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% ---- Title ----
\title{%
  From Factory Floor to Token Stream:\\
  A Taxonomy of Operations Management Methods\\
  for LLM Agent Orchestration%
}

\author{
  Christian Nennemann\\
  Independent Researcher\\
  \texttt{chris@nennemann.de}
}

\date{April 2026}

\begin{document}
\maketitle

% ============================================================
\begin{abstract}
Multi-agent systems built on large language models (LLMs) increasingly adopt
metaphors from human project management---sprints, standups, code review---yet
draw from a remarkably narrow slice of the operations management literature.
This paper presents a systematic taxonomy of twelve established PM/OM methods,
evaluates their structural compatibility with LLM agent constraints (stateless
invocation, cheap cloning, deterministic dysfunction, absence of human
psychology), and identifies which methods are underexploited, which are
inapplicable, and which require fundamental adaptation. We find that methods
designed for \emph{flow optimization} (Kanban, Theory of Constraints) and
\emph{rapid decision-making} (OODA Loop) are structurally well-suited to
agent orchestration but remain largely unexplored, while methods centered on
\emph{human psychology} (Scrum ceremonies, Design Thinking empathy phases)
transfer poorly without significant reformulation. We propose a decision
framework for selecting orchestration methods based on task complexity, agent
count, and quality requirements, and identify five open research directions
at the intersection of operations management and agentic AI.
\end{abstract}

% ============================================================
\section{Introduction}
\label{sec:intro}

The dominant paradigm for multi-agent LLM systems borrows from agile software
development: agents are organized into ``teams'' with role-based
specialization, tasks are decomposed into work items, and results are reviewed
before merging \citep{hong2024metagpt, qian2024chatdev}. This borrowing is
natural---the humans building these systems are software engineers familiar
with agile methods---but it is also narrow. The operations management
literature contains dozens of methods developed over a century of industrial
practice, each encoding different assumptions about workflow structure, quality
assurance, failure modes, and coordination costs.

Not all of these methods are equally applicable to LLM agents. Agents differ
from human workers in five structurally important ways:

\begin{enumerate}[label=\textbf{C\arabic*}]
  \item \label{c:stateless} \textbf{Stateless invocation}: Agents do not
  retain memory between invocations unless explicitly persisted. Human team
  members accumulate institutional knowledge automatically.

  \item \label{c:cloning} \textbf{Cheap to clone, expensive to coordinate}:
  Spawning a new agent costs milliseconds and cents; coordinating two agents
  costs tokens and latency. For human teams, the inverse holds---hiring is
  expensive, coordination is (comparatively) cheap.

  \item \label{c:dysfunction} \textbf{Deterministic dysfunction}: LLM agents
  fail in predictable, repeatable patterns---verbosity, scope creep, false
  positives---rather than the varied, context-dependent failures of human
  cognition \citep{nennemann2026archeflow}.

  \item \label{c:psychology} \textbf{No psychology}: Agents have no morale,
  fatigue, ego, or office politics. Methods designed to manage human
  psychology (retrospectives, team-building, conflict resolution) have no
  direct function.

  \item \label{c:speed} \textbf{Cycle speed}: Agents complete tasks in
  seconds to minutes, enabling iteration frequencies that would be
  impractical for human teams. Methods that assume week-long or month-long
  cycles can be compressed.
\end{enumerate}

These constraints define a \emph{fitness landscape}: some PM/OM methods gain
effectiveness when applied to agents (because agents remove friction those
methods were designed to manage), while others lose their raison d'\^etre
(because they solve human problems agents don't have).

This paper contributes:
\begin{itemize}
  \item A systematic taxonomy of twelve PM/OM methods evaluated against the
  five agent constraints (\ref{c:stateless}--\ref{c:speed}).
  \item A compatibility matrix scoring each method's structural fit for
  agent orchestration (\S\ref{sec:matrix}).
  \item A decision framework for practitioners selecting orchestration
  strategies (\S\ref{sec:decision}).
  \item Five open research directions at the intersection of operations
  management theory and agentic AI (\S\ref{sec:future}).
\end{itemize}

% ============================================================
\section{Background: Current Agent Orchestration Landscape}
\label{sec:background}

\subsection{Frameworks and Their Implicit PM Models}

The current generation of multi-agent LLM frameworks implicitly adopts
project management concepts, though rarely with explicit attribution to
PM/OM theory.

\textbf{MetaGPT} \citep{hong2024metagpt} assigns human job titles (product
manager, architect, engineer) and enforces communication through Standardized
Operating Procedures (SOPs)---an implicit adoption of \emph{waterfall}
phase gates with role-based access control.

\textbf{ChatDev} \citep{qian2024chatdev} simulates a software company with
sequential phases (design, coding, testing, documentation). Despite the
``company'' framing, the execution model is a \emph{linear pipeline} with
pair-programming-style chat between adjacent roles.

\textbf{CrewAI} organizes agents into ``crews'' with a ``manager'' agent
orchestrating task delegation---an implicit \emph{hierarchical management}
model with single-point-of-failure coordination.

\textbf{AutoGen} \citep{wu2023autogen} provides a conversation-based
framework where agents negotiate through multi-turn dialogue. The implicit
model is \emph{committee decision-making}---all agents see all messages,
consensus emerges through discussion.

\textbf{ArcheFlow} \citep{nennemann2026archeflow} explicitly applies PDCA
quality cycles with Jungian archetypal roles, representing the first
framework to deliberately adopt a named PM/OM methodology with formal
convergence criteria.

\subsection{The Gap}

Despite the variety of frameworks, the PM/OM methods actually employed
cluster tightly around three approaches: (1) waterfall-style sequential
phases, (2) role-based team simulation, and (3) informal ``manager''
delegation. Methods from lean manufacturing, statistical process control,
military decision-making, innovation management, and constraint theory
remain entirely unexplored in the agent orchestration literature.

% ============================================================
\section{Taxonomy of PM/OM Methods}
\label{sec:taxonomy}

We evaluate twelve methods spanning five categories: iterative improvement,
flow optimization, decision-making, innovation management, and quality
engineering. For each method, we describe the core mechanism, evaluate
structural compatibility with agent constraints \ref{c:stateless}--\ref{c:speed},
identify the primary adaptation required, and assess overall fitness.

% ---- 3.1 Iterative Improvement ----
\subsection{Iterative Improvement Methods}

\subsubsection{PDCA (Plan--Do--Check--Act)}
\label{sec:pdca}

\textbf{Origin}: Shewhart \citep{shewhart1939statistical}, popularized by
Deming \citep{deming1986out}.

\textbf{Mechanism}: Four-phase cycle repeated until quality targets are met.
Each cycle narrows the gap between current and desired state through
structured feedback.

\textbf{Agent fitness}: \textsc{High}. PDCA's phase structure maps directly
to agent orchestration: Plan (research + design agents), Do (implementation
agent), Check (review agents), Act (routing + merge decisions). The cycle
abstraction handles the core challenge of ``when to stop iterating'' through
convergence metrics. Demonstrated in ArcheFlow \citep{nennemann2026archeflow}.

\textbf{Key adaptation}: Convergence detection must be automated (human PDCA
relies on subjective judgment). ArcheFlow addresses this with a convergence
score based on finding classification (new, resolved, persistent, regressed)
and oscillation detection.

\textbf{Constraint fit}: Stateless (\ref{c:stateless})---artifacts persist
state between cycles. Cloning (\ref{c:cloning})---fresh agents per cycle
avoid accumulated bias. Speed (\ref{c:speed})---cycles complete in minutes,
enabling 2--3 cycles where humans would manage one.

\subsubsection{Scrum}
\label{sec:scrum}

\textbf{Origin}: Schwaber \& Sutherland, 1995.

\textbf{Mechanism}: Time-boxed sprints with defined roles (Product Owner,
Scrum Master, Development Team), ceremonies (planning, daily standup,
review, retrospective), and artifacts (backlog, sprint board, burndown).

\textbf{Agent fitness}: \textsc{Low--Medium}. Scrum's ceremony-heavy
structure exists primarily to manage human coordination challenges: standups
maintain shared awareness (agents can share a filesystem), retrospectives
address interpersonal friction (agents have none), sprint planning negotiates
capacity (agents have deterministic throughput). The useful kernel---time-boxed
work with a prioritized backlog---is trivially implementable without Scrum's
overhead.

\textbf{Key adaptation}: Strip ceremonies, keep the backlog + sprint
structure. ``Daily standups'' become status file reads. ``Retrospectives''
become cross-run memory extraction. The Scrum Master role is pure overhead
for agents.

\textbf{Constraint fit}: Psychology (\ref{c:psychology})---most Scrum
ceremonies solve human problems. Speed (\ref{c:speed})---sprint length
compresses from weeks to minutes. Cloning (\ref{c:cloning})---team
stability (a Scrum value) is irrelevant when agents are stateless.

\subsubsection{DMAIC (Six Sigma)}
\label{sec:dmaic}

\textbf{Origin}: Motorola, 1986; systematized by General Electric.

\textbf{Mechanism}: Define--Measure--Analyze--Improve--Control. Unlike PDCA,
DMAIC emphasizes \emph{statistical measurement} of process capability and
explicitly separates analysis (understanding the problem) from improvement
(fixing it).

\textbf{Agent fitness}: \textsc{Medium--High}. The Define--Measure--Analyze
front-loading is valuable for agents: it forces explicit quality metrics
\emph{before} implementation, preventing the common failure mode of agents
optimizing for the wrong objective. The Control phase---establishing
monitoring to prevent regression---maps to cross-run memory systems.

\textbf{Key adaptation}: Agents can compute statistical process control
metrics (defect rates, cycle times, sigma levels) automatically from event
logs. The ``Measure'' phase, which is expensive and tedious for humans,
becomes a strength: agents can instrument everything.

\textbf{Constraint fit}: Speed (\ref{c:speed})---full DMAIC in minutes.
Dysfunction (\ref{c:dysfunction})---agent failure modes have measurable
baselines, making sigma calculations meaningful. Stateless
(\ref{c:stateless})---Control phase requires persistent monitoring, which
must be explicitly built.

% ---- 3.2 Flow Optimization ----
\subsection{Flow Optimization Methods}

\subsubsection{Kanban}
\label{sec:kanban}

\textbf{Origin}: Toyota Production System, Taiichi Ohno, 1950s.

\textbf{Mechanism}: Pull-based workflow with explicit work-in-progress (WIP)
limits. Work items flow through columns (stages); new work is pulled only
when capacity is available. No iterations---continuous flow.

\textbf{Agent fitness}: \textsc{High}. Kanban's WIP limits directly address
a critical agent challenge: \emph{coordination cost scaling}. Without WIP
limits, spawning more agents increases throughput initially but eventually
degrades quality due to coordination overhead (conflicting changes, merge
conflicts, context fragmentation). Kanban provides a principled mechanism for
determining optimal concurrency.

\textbf{Key adaptation}: WIP limits should be \emph{dynamic}, adjusting
based on observed coordination costs (merge conflicts, finding duplications)
rather than fixed. The pull mechanism maps naturally: agents poll a task
queue and pull the highest-priority item they can handle.

\textbf{Constraint fit}: Cloning (\ref{c:cloning})---WIP limits are
\emph{exactly} the missing constraint for cheap-to-clone agents. Speed
(\ref{c:speed})---flow metrics (lead time, cycle time, throughput) update
in real-time. Psychology (\ref{c:psychology})---no ``swarming'' or
``blocked item'' social dynamics to manage.

\subsubsection{Theory of Constraints (TOC)}
\label{sec:toc}

\textbf{Origin}: Goldratt, \emph{The Goal}, 1984.

\textbf{Mechanism}: Identify the system's constraint (bottleneck), exploit
it (maximize its throughput), subordinate everything else to it, elevate it
(invest to remove it), repeat. The Five Focusing Steps.

\textbf{Agent fitness}: \textsc{High}. In multi-agent pipelines, the
bottleneck is typically the most capable (and expensive) agent: the
implementation agent that must run on a powerful model, or the security
reviewer that requires deep context. TOC provides a framework for
organizing the entire pipeline around this constraint.

\textbf{Key adaptation}: ``Exploit the constraint'' means ensuring the
bottleneck agent never waits for input. Pre-compute its context, batch
its inputs, and schedule cheaper agents (research, formatting, validation)
to run during its processing time. ``Subordinate'' means cheaper agents
should produce output in the format the bottleneck needs, not in whatever
format is easiest for them.

\textbf{Constraint fit}: Cloning (\ref{c:cloning})---non-bottleneck agents
are cheap to overprovision. Speed (\ref{c:speed})---constraint shifts can
be detected and responded to within a single run. Dysfunction
(\ref{c:dysfunction})---bottleneck agent's failure mode has outsized impact,
justifying targeted shadow detection.

\subsubsection{Lean / Toyota Production System}
\label{sec:lean}

\textbf{Origin}: Ohno, 1988; Womack \& Jones, 1996.

\textbf{Mechanism}: Eliminate waste (\emph{muda}), reduce variability
(\emph{mura}), avoid overburden (\emph{muri}). Seven wastes: overproduction,
waiting, transport, overprocessing, inventory, motion, defects.

\textbf{Agent fitness}: \textsc{Medium--High}. The seven wastes map
surprisingly well to agent systems:

\begin{itemize}[nosep]
  \item \textbf{Overproduction}: Agents generating output nobody reads
  (verbose research reports, unused alternative proposals).
  \item \textbf{Waiting}: Agents idle while waiting for predecessor output
  (sequential pipeline where parallel would work).
  \item \textbf{Transport}: Redundant context passing (sending full codebase
  to agents that need only a diff).
  \item \textbf{Overprocessing}: Running thorough review on trivial changes.
  \item \textbf{Inventory}: Accumulated artifacts from prior cycles that
  are never referenced.
  \item \textbf{Motion}: Agents reading files they don't need, exploring
  irrelevant code paths.
  \item \textbf{Defects}: Findings that are false positives, requiring
  rework to dismiss.
\end{itemize}

\textbf{Key adaptation}: Lean's ``respect for people'' pillar has no direct
analog. The technical pillar (continuous improvement, waste elimination)
transfers fully.

% ---- 3.3 Decision-Making ----
\subsection{Decision-Making Methods}

\subsubsection{OODA Loop (Observe--Orient--Decide--Act)}
\label{sec:ooda}

\textbf{Origin}: John Boyd, 1976. Military strategy for air combat; later
generalized to competitive decision-making.

\textbf{Mechanism}: Continuous loop of Observe (gather data), Orient (analyze
context, update mental models), Decide (select course of action), Act
(execute). The key insight is that the \emph{speed} of the loop---not any
individual decision's quality---determines competitive advantage. ``Getting
inside the opponent's OODA loop'' means acting faster than the adversary can
react.

\textbf{Agent fitness}: \textsc{High}. OODA is structurally similar to PDCA
but optimized for speed over thoroughness. For agent systems, this maps to
scenarios requiring rapid adaptation: adversarial testing, incident response,
market-reactive coding, or any context where the problem space changes
during execution.

\textbf{Key adaptation}: Boyd's ``Orient'' phase---updating mental models
based on new information---is the hardest to implement for stateless agents.
It requires either persistent state (a world model that updates across
iterations) or a ``fast reorientation'' agent that rapidly synthesizes new
information into an updated context.

\textbf{Constraint fit}: Speed (\ref{c:speed})---agents can OODA at
superhuman frequency. Stateless (\ref{c:stateless})---the Orient phase
needs explicit state management. Psychology (\ref{c:psychology})---Boyd's
concept of ``mental agility'' translates to model selection: smaller, faster
models for rapid OODA; larger models for deep Orient phases.

\subsubsection{Cynefin Framework}
\label{sec:cynefin}

\textbf{Origin}: Snowden \& Boone, 2007.

\textbf{Mechanism}: Classify problems into five domains---\textsc{Clear}
(obvious cause-effect), \textsc{Complicated} (expert analysis needed),
\textsc{Complex} (emergent, probe-sense-respond), \textsc{Chaotic}
(act first, then sense), \textsc{Confused} (unknown domain)---and apply
domain-appropriate strategies.

\textbf{Agent fitness}: \textsc{Medium--High}. Cynefin provides a
\emph{meta-framework}: instead of choosing one orchestration method for all
tasks, classify the task first, then select the appropriate method:

\begin{itemize}[nosep]
  \item \textsc{Clear}: Single agent, no review (``fix this typo'').
  \item \textsc{Complicated}: Expert agent with review (PDCA fast workflow).
  \item \textsc{Complex}: Multiple competing proposals, let results emerge
  (PDCA standard/thorough with parallel alternatives).
  \item \textsc{Chaotic}: Act immediately, stabilize, then analyze (OODA
  with hotfix agent, then PDCA for proper fix).
\end{itemize}

\textbf{Key adaptation}: Task classification must be automated. Proxies:
number of files affected, cross-module dependencies, security sensitivity,
test coverage of affected area.

% ---- 3.4 Innovation Management ----
\subsection{Innovation Management Methods}

\subsubsection{Stage-Gate}
\label{sec:stagegate}

\textbf{Origin}: Cooper, 1990.

\textbf{Mechanism}: Innovation projects pass through stages (scoping,
business case, development, testing, launch), separated by gates where a
cross-functional team decides: Go, Kill, Hold, or Recycle. The gate
decision is binary---no ``continue with reservations.''

\textbf{Agent fitness}: \textsc{Medium}. The gate mechanism maps well to
agent confidence checks: a Creator agent's proposal either meets the
confidence threshold (Go) or doesn't (Kill/Recycle). However, Stage-Gate
assumes expensive stages (weeks/months of human work), making Kill decisions
high-stakes. For agents, stages are cheap (minutes), reducing the value of
formal gate decisions.

\textbf{Key adaptation}: Gates become lightweight confidence checks rather
than committee reviews. The ``Kill'' decision---rare and painful in human
innovation---should be common and cheap for agents. Explore multiple
proposals in parallel, gate aggressively, continue only the best.

\subsubsection{Design Thinking}
\label{sec:designthinking}

\textbf{Origin}: IDEO / Stanford d.school, 2000s.

\textbf{Mechanism}: Five phases: Empathize (understand the user),
Define (frame the problem), Ideate (generate solutions), Prototype (build
quickly), Test (get feedback). Emphasis on user empathy and divergent
thinking.

\textbf{Agent fitness}: \textsc{Low}. Design Thinking's core value
proposition---\emph{empathy with users}---is precisely what LLM agents
cannot genuinely do. Agents can simulate empathy (generate persona-based
scenarios), but the insight that comes from observing real users in context
has no agent equivalent. The Ideate phase (divergent brainstorming) is
feasible but produces quantity over quality without the ``empathy filter''
that makes Design Thinking effective.

\textbf{Key adaptation}: If used, the Empathize phase must be replaced
with explicit user research artifacts (personas, journey maps, interview
transcripts) provided as input. This transforms Design Thinking from a
discovery method into a synthesis method---fundamentally changing its nature.

\subsubsection{TRIZ}
\label{sec:triz}

\textbf{Origin}: Altshuller, 1946--1985. Theory of Inventive Problem
Solving.

\textbf{Mechanism}: Problems contain contradictions (improving one parameter
worsens another). TRIZ provides a contradiction matrix mapping 39 engineering
parameters to 40 inventive principles. Instead of compromise, TRIZ seeks
solutions that resolve the contradiction.

\textbf{Agent fitness}: \textsc{Medium}. TRIZ's structured problem-solving
is well-suited to agents: the contradiction matrix is a lookup table, and
agents can systematically apply inventive principles. However, TRIZ requires
\emph{reformulating the problem as a contradiction}---a creative step that
is itself challenging for agents.

\textbf{Key adaptation}: Provide the contradiction matrix as context. Train
agents to identify the ``improving parameter'' and ``worsening parameter''
in engineering tasks (e.g., ``improving security worsens performance'').
Use TRIZ principles as a structured brainstorming prompt for the Creator
archetype.

% ---- 3.5 Quality Engineering ----
\subsection{Quality Engineering Methods}

\subsubsection{FMEA (Failure Mode and Effects Analysis)}
\label{sec:fmea}

\textbf{Origin}: US Military, 1949; adopted by automotive (AIAG) and
aerospace.

\textbf{Mechanism}: For each component/process step, systematically
enumerate: (1) potential failure modes, (2) effects of each failure,
(3) causes, (4) current controls, (5) risk priority number
(severity $\times$ occurrence $\times$ detection). Address highest-RPN
items first.

\textbf{Agent fitness}: \textsc{High}. FMEA's systematic enumeration is
exactly what LLM agents excel at: given a design, enumerate everything that
could go wrong, assess severity, and propose mitigations. The Risk Priority
Number provides a quantitative framework for prioritizing review effort---more
principled than the common ``CRITICAL/WARNING/INFO'' severity classification.

\textbf{Key adaptation}: Use FMEA \emph{before} implementation (as part of
the Plan phase) rather than only during review. An FMEA agent analyzes the
Creator's proposal and generates a failure mode table; the Maker then
implements with awareness of high-RPN failure modes; the Guardian validates
that mitigations are in place.

\textbf{Constraint fit}: Dysfunction (\ref{c:dysfunction})---agents' own
failure modes can be pre-enumerated via FMEA, creating a meta-level
quality system. Cloning (\ref{c:cloning})---FMEA agents are cheap
(analytical, not creative), enabling systematic coverage.

\subsubsection{Statistical Process Control (SPC)}
\label{sec:spc}

\textbf{Origin}: Shewhart, 1920s.

\textbf{Mechanism}: Monitor process outputs over time using control charts.
Distinguish \emph{common cause} variation (inherent to the process) from
\emph{special cause} variation (attributable to specific events). React only
to special causes; reduce common cause variation through process improvement.

\textbf{Agent fitness}: \textsc{Medium--High}. SPC requires historical data,
which agent orchestration systems naturally generate (event logs, finding
counts, cycle times, token usage). Control charts over agent effectiveness
scores can distinguish between normal variation (``Guardian found 2 issues
this run vs. 1 last run'') and genuine degradation (``Guardian's false
positive rate spiked after a model update'').

\textbf{Key adaptation}: Sufficient run history is needed to establish
control limits. Early runs operate without SPC; after 10--20 runs,
control limits become meaningful. Model updates reset control limits
(new process = new baseline).

% ============================================================
\section{Compatibility Matrix}
\label{sec:matrix}

Table~\ref{tab:matrix} scores each method against the five agent constraints,
producing an overall fitness assessment.

\begin{table}[t]
\centering
\small
\caption{Compatibility matrix: PM/OM methods scored against agent constraints.
\textcolor{highfit}{\textbf{+}} = method benefits from this constraint;
\textcolor{lowfit}{\textbf{--}} = method is undermined;
\textcolor{neutral}{\textbf{0}} = neutral.
Overall fitness: H = High, M = Medium, L = Low.}
\label{tab:matrix}
\begin{tabular}{@{}l*{5}{c}c@{}}
\toprule
\textbf{Method} &
\textbf{C1} &
\textbf{C2} &
\textbf{C3} &
\textbf{C4} &
\textbf{C5} &
\textbf{Fit} \\
\midrule
PDCA            & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textbf{H} \\
Scrum           & \textcolor{lowfit}{--}  & \textcolor{neutral}{0}  & \textcolor{neutral}{0}  & \textcolor{lowfit}{--}  & \textcolor{highfit}{+}  & \textbf{L--M} \\
DMAIC           & \textcolor{lowfit}{--}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textbf{M--H} \\
Kanban          & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textbf{H} \\
TOC             & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textbf{H} \\
Lean            & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{neutral}{0}  & \textcolor{lowfit}{--}  & \textcolor{highfit}{+}  & \textbf{M--H} \\
OODA            & \textcolor{lowfit}{--}  & \textcolor{highfit}{+}  & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textbf{H} \\
Cynefin         & \textcolor{neutral}{0}  & \textcolor{neutral}{0}  & \textcolor{neutral}{0}  & \textcolor{neutral}{0}  & \textcolor{neutral}{0}  & \textbf{M--H} \\
Stage-Gate      & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{lowfit}{--}  & \textbf{M} \\
Design Think.   & \textcolor{neutral}{0}  & \textcolor{neutral}{0}  & \textcolor{neutral}{0}  & \textcolor{lowfit}{--}  & \textcolor{neutral}{0}  & \textbf{L} \\
TRIZ            & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{neutral}{0}  & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textbf{M} \\
FMEA            & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textbf{H} \\
SPC             & \textcolor{lowfit}{--}  & \textcolor{neutral}{0}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textcolor{highfit}{+}  & \textbf{M--H} \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Analysis}

Several patterns emerge from the compatibility matrix:

\textbf{High-fitness methods share three properties}: they are
\emph{mechanistic} (decisions follow rules, not judgment), \emph{flow-oriented}
(optimize throughput, not team dynamics), and \emph{metric-driven} (quality
is quantified, not discussed). PDCA, Kanban, TOC, OODA, and FMEA all share
this profile.

\textbf{Low-fitness methods are psychology-dependent}: Scrum and Design
Thinking derive their primary value from managing human cognitive and social
limitations. Without those limitations, the methods become overhead.

\textbf{The ``Cheap Clone'' constraint is universally beneficial}: every
method either benefits from or is neutral to the ability to spawn agents
cheaply. This suggests that agent orchestration should generally favor
\emph{parallelism}---run multiple approaches simultaneously, then
select the best result.

\textbf{``Stateless'' is the most disruptive constraint}: methods that
assume accumulated knowledge (Scrum's team velocity, SPC's control charts,
DMAIC's baseline measurements) require explicit persistence mechanisms that
agents don't provide natively.

% ============================================================
\section{Hybrid Approaches and Method Composition}
\label{sec:hybrid}

The methods in our taxonomy are not mutually exclusive. Effective agent
orchestration likely requires combining methods at different levels:

\subsection{Proposed Three-Layer Architecture}

\begin{description}
  \item[Strategic layer (Cynefin)]: Classify the task and select the
  appropriate orchestration method. Simple tasks get a single agent;
  complicated tasks get PDCA; complex tasks get parallel competing
  approaches; chaotic tasks get OODA.

  \item[Operational layer (PDCA/OODA + Kanban)]: Execute the selected
  method with flow control. Kanban WIP limits prevent coordination
  overload. PDCA provides quality convergence for standard tasks; OODA
  provides rapid adaptation for time-sensitive tasks.

  \item[Quality layer (FMEA + SPC + TOC)]: Monitor execution quality.
  FMEA front-loads failure analysis in the Plan phase. SPC monitors
  long-term agent effectiveness trends. TOC identifies and optimizes
  around bottleneck agents.
\end{description}

\subsection{ArcheFlow as a Case Study}

ArcheFlow \citep{nennemann2026archeflow} already implements elements of
this three-layer architecture, though without explicitly naming all methods:

\begin{itemize}[nosep]
  \item \textbf{Strategic}: Workflow selection (fast/standard/thorough)
  functions as a simplified Cynefin classification.
  \item \textbf{Operational}: PDCA cycles with convergence detection;
  sprint mode with WIP-limited parallel dispatch (implicit Kanban).
  \item \textbf{Quality}: Shadow detection (behavioral FMEA for agent
  failure modes); effectiveness scoring (rudimentary SPC); Guardian
  fast-path (TOC---don't waste the bottleneck on clean code).
\end{itemize}

The gap is in explicit TOC application (identifying and optimizing around
the most expensive agent) and in OODA integration for time-sensitive tasks.

% ============================================================
\section{Decision Framework}
\label{sec:decision}

We propose a practitioner-oriented decision framework for selecting
orchestration methods based on three dimensions:

\begin{figure}[h]
\centering
\begin{tikzpicture}[
  box/.style={draw, rounded corners, minimum width=3.5cm, minimum height=0.7cm, font=\small, fill=#1},
  arrow/.style={-{Stealth[length=3mm]}, thick},
]

% Decision tree
\node[box=yellow!20] (start) {Task arrives};
\node[box=orange!15, below=0.8cm of start] (cynefin) {Classify (Cynefin)};

\node[box=green!15, below left=1cm and 2cm of cynefin] (clear) {Clear};
\node[box=green!15, below left=1cm and 0cm of cynefin] (complicated) {Complicated};
\node[box=blue!10, below right=1cm and 0cm of cynefin] (complex) {Complex};
\node[box=red!10, below right=1cm and 2cm of cynefin] (chaotic) {Chaotic};

\node[box=white, below=0.7cm of clear, text width=2.5cm, align=center, font=\scriptsize] (m1) {Single agent\\No review};
\node[box=white, below=0.7cm of complicated, text width=2.5cm, align=center, font=\scriptsize] (m2) {PDCA fast\\+ FMEA};
\node[box=white, below=0.7cm of complex, text width=2.5cm, align=center, font=\scriptsize] (m3) {PDCA thorough\\+ parallel proposals};
\node[box=white, below=0.7cm of chaotic, text width=2.5cm, align=center, font=\scriptsize] (m4) {OODA\\then PDCA};

\draw[arrow] (start) -- (cynefin);
\draw[arrow] (cynefin) -- (clear);
\draw[arrow] (cynefin) -- (complicated);
\draw[arrow] (cynefin) -- (complex);
\draw[arrow] (cynefin) -- (chaotic);
\draw[arrow] (clear) -- (m1);
\draw[arrow] (complicated) -- (m2);
\draw[arrow] (complex) -- (m3);
\draw[arrow] (chaotic) -- (m4);

\end{tikzpicture}
\caption{Decision framework for selecting agent orchestration method
based on Cynefin task classification.}
\label{fig:decision}
\end{figure}

\textbf{Cross-cutting concerns} apply regardless of classification:
\begin{itemize}[nosep]
  \item \textbf{Kanban WIP limits}: Always. Prevents coordination overload.
  \item \textbf{TOC awareness}: Identify the costliest agent; schedule
  others around it.
  \item \textbf{SPC monitoring}: After 10+ runs, establish control limits
  for agent effectiveness.
  \item \textbf{Lean waste audit}: Periodically review token usage patterns
  for waste (unused artifacts, redundant context, overprocessing).
\end{itemize}

% ============================================================
\section{Open Research Directions}
\label{sec:future}

\subsection{Adaptive Method Selection}

Current frameworks use a fixed orchestration method. An adaptive system
would classify each incoming task (Cynefin), select the appropriate method,
and switch methods mid-execution if the task's nature changes (e.g.,
a ``complicated'' task reveals unexpected complexity during exploration).
This requires a \emph{method-aware orchestrator} that understands the
assumptions and exit criteria of each method.

\subsection{Kanban for Agent Swarms}

As agent counts increase beyond 5--10, coordination costs dominate.
Kanban's WIP limits and flow metrics provide a theoretical basis for
determining optimal agent concurrency, but empirical studies are needed
to establish how coordination cost scales with agent count across
different task types and model capabilities.

\subsection{OODA for Adversarial Agent Scenarios}

Boyd's OODA loop was designed for competitive environments where speed of
decision-making determines the winner. Applications include adversarial
testing (red team agents vs. blue team agents), competitive code generation
(multiple agents racing to solve a problem), and incident response
(rapid diagnosis and mitigation under time pressure).

\subsection{Cross-Method Quality Metrics}

Each PM/OM method defines quality differently: PDCA uses convergence scores,
Six Sigma uses sigma levels, Lean uses waste ratios, SPC uses control
limits. A unified quality metric for agent orchestration---one that allows
meaningful comparison across methods---does not yet exist.

\subsection{FMEA for Agent Failure Modes}

Agent failure modes (hallucination, scope creep, false positive reviews,
persona drift \citep{lu2026assistant}) can be systematically enumerated
using FMEA methodology. A comprehensive FMEA catalog for LLM agents---with
severity, occurrence, and detection ratings calibrated from empirical
data---would provide a foundation for designing more robust orchestration
systems.

% ============================================================
\section{Conclusion}
\label{sec:conclusion}

The operations management literature offers a rich toolkit for agent
orchestration that extends far beyond the agile methods currently dominant
in the field. Our taxonomy reveals that the highest-fitness methods---PDCA,
Kanban, TOC, OODA, and FMEA---share a common profile: mechanistic,
flow-oriented, and metric-driven. Methods centered on human psychology
(Scrum, Design Thinking) transfer poorly without fundamental reformulation.

The key insight is that LLM agents are not ``fast humans.'' They have
fundamentally different constraint profiles---cheap to clone, expensive to
coordinate, stateless, psychologically inert---and these differences make
some PM/OM methods \emph{more} effective (OODA loops at superhuman speed,
FMEA with exhaustive enumeration) while rendering others irrelevant
(standups without psychology, retrospectives without learning).

We encourage the agent orchestration community to look beyond agile sprints
and role-playing frameworks toward the broader operations management
tradition. A century of industrial practice has much to teach us about
orchestrating intelligent agents---if we take the time to translate.

% ============================================================
\section*{Acknowledgments}

The author thanks the operations management and quality engineering
communities whose work, developed over decades for human organizations,
provides the theoretical foundation for this analysis.

% ============================================================
\bibliographystyle{plainnat}
\bibliography{taxonomy-refs}

\end{document}
