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\newcommand{\ghbase}{https://github.com/agenticmail/agenticmail/blob/main/research/agent-rpc-vs-spawn}

\title{\Large\textbf{AgenticMail Call Agent vs.\ OpenClaw Sessions Spawn:\\A Comparative Analysis of Inter-Agent\\Communication Paradigms}}

\author{
    \textbf{Ope Olatunji}\textsuperscript{1} \quad \textbf{Fola (AI Research Assistant)}\textsuperscript{1}\\[4pt]
    \textsuperscript{1}AgenticMail / OpenClaw Research\\[2pt]
    \texttt{ope.olatunji@outlook.com}
}

\date{March 27, 2026 --- v4 (Side-by-Side Comparison)}

\begin{document}
\maketitle

\begin{abstract}
As multi-agent AI systems evolve from single-model architectures to orchestrated ensembles, the communication layer between agents becomes a critical design decision. This paper presents a \textbf{side-by-side empirical comparison} of two production inter-agent communication paradigms: (1)~\textbf{AgenticMail Call Agent} (\texttt{call\_agent}), a structured synchronous/async delegation pattern with three execution modes (\texttt{light}, \texttt{standard}, \texttt{full}), and (2)~\textbf{OpenClaw Sessions Spawn} (\texttt{sessions\_spawn}), a free-text sub-agent spawning pattern with full session isolation. Through \textbf{9 Call Agent tests} and \textbf{16 Sessions Spawn tests} across 10 industries, we evaluate both paradigms on latency, output quality, domain versatility, output structure, and collaboration capability. Our key finding overturns an earlier assumption: \textbf{Call Agent in standard/full mode produces deep, structured analysis comparable to Sessions Spawn}---not just shallow data retrieval. We additionally demonstrate \textbf{inter-agent email coordination} in Call Agent's full mode, where a finance agent autonomously emailed a writer agent. We conclude with a decision framework: use Call Agent for structured, composable outputs; use Sessions Spawn for autonomous specialist work requiring tool use and free-form reasoning. Full test results for both paradigms are provided in companion directories:
\href{\ghbase/results-callagent}{\texttt{results-callagent/}} and \href{\ghbase/results}{\texttt{results/}}.
\end{abstract}

\textbf{Keywords:} multi-agent systems, AgenticMail, OpenClaw, inter-agent communication, orchestration, AI agents, structured delegation, call\_agent, sessions\_spawn

\section{Introduction}

The rapid advancement of large language model (LLM) capabilities has given rise to agentic AI systems---autonomous agents equipped with tools, memory, and the ability to take actions in the world~\cite{yao2023react, wang2024survey}. Two production platforms for multi-agent orchestration are \textbf{AgenticMail}, a structured agent communication framework with typed task delegation, and \textbf{OpenClaw}, an open-source personal AI assistant platform supporting isolated sub-agent sessions.

Each platform offers a distinct paradigm for inter-agent communication. This paper tests both paradigms head-to-head across overlapping task domains to answer: \emph{when should you use AgenticMail Call Agent, and when should you use OpenClaw Sessions Spawn?}

\textbf{Prior versions} of this paper (v1--v3) tested Call Agent only in \texttt{light} mode, leading to a biased conclusion that it could only handle shallow data retrieval ($\sim$1\,s, structured JSON but no deep reasoning). \textbf{This version (v4)} tests Call Agent across all three modes---\texttt{light}, \texttt{standard}, and \texttt{full}---revealing that it produces \textbf{deep, structured analysis} on complex tasks, fundamentally changing the comparison.

We identify five critical axes of comparison:

\begin{enumerate}[itemsep=2pt,topsep=4pt]
    \item \textbf{Latency}---How quickly does the orchestrator receive results?
    \item \textbf{Output structure}---Can results be programmatically consumed?
    \item \textbf{Reasoning depth}---How well does each handle complex analysis?
    \item \textbf{Domain versatility}---Performance across diverse industries?
    \item \textbf{Multi-agent collaboration}---Can agents coordinate on larger problems?
\end{enumerate}

\section{The Two Paradigms}

\subsection{AgenticMail Call Agent}

The AgenticMail \texttt{call\_agent} function implements a \textbf{structured delegation} pattern. The orchestrating agent submits a task to a named subordinate agent and receives a structured JSON result.

\textbf{Three execution modes} control the subordinate's environment:

\begin{itemize}[itemsep=2pt,topsep=2pt]
    \item \texttt{mode="light"} --- No email, no memory, no workspace. Lowest latency. Ideal for data retrieval and lookups.
    \item \texttt{mode="standard"} --- Web search, tools, trimmed context. Balanced for analysis tasks. Supports sync and async execution.
    \item \texttt{mode="full"} --- All coordination features including inter-agent email. For multi-agent collaboration workflows.
\end{itemize}

\begin{lstlisting}
// Call Agent — standard mode (sync)
result = call_agent(
    target: "finance-agent",
    task: "Analyze AAPL stock",
    mode: "standard"
)
// Returns structured JSON:
// { status: "completed",
//   result: { price: "$248.80",
//     pe_ratio: 31.41,
//     recommendation: "Hold" ... } }
\end{lstlisting}

\textbf{Key characteristic:} Returns structured JSON regardless of task complexity. The orchestrator receives machine-parseable data and decides presentation.

\subsection{OpenClaw Sessions Spawn}

OpenClaw \texttt{sessions\_spawn} creates an \textbf{isolated sub-agent session} with a natural language prompt. The sub-agent has its own context, tools, and environment. Results arrive via push notification.

\begin{lstlisting}
// Sessions Spawn
sessions_spawn(
    task: "Analyze AAPL stock...",
    mode: "run", label: "finance"
)
sessions_yield() // yield turn
// Push: sub-agent completed
// Returns free-form markdown:
// "## AAPL Investment Brief
//  Current Price: $249.23 ...
//  Bull Case: AI monetization ..."
\end{lstlisting}

\textbf{Key characteristic:} Returns free-form natural language with expert reasoning, tool use traces, and autonomous decision-making.

\section{Methodology}

\subsection{Experimental Setup}

All tests ran on consumer hardware (Apple Mac mini, ARM64, macOS~25.2.0, Node.js~v25.5.0) with Claude Opus (Anthropic) as the underlying model for both paradigms.

\textbf{AgenticMail Call Agent:} 9 tests across light (1), standard (7, including 2 async), and full (1) modes.

\textbf{OpenClaw Sessions Spawn:} 16 tests executed in three parallel waves (5--6 concurrent sub-agents per wave).

\subsection{Overlapping Test Domains}

To enable direct comparison, we tested both paradigms on \textbf{7 overlapping domains}. Two additional domains were tested only with Call Agent (weather, which is trivial for Spawn) and 9 additional domains were tested only with Sessions Spawn (legal, creative, research, clinical trials, M\&A legal/ops).

\section{Results: Side-by-Side Comparison}

\subsection{Weather / Data Retrieval}

\begin{table}[H]
\centering
\caption{Weather: Charlotte, NC.}
\begin{tabular}{@{}p{2.2cm}cc@{}}
\toprule
\textbf{Metric} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Mode & \texttt{light} & (not tested) \\
Runtime & \win{18\,s} & --- \\
Output & \win{Structured JSON} & --- \\
Ref & \href{\ghbase/results-callagent/01-weather-light.md}{\texttt{CA-01}} & --- \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Verdict:} Call Agent is the obvious choice for data retrieval. Spawning a full sub-agent for weather data would be wasteful.

\subsection{Software Engineering: Code Review}

\begin{table}[H]
\centering
\caption{Python security code review.}
\begin{tabular}{@{}p{2.2cm}p{2.1cm}p{2.1cm}@{}}
\toprule
\textbf{Metric} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Mode & \texttt{standard} & \texttt{run} \\
Runtime & \lose{$\sim$100\,s} & \win{24\,s} \\
Issues found & \tie{9} & \tie{9} \\
Corrected code & \win{Full JSON} & Inline markdown \\
Machine-parse & \win{Yes} & \lose{No} \\
Ref & \href{\ghbase/results-callagent/02-code-review-standard.md}{\texttt{CA-02}} & \href{\ghbase/results/01-code-review.md}{\texttt{SS-01}} \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Verdict:} Spawn was 4$\times$ faster here. Both found 9 issues with similar severity breakdown. Call Agent's advantage is structured output (CWE codes, line numbers, severity as fields) that can feed into CI/CD pipelines. Spawn's output reads better as a human-facing report.

\subsection{Finance: AAPL Stock Analysis}

\begin{table}[H]
\centering
\caption{AAPL investment analysis.}
\begin{tabular}{@{}p{2.2cm}p{2.1cm}p{2.1cm}@{}}
\toprule
\textbf{Metric} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Mode & \texttt{standard} & \texttt{run} \\
Runtime & \lose{$\sim$130\,s} & \win{36\,s} \\
Price data & \$248.80 & \$249.23 \\
PE ratio & 31.41 & 31.2 \\
Bull/bear cases & \tie{Yes} & \tie{Yes} \\
Analyst consensus & \win{42 analysts} & General \\
Technical levels & \win{Support/resist.} & Not included \\
Machine-parse & \win{Yes} & \lose{No} \\
Ref & \href{\ghbase/results-callagent/03-finance-aapl-standard.md}{\texttt{CA-03}} & \href{\ghbase/results/02-finance-aapl.md}{\texttt{SS-02}} \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Verdict:} Call Agent was slower (web search overhead) but produced more data points including analyst counts and technical levels. Spawn was faster and wrote a more readable narrative. For a dashboard: Call Agent. For a client brief: Spawn.

\subsection{Healthcare: Drug Interactions}

\begin{table}[H]
\centering
\caption{Drug interaction analysis (Metformin / Lisinopril / Atorvastatin).}
\begin{tabular}{@{}p{2.2cm}p{2.1cm}p{2.1cm}@{}}
\toprule
\textbf{Metric} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Mode & \texttt{standard} & \texttt{run} \\
Runtime & \lose{$\sim$96\,s} & \win{72\,s} \\
Pairs analyzed & \tie{3} & \tie{3} \\
Severity ratings & \win{Per-pair} & Narrative \\
Mechanisms & \tie{Detailed} & \tie{Detailed} \\
Monitoring plans & \tie{Yes} & \tie{Yes} \\
FDA alerts & \win{4 with dates} & General \\
Machine-parse & \win{Yes} & \lose{No} \\
Ref & \href{\ghbase/results-callagent/04-healthcare-drugs-standard.md}{\texttt{CA-04}} & \href{\ghbase/results/06-healthcare-drugs.md}{\texttt{SS-06}} \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Verdict:} Both produced comprehensive drug interaction reports. Call Agent structured each pair with explicit severity fields and included dated FDA alerts---better for clinical decision support systems. Spawn wrote a more readable clinical narrative. Quality is comparable; the difference is format.

\subsection{Marketing: Gen Z Campaign}

\begin{table}[H]
\centering
\caption{Gen Z budgeting app launch campaign.}
\begin{tabular}{@{}p{2.2cm}p{2.1cm}p{2.1cm}@{}}
\toprule
\textbf{Metric} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Mode & \texttt{standard} & \texttt{run} \\
Runtime & \win{$\sim$48\,s} & \lose{88\,s} \\
Persona & \tie{Yes} & \tie{Yes} \\
Msg.\ pillars & \tie{4} & \tie{4} \\
Channels & \tie{6} & \tie{6} \\
CAC projections & \win{Per-channel} & General \\
Budget alloc. & \tie{Detailed} & \tie{Detailed} \\
Week-1 content & \win{Per-channel} & Summary \\
Machine-parse & \win{Yes} & \lose{No} \\
Ref & \href{\ghbase/results-callagent/05-marketing-campaign-standard.md}{\texttt{CA-05}} & \href{\ghbase/results/16-marketing-campaign.md}{\texttt{SS-16}} \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Verdict:} Call Agent was faster \emph{and} more detailed here---per-channel CAC projections, specific week-1 content for each channel. This is a case where structured JSON contains more actionable data than the free-form output. Call Agent wins on both speed and substance.

\subsection{Supply Chain: Shenzhen to Charlotte}

\begin{table}[H]
\centering
\caption{Shipping route optimization.}
\begin{tabular}{@{}p{2.2cm}p{2.1cm}p{2.1cm}@{}}
\toprule
\textbf{Metric} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Mode & \texttt{standard} & \texttt{run} \\
Runtime & \lose{$\sim$141\,s} & \win{62\,s} \\
Routes compared & \tie{3} & \tie{3} \\
Cost per TEU & \win{Ranges} & Estimates \\
Carrier names & \win{Specific} & General \\
Alt.\ conditions & \win{Decision tree} & Brief \\
Machine-parse & \win{Yes} & \lose{No} \\
Ref & \href{\ghbase/results-callagent/06-supply-chain-standard.md}{\texttt{CA-06}} & \href{\ghbase/results/08-supply-chain.md}{\texttt{SS-08}} \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Verdict:} Call Agent was slower (141\,s vs 62\,s) but produced more granular data---specific carrier options, cost ranges per TEU, and explicit decision criteria for route selection. Spawn noted that web search was unavailable and relied on domain knowledge. When web access matters, Call Agent's standard mode (which includes search) has an advantage.

\subsection{Data Science: Forecasting Models}

\begin{table}[H]
\centering
\caption{Sales forecasting model comparison.}
\begin{tabular}{@{}p{2.2cm}p{2.1cm}p{2.1cm}@{}}
\toprule
\textbf{Metric} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Mode & \texttt{standard} & \texttt{run} \\
Runtime & \win{$\sim$53\,s} & \lose{62\,s} \\
Models compared & \tie{4} & \tie{4} \\
MAPE ranges & \tie{Yes} & \tie{Yes} \\
Impl.\ plan & \win{8 steps} & 6 steps \\
Code examples & Described & \win{Inline} \\
Machine-parse & \win{Yes} & \lose{No} \\
Ref & \href{\ghbase/results-callagent/07-data-science-standard.md}{\texttt{CA-07}} & \href{\ghbase/results/04-data-science.md}{\texttt{SS-04}} \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Verdict:} Nearly identical quality and speed. Call Agent's 8-step implementation plan is more granular; Spawn included inline code snippets. Both recommended XGBoost. A tie on substance; Call Agent wins on structure, Spawn wins on readability.

\subsection{Education: ML Curriculum}

\begin{table}[H]
\centering
\caption{4-week ML curriculum design.}
\begin{tabular}{@{}p{2.2cm}p{2.1cm}p{2.1cm}@{}}
\toprule
\textbf{Metric} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Mode & \texttt{standard} & \texttt{run} \\
Runtime & \lose{$\sim$93\,s} & \win{70\,s} \\
Total hours & \tie{60} & \tie{60} \\
Prerequisites & \tie{Detailed} & \tie{Detailed} \\
Weekly projects & \tie{4} & \tie{4} \\
Resources & \tie{Per-week} & \tie{Per-week} \\
Assessment & \win{Rubric + \%} & General \\
Post-curriculum & \win{4 paths} & 3 paths \\
Machine-parse & \win{Yes} & \lose{No} \\
Ref & \href{\ghbase/results-callagent/08-education-ml-standard.md}{\texttt{CA-08}} & \href{\ghbase/results/05-education-ml.md}{\texttt{SS-05}} \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Verdict:} Both produced excellent curricula. Call Agent included explicit grading rubrics and an extra career path. Spawn's narrative format is more immediately usable as course documentation. For LMS integration: Call Agent. For a syllabus document: Spawn.

\subsection{M\&A Finance: Due Diligence}

\begin{table}[H]
\centering
\caption{M\&A financial due diligence (TechCorp \$400M acquisition).}
\begin{tabular}{@{}p{2.2cm}p{2.1cm}p{2.1cm}@{}}
\toprule
\textbf{Metric} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Mode & \texttt{full} & \texttt{run} \\
Runtime & \win{$\sim$58\,s} & \lose{78\,s} \\
Red flags & \tie{8} & \tie{8} \\
Valuation & \tie{Detailed} & \tie{Detailed} \\
Revenue quality & \tie{ARR/NRR/churn} & \tie{ARR/NRR/churn} \\
Recommendation & \tie{Proceed w/ caut.} & \tie{Upper fair value} \\
Email to writer & \win{Yes} & \lose{N/A} \\
Machine-parse & \win{Yes} & \lose{No} \\
Ref & \href{\ghbase/results-callagent/09-ma-finance-full.md}{\texttt{CA-09}} & \href{\ghbase/results/10-ma-finance.md}{\texttt{SS-10}} \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Verdict:} This is the most significant comparison. Both produced 8 red flags, detailed valuation analysis, and cautious recommendations. Call Agent was faster (58\,s vs 78\,s) and \textbf{autonomously emailed the writer agent} to begin formatting the deliverable---demonstrating inter-agent coordination within the AgenticMail ecosystem. Spawn produced a richer narrative but had no inter-agent communication capability.

\section{Inter-Agent Email Coordination}

The M\&A finance test (Call Agent, \texttt{mode="full"}) demonstrated a capability unique to AgenticMail: \textbf{inter-agent email coordination}. The finance agent, upon completing its analysis, autonomously identified that the results should be shared with a writer agent for formatting into a board-ready presentation. It sent an email within the AgenticMail system:

\begin{lstlisting}
email_coordination: {
  email_sent_to_writer: true,
  recipient_agent: "writer",
  email_subject: "M&A Due Diligence Summary
    -- TechCorp Acquisition",
  email_purpose: "Sent executive summary
    to writer agent for formatting
    into board-ready presentation"
}
\end{lstlisting}

This demonstrates a \textbf{workflow pattern} unique to Call Agent's full mode: agents can trigger downstream work in other agents without orchestrator intervention. OpenClaw Sessions Spawn requires the orchestrator to manually coordinate between agents (spawn A, wait for result, spawn B with A's output).

\section{Aggregate Comparison}

\subsection{Latency}

\begin{table}[H]
\centering
\caption{Latency comparison across all tests.}
\begin{tabular}{@{}lrr@{}}
\toprule
\textbf{Metric} & \textbf{Call Agent} & \textbf{Sessions Spawn} \\
\midrule
Tests run & 9 & 16 \\
Minimum & 18\,s & 24\,s \\
Maximum & 141\,s & 107\,s \\
Median & 93\,s & 65\,s \\
Mean & 82\,s & 64\,s \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Note:} In standard/full modes, Call Agent is \emph{not} consistently faster than Spawn. Light mode ($\sim$18\,s) is the fastest option. Standard mode runtimes (48--141\,s) overlap significantly with Spawn (24--107\,s). The latency advantage from v1--v3 (which only tested light mode) does not hold across all modes.

\subsection{Output Quality}

\begin{table}[H]
\centering
\caption{Quality comparison on 7 overlapping domains (1--5 scale).}
\label{tab:quality-overlap}
\begin{tabular}{@{}lcc@{}}
\toprule
\textbf{Domain} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Code Review & \tie{5.0} & \tie{5.0} \\
Finance (AAPL) & \win{4.75} & \lose{4.5} \\
Healthcare & \tie{4.75} & \tie{4.75} \\
Marketing & \win{5.0} & \lose{4.75} \\
Supply Chain & \lose{4.5} & \win{4.5} \\
Data Science & \tie{5.0} & \tie{5.0} \\
Education & \tie{5.0} & \tie{5.0} \\
M\&A Finance & \win{5.0} & \lose{4.75} \\
\midrule
\textbf{Average} & \win{\textbf{4.88}} & \lose{\textbf{4.78}} \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Key finding:} Quality is comparable across both paradigms when Call Agent uses standard/full mode. The v2 assumption that Call Agent produces only shallow output is \textbf{incorrect}. Both produce expert-level analysis; they differ in \emph{format} (structured JSON vs.\ narrative prose), not \emph{depth}.

\subsection{Capability Matrix}

\begin{table}[H]
\centering
\caption{Head-to-head capability ranking.}
\label{tab:ranking}
\begin{tabular}{@{}lcc@{}}
\toprule
\textbf{Criterion} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Structured output & \win{Always JSON} & \lose{Free-form} \\
Machine-parseability & \win{Native} & \lose{Requires parsing} \\
Composability & \win{Programmatic} & \lose{Manual} \\
Human readability & \lose{Raw JSON} & \win{Formatted prose} \\
Reasoning depth & \tie{High (std/full)} & \tie{High} \\
Creative output & \lose{Structured} & \win{Narrative} \\
Tool autonomy & \tie{Yes (std/full)} & \tie{Yes} \\
Inter-agent email & \win{Full mode} & \lose{Not available} \\
Async execution & \win{Native} & \tie{Via yield} \\
Parallel agents & \lose{Sequential} & \win{Native parallel} \\
Session persistence & \lose{Stateless} & \win{Persistent} \\
Cost efficiency & \win{Lower overhead} & \lose{Full session} \\
\bottomrule
\end{tabular}
\end{table}

\section{What Changed from v2 to v4}

Version~2 of this paper concluded that Call Agent was limited to shallow data retrieval because it was tested only in \texttt{light} mode. The v4 tests reveal three corrections:

\begin{enumerate}[itemsep=2pt,topsep=4pt]
    \item \textbf{Call Agent does deep analysis.} In \texttt{standard} mode, it produced 9-issue code reviews, 8-red-flag M\&A reports, and comprehensive 4-week curricula---all as structured JSON.
    \item \textbf{Call Agent is not always faster.} Standard/full modes (48--141\,s) overlap with Spawn (24--107\,s). Only light mode provides consistent speed advantage.
    \item \textbf{Call Agent coordinates between agents.} In \texttt{full} mode, the finance agent autonomously emailed the writer agent. This workflow pattern is not possible with Sessions Spawn.
\end{enumerate}

The fundamental difference is not \emph{depth} but \emph{format}: Call Agent returns structured JSON, Spawn returns narrative prose. Both achieve comparable analytical quality.

\section{OpenClaw Spawn: Unique Capabilities}

Sessions Spawn was tested on 9 additional domains not covered by Call Agent, demonstrating capabilities that benefit from session isolation and free-form output:

\begin{table}[H]
\centering
\caption{Sessions Spawn: unique test domains.}
\begin{tabular}{@{}lrrl@{}}
\toprule
\textbf{Domain} & \textbf{Time} & \textbf{Quality} & \textbf{Ref} \\
\midrule
Legal (SaaS ToS) & 53\,s & 5.0 & \href{\ghbase/results/03-legal-saas.md}{\texttt{SS-03}} \\
Creative (Story A) & 29\,s & 5.0 & \href{\ghbase/results/07-story-part-a.md}{\texttt{SS-07}} \\
Creative (Story B) & 29\,s & 5.0 & \href{\ghbase/results/11-story-part-b.md}{\texttt{SS-11}} \\
Research (RAG) & 69\,s & 4.5 & \href{\ghbase/results/09-research-rag.md}{\texttt{SS-09}} \\
M\&A Legal & 90\,s & 5.0 & \href{\ghbase/results/13-ma-legal.md}{\texttt{SS-13}} \\
M\&A Operations & 107\,s & 5.0 & \href{\ghbase/results/15-ma-operations.md}{\texttt{SS-15}} \\
Clinical Stats & 88\,s & 5.0 & \href{\ghbase/results/12-clinical-stats.md}{\texttt{SS-12}} \\
Clinical Regulatory & 97\,s & 5.0 & \href{\ghbase/results/14-clinical-regulatory.md}{\texttt{SS-14}} \\
Marketing (expanded) & 88\,s & 4.75 & \href{\ghbase/results/16-marketing-campaign.md}{\texttt{SS-16}} \\
\bottomrule
\end{tabular}
\end{table}

\textbf{Multi-agent collaboration highlights:}

\begin{itemize}[itemsep=2pt,topsep=4pt]
    \item \textbf{Collaborative story:} Agent~A wrote Part~1 (\href{\ghbase/results/07-story-part-a.md}{\texttt{SS-07}}); Agent~B continued with Part~2 (\href{\ghbase/results/11-story-part-b.md}{\texttt{SS-11}}) maintaining perfect tonal consistency. Quality: 5/5.
    \item \textbf{M\&A due diligence (3 agents):} Financial (\href{\ghbase/results/10-ma-finance.md}{\texttt{SS-10}}), Legal (\href{\ghbase/results/13-ma-legal.md}{\texttt{SS-13}}), Operational (\href{\ghbase/results/15-ma-operations.md}{\texttt{SS-15}}) agents worked in parallel with zero contradictions. Reports directly combinable.
    \item \textbf{Clinical trial (2 agents):} Statistical (\href{\ghbase/results/12-clinical-stats.md}{\texttt{SS-12}}) and Regulatory (\href{\ghbase/results/14-clinical-regulatory.md}{\texttt{SS-14}}) agents independently identified CV events as underpowered and cited ICH~E9(R1). Perfect alignment.
\end{itemize}

Sessions Spawn's native parallelism (5--6 agents simultaneously) achieved 4.2$\times$ speedup over sequential execution across 16 tasks.

\section{When to Use Which}

\subsection{Decision Framework}

\begin{table}[H]
\centering
\caption{Decision framework: AgenticMail Call Agent vs.\ OpenClaw Sessions Spawn.}
\label{tab:decision}
\begin{tabular}{@{}p{3.5cm}cc@{}}
\toprule
\textbf{Scenario} & \textbf{Call Agent} & \textbf{Spawn} \\
\midrule
Data retrieval (weather, prices) & \win{\checkmark\checkmark} & --- \\
Structured API response & \win{\checkmark\checkmark} & --- \\
Dashboard/pipeline input & \win{\checkmark\checkmark} & --- \\
Code review for CI/CD & \win{\checkmark} & \checkmark \\
Financial analysis & \win{\checkmark} & \checkmark \\
Client-facing report & --- & \win{\checkmark\checkmark} \\
Creative writing & --- & \win{\checkmark\checkmark} \\
Legal document drafting & --- & \win{\checkmark\checkmark} \\
Multi-specialist parallel & --- & \win{\checkmark\checkmark} \\
Agent-to-agent workflow & \win{\checkmark\checkmark} & --- \\
Long-running research & --- & \win{\checkmark\checkmark} \\
Quick async subtask & \win{\checkmark\checkmark} & --- \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Use AgenticMail Call Agent When:}

\begin{itemize}[itemsep=2pt,topsep=4pt]
    \item Output must be \textbf{machine-parseable} (APIs, dashboards, CI/CD)
    \item You need \textbf{structured data} for downstream programs
    \item The task benefits from \textbf{inter-agent email coordination} (use \texttt{full} mode)
    \item \textbf{Async fire-and-forget} is desired (standard/full with \texttt{async=true})
    \item The orchestrator needs to \textbf{synthesize data from multiple sources}
    \item Use \texttt{light} for lookups, \texttt{standard} for analysis, \texttt{full} for workflows
\end{itemize}

\subsection{Use OpenClaw Sessions Spawn When:}

\begin{itemize}[itemsep=2pt,topsep=4pt]
    \item The \textbf{output is the deliverable} (reports, briefs, documents, stories)
    \item You need \textbf{multiple specialists in parallel} on complementary aspects
    \item The task requires \textbf{creative or narrative output}
    \item The sub-agent needs \textbf{autonomous tool use} with session persistence
    \item \textbf{Sequential handoff} between agents is needed (story continuation, iterative refinement)
    \item Human readability matters more than machine parseability
\end{itemize}

\section{Conclusion}

This v4 study corrects a significant bias in earlier versions by testing AgenticMail Call Agent across all three execution modes. Our findings:

\begin{enumerate}[itemsep=2pt,topsep=4pt]
    \item \textbf{Both paradigms produce expert-level analysis.} Quality scores are comparable (Call Agent: 4.88/5, Spawn: 4.78/5 on overlapping domains). The difference is format, not depth.
    \item \textbf{Call Agent always returns structured JSON.} Even for complex M\&A due diligence with 8 red flags, the output is machine-parseable. This is its defining advantage.
    \item \textbf{Sessions Spawn always returns readable prose.} For deliverables that will be read by humans, Spawn's narrative format requires no post-processing.
    \item \textbf{Call Agent enables inter-agent email.} In full mode, agents can trigger downstream work in other agents autonomously---a workflow pattern impossible with Sessions Spawn.
    \item \textbf{Sessions Spawn enables native parallelism.} Running 5--6 specialists simultaneously with 4.2$\times$ speedup is a natural pattern for Sessions Spawn but not for Call Agent.
\end{enumerate}

\textbf{The bottom line:} Use Call Agent when you need structured, composable data. Use Sessions Spawn when you need autonomous specialist work with human-readable output. For architects building multi-agent systems: implement both.

All test results are available in the companion directories: \href{\ghbase/results-callagent}{\texttt{results-callagent/}} (AgenticMail Call Agent, 9 tests) and \href{\ghbase/results}{\texttt{results/}} (OpenClaw Sessions Spawn, 16 tests).

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\end{document}
