
\section{OPT and Agentic AI Workflows}
\label{sec:opt-agentic}

Recent advances in agentic artificial intelligence emphasize systems that
plan, act, evaluate, and repair their own behavior through iterative
interaction with tools, environments, and internal models. Such systems
typically decompose goals, invoke tools, assess outcomes, and revise plans in
closed loops. While these architectures have proven powerful, they frequently
lack an explicit representation of the \emph{operative mechanisms} through
which actions are taken and errors arise. This omission complicates reasoning
about failure modes, governance constraints, and design trade-offs.

The Operational Premise Taxonomy (OPT) provides a mechanism-level abstraction
layer that can be integrated into agentic workflows to address these gaps.
Rather than prescribing a particular agent architecture, OPT supplies a shared
vocabulary and analytical framework that agentic systems can use to reason
about how tasks are performed, how errors should be interpreted, and how
repairs should be constrained.

\subsection{Mechanism Awareness in Agentic Systems}

Agentic workflows are often described in terms of high-level functional stages
(planning, execution, critique, repair), but these stages are agnostic to the
computational mechanisms employed. In practice, however, the behavior and risk
profile of an agentic system depend critically on whether its actions rely on
parametric learning (\Lrn), symbolic reasoning (\Sym), search (\Sch),
probabilistic inference (\Prb), control (\Ctl), evolutionary adaptation (\Evo),
or swarm dynamics (\Swm), or some hybrid combination thereof.

OPT introduces explicit mechanism awareness into agentic reasoning. An
OPT-aware agent can classify its own components, tools, or subplans in terms of
OPT roots, enabling it to reason not merely about \emph{what} is being done, but
about \emph{how} it is being done. This distinction becomes especially
important in hybrid agentic systems that combine learning-based components with
search, symbolic constraints, or control loops.

\subsection{OPT--Intent in Agentic Planning}

During goal intake and planning, agentic systems must decide not only which
actions to take, but which classes of computational strategies are appropriate.
OPT--Intent provides a compact way to express these design-time commitments.
An OPT--Intent declaration specifies the intended operative mechanisms, the
system’s goal, relevant constraints, and anticipated risks.

In an agentic context, OPT--Intent functions as a planning constraint. It
guides the selection of tools and strategies, discourages unprincipled
mechanism substitution (e.g., defaulting to learning-based solutions when
symbolic or search-based approaches are more appropriate), and provides an
explicit reference against which subsequent behavior can be evaluated.

\subsection{OPT--Code and Runtime Self-Description}

As an agent executes plans and invokes tools, its effective operative
mechanisms may diverge from those originally intended. OPT--Code provides a
runtime or post-hoc description of the mechanisms actually employed. In
agentic systems, this enables self-description and introspection: the agent
can record and report which mechanisms were used to achieve a result.

Comparing OPT--Code against OPT--Intent enables the detection of \emph{mechanism
drift}, where new mechanisms are introduced implicitly or intended mechanisms
are bypassed. This capability is particularly relevant for long-running or
self-modifying agentic systems, where accumulated changes can undermine
assumptions about safety, explainability, or compliance.

\subsection{Mechanism-Guided Error Interpretation}

A central challenge in agentic AI is automated error remediation. Errors in
agentic systems are often diagnosed at the surface level (e.g., “the output was
incorrect”), without regard to the underlying mechanism that produced the
error. OPT enables mechanism-guided error interpretation by associating
distinct classes of failure modes with different operative premises.

For example, failures in \Lrn-dominated systems often involve generalization
error or distributional shift, while failures in \Sch systems may involve
heuristic bias or combinatorial explosion. Control-oriented systems (\Ctl) are
prone to instability or oscillation, and evolutionary systems (\Evo) may suffer
from premature convergence or loss of diversity. By classifying the operative
mechanism, an agent can narrow the space of plausible diagnoses and select
repair strategies that are appropriate to the mechanism in use.

\subsection{Constraint-Preserving Repair and Governance}

OPT also supports constraint-aware repair. In governance-sensitive contexts,
repairs must not introduce new operative mechanisms without justification, as
doing so may alter the system’s risk profile or regulatory status. An
OPT-aware agent can evaluate proposed repairs against OPT--Intent to determine
whether they preserve or violate intended constraints.

This capability enables a form of \emph{mechanism-level governance} within
agentic workflows. Rather than relying solely on external oversight, agents can
self-monitor compliance with declared mechanism constraints, flag deviations,
and require explicit authorization for changes that introduce new operative
premises.

\subsection{Multi-Agent Differentiation and Coordination}

In multi-agent systems, OPT provides a principled basis for role differentiation
and coordination. Agents may be specialized according to dominant operative
mechanisms (e.g., search-focused agents, symbolic-reasoning agents, or
learning-focused agents), reducing cognitive load and improving interpretability.
OPT also provides a shared vocabulary for resolving conflicts when agents
propose incompatible strategies, enabling negotiation in terms of mechanism
trade-offs rather than ad hoc preferences.

\subsection{Implications for Agentic AI Design}

Incorporating OPT into agentic workflows does not require abandoning existing
architectures. Instead, OPT functions as an intermediate abstraction layer that
connects goals, mechanisms, and outcomes. By making operative premises explicit,
OPT enhances planning discipline, improves error diagnosis, supports governance
constraints, and provides a foundation for more transparent and accountable
agentic AI systems.

As agentic AI continues to move toward greater autonomy and complexity, the
ability to reason explicitly about operative mechanisms will become
increasingly important. OPT offers a structured and extensible framework for
supporting this capability within both single-agent and multi-agent systems.

