120 lines
6.5 KiB
TeX
Executable File
120 lines
6.5 KiB
TeX
Executable File
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\section{OPT and Agentic AI Workflows}
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\label{sec:opt-agentic}
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Recent advances in agentic artificial intelligence emphasize systems that
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plan, act, evaluate, and repair their own behavior through iterative
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interaction with tools, environments, and internal models. Such systems
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typically decompose goals, invoke tools, assess outcomes, and revise plans in
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closed loops. While these architectures have proven powerful, they frequently
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lack an explicit representation of the \emph{operative mechanisms} through
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which actions are taken and errors arise. This omission complicates reasoning
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about failure modes, governance constraints, and design trade-offs.
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The Operational Premise Taxonomy (OPT) provides a mechanism-level abstraction
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layer that can be integrated into agentic workflows to address these gaps.
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Rather than prescribing a particular agent architecture, OPT supplies a shared
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vocabulary and analytical framework that agentic systems can use to reason
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about how tasks are performed, how errors should be interpreted, and how
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repairs should be constrained.
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\subsection{Mechanism Awareness in Agentic Systems}
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Agentic workflows are often described in terms of high-level functional stages
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(planning, execution, critique, repair), but these stages are agnostic to the
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computational mechanisms employed. In practice, however, the behavior and risk
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profile of an agentic system depend critically on whether its actions rely on
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parametric learning (\Lrn), symbolic reasoning (\Sym), search (\Sch),
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probabilistic inference (\Prb), control (\Ctl), evolutionary adaptation (\Evo),
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or swarm dynamics (\Swm), or some hybrid combination thereof.
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OPT introduces explicit mechanism awareness into agentic reasoning. An
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OPT-aware agent can classify its own components, tools, or subplans in terms of
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OPT roots, enabling it to reason not merely about \emph{what} is being done, but
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about \emph{how} it is being done. This distinction becomes especially
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important in hybrid agentic systems that combine learning-based components with
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search, symbolic constraints, or control loops.
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\subsection{OPT--Intent in Agentic Planning}
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During goal intake and planning, agentic systems must decide not only which
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actions to take, but which classes of computational strategies are appropriate.
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OPT--Intent provides a compact way to express these design-time commitments.
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An OPT--Intent declaration specifies the intended operative mechanisms, the
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system’s goal, relevant constraints, and anticipated risks.
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In an agentic context, OPT--Intent functions as a planning constraint. It
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guides the selection of tools and strategies, discourages unprincipled
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mechanism substitution (e.g., defaulting to learning-based solutions when
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symbolic or search-based approaches are more appropriate), and provides an
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explicit reference against which subsequent behavior can be evaluated.
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\subsection{OPT--Code and Runtime Self-Description}
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As an agent executes plans and invokes tools, its effective operative
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mechanisms may diverge from those originally intended. OPT--Code provides a
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runtime or post-hoc description of the mechanisms actually employed. In
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agentic systems, this enables self-description and introspection: the agent
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can record and report which mechanisms were used to achieve a result.
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Comparing OPT--Code against OPT--Intent enables the detection of \emph{mechanism
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drift}, where new mechanisms are introduced implicitly or intended mechanisms
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are bypassed. This capability is particularly relevant for long-running or
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self-modifying agentic systems, where accumulated changes can undermine
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assumptions about safety, explainability, or compliance.
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\subsection{Mechanism-Guided Error Interpretation}
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A central challenge in agentic AI is automated error remediation. Errors in
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agentic systems are often diagnosed at the surface level (e.g., “the output was
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incorrect”), without regard to the underlying mechanism that produced the
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error. OPT enables mechanism-guided error interpretation by associating
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distinct classes of failure modes with different operative premises.
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For example, failures in \Lrn-dominated systems often involve generalization
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error or distributional shift, while failures in \Sch systems may involve
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heuristic bias or combinatorial explosion. Control-oriented systems (\Ctl) are
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prone to instability or oscillation, and evolutionary systems (\Evo) may suffer
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from premature convergence or loss of diversity. By classifying the operative
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mechanism, an agent can narrow the space of plausible diagnoses and select
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repair strategies that are appropriate to the mechanism in use.
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\subsection{Constraint-Preserving Repair and Governance}
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OPT also supports constraint-aware repair. In governance-sensitive contexts,
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repairs must not introduce new operative mechanisms without justification, as
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doing so may alter the system’s risk profile or regulatory status. An
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OPT-aware agent can evaluate proposed repairs against OPT--Intent to determine
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whether they preserve or violate intended constraints.
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This capability enables a form of \emph{mechanism-level governance} within
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agentic workflows. Rather than relying solely on external oversight, agents can
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self-monitor compliance with declared mechanism constraints, flag deviations,
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and require explicit authorization for changes that introduce new operative
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premises.
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\subsection{Multi-Agent Differentiation and Coordination}
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In multi-agent systems, OPT provides a principled basis for role differentiation
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and coordination. Agents may be specialized according to dominant operative
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mechanisms (e.g., search-focused agents, symbolic-reasoning agents, or
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learning-focused agents), reducing cognitive load and improving interpretability.
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OPT also provides a shared vocabulary for resolving conflicts when agents
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propose incompatible strategies, enabling negotiation in terms of mechanism
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trade-offs rather than ad hoc preferences.
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\subsection{Implications for Agentic AI Design}
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Incorporating OPT into agentic workflows does not require abandoning existing
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architectures. Instead, OPT functions as an intermediate abstraction layer that
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connects goals, mechanisms, and outcomes. By making operative premises explicit,
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OPT enhances planning discipline, improves error diagnosis, supports governance
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constraints, and provides a foundation for more transparent and accountable
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agentic AI systems.
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As agentic AI continues to move toward greater autonomy and complexity, the
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ability to reason explicitly about operative mechanisms will become
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increasingly important. OPT offers a structured and extensible framework for
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supporting this capability within both single-agent and multi-agent systems.
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