diff --git a/paper/taxonomy-refs.bib b/paper/taxonomy-refs.bib
index a4bc109..a6d3753 100644
--- a/paper/taxonomy-refs.bib
+++ b/paper/taxonomy-refs.bib
@@ -40,6 +40,56 @@
   url={https://github.com/XORwell/archeflow}
 }
 
+@article{nguyen2024agilecoder,
+  title={AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology},
+  author={Nguyen, Minh Huynh and Chau, Thang Phan and Phung, Phong X. and Nguyen, Nghi D. Q.},
+  journal={arXiv preprint arXiv:2406.11912},
+  year={2024},
+  url={https://arxiv.org/abs/2406.11912}
+}
+
+@article{patel2026sixsigma,
+  title={The Six Sigma Agent: Achieving Enterprise-Grade Reliability in LLM Systems Through Consensus-Driven Decomposed Execution},
+  author={Patel, Rushi and Surendira, Bala and George, Allen and Kapale, Kiran},
+  journal={arXiv preprint arXiv:2601.22290},
+  year={2026},
+  url={https://arxiv.org/abs/2601.22290}
+}
+
+@article{shinn2023reflexion,
+  title={Reflexion: Language Agents with Verbal Reinforcement Learning},
+  author={Shinn, Noah and Cassano, Federico and Gopinath, Ashwin and Narasimhan, Karthik and Yao, Shunyu},
+  journal={Advances in Neural Information Processing Systems},
+  volume={36},
+  year={2023},
+  url={https://arxiv.org/abs/2303.11366}
+}
+
+@article{xia2024eddops,
+  title={Evaluation-Driven Development and Operations of LLM Agents: A Process Model and Reference Architecture},
+  author={Xia, Boming and Lu, Qinghua and Zhu, Liming and Xing, Zhenchang and Zhao, Dehai and Zhang, Hao},
+  journal={arXiv preprint arXiv:2411.13768},
+  year={2024},
+  url={https://arxiv.org/abs/2411.13768}
+}
+
+@article{rasheed2024survey,
+  title={LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead},
+  author={Rasheed, Zeeshan and others},
+  journal={ACM Transactions on Software Engineering and Methodology},
+  year={2025},
+  url={https://arxiv.org/abs/2404.04834}
+}
+
+@article{li2023camel,
+  title={CAMEL: Communicative Agents for ``Mind'' Exploration of Large Language Model Society},
+  author={Li, Guohao and Hammoud, Hasan Abed Al Kader and Itani, Hani and Khizbullin, Dmitrii and Ghanem, Bernard},
+  journal={Advances in Neural Information Processing Systems},
+  volume={36},
+  year={2023},
+  url={https://arxiv.org/abs/2303.17760}
+}
+
 % ---- Persona Stability ----
 
 @article{lu2026assistant,
diff --git a/paper/taxonomy.tex b/paper/taxonomy.tex
index d8d92c3..4ed383d 100644
--- a/paper/taxonomy.tex
+++ b/paper/taxonomy.tex
@@ -141,6 +141,11 @@ 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{AgileCoder} \citep{nguyen2024agilecoder} is the first framework to
+explicitly adopt sprint-based iteration, assigning Scrum Master and Product
+Manager roles to LLM agents with a Dynamic Code Graph Generator tracking
+inter-file dependencies between sprints.
+
 \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.
@@ -150,6 +155,16 @@ 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{The Six Sigma Agent} \citep{patel2026sixsigma} decomposes tasks
+into atomic dependency trees, executes each node $n$ times with independent
+LLM samples, and uses consensus voting to achieve defect rates scaling as
+$O(p^{\lceil n/2 \rceil})$---reaching 3.4 DPMO (the Six Sigma threshold)
+at $n=13$.
+
+\textbf{Reflexion} \citep{shinn2023reflexion} implements a de facto PDCA
+loop through verbal reinforcement: Plan $\to$ Act $\to$ Evaluate (Check)
+$\to$ Reflect (Act), though it does not name this structure explicitly.
+
 \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
@@ -158,11 +173,17 @@ 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.
+cluster tightly around four approaches: (1) waterfall-style sequential
+phases (MetaGPT, ChatDev), (2) role-based team simulation (CAMEL
+\citep{li2023camel}, CrewAI), (3) informal ``manager'' delegation
+(AutoGen), and (4) agile sprints (AgileCoder). The Six Sigma Agent
+\citep{patel2026sixsigma} is a notable exception---the only framework to
+explicitly name a PM/OM method as its primary architectural contribution.
+
+Methods from lean manufacturing, constraint theory, military
+decision-making, innovation management, and failure analysis remain
+unexplored in the peer-reviewed agent orchestration literature, despite
+strong structural compatibility with agent constraints.
 
 % ============================================================
 \section{Taxonomy of PM/OM Methods}