--Code, the agent attributes the issue primarily to the
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learning-based failure predictor (\Lrn), potentially due to distributional
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shift.
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\subsection{Step 5: Constraint-Preserving Repair}
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The agent proposes two alternatives:
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\begin{enumerate}
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\item Replace the neural predictor with symbolic failure rules (\Sym),
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\item Retain \Lrn but update OPT--Intent and governance constraints.
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\end{enumerate}
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The first option preserves the original intent. The second requires explicit
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authorization.
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\subsection{Step 6: Verified Repair}
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If the symbolic replacement is adopted, the new OPT--Code becomes:
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\begin{quote}\ttfamily
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OPT = Sch/Sym
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\end{quote}
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Alignment with OPT--Intent is restored, and mechanism drift is resolved.
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\subsection{Discussion}
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This example illustrates how OPT provides:
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\begin{itemize}
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\item Mechanism-aware planning,
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\item Explicit drift detection,
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\item Targeted error diagnosis,
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\item Governance-compatible repair,
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\item Structured traceability.
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\end{itemize}
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Importantly, OPT does not constrain the agent’s architecture. Instead, it
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provides a stable abstraction layer that connects design commitments,
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implementation choices, and remediation strategies.
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