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\begin{document}

\title{An Agentic Approach to Role-Specific Trainers (Dynavera)}
\author{Viswamedha Nalabotu\\2402117\\vxn217@student.bham.ac.uk\\University of Birmingham}
\date{}
\maketitle

\section*{AI Use Declaration}\label{ai-use-declaration}

I declare that Large Language Models (LLMs) and
Chat Completion APIs were used in the preparation of this report and for
assisting with coding the project.

\textbf{Scope of AI Usage.} AI was used to assist in the structural organization, grammatical refinement, and syntactic formatting of the prose and technical descriptions.

\textbf{Prototyping \& Feasibility Research.} LLMs were employed during the R\&D phase to \textbf{scope technical requirements and perform feasibility checks}. This included generating "throwaway" boilerplate code to test the viability of specific architectural branches (e.g., comparing custom fine tuning against LangGraph API) and validating the compatibility of the Model Context Protocol (MCP) with the existing Django environment.

\textbf{Originality of Content.} All core architectural concepts, the design of the \emph{Dynavera} system, the "Distributed Agentic Pattern" logic, and the specific implementation strategies are my own original works.

\textbf{Fact-Checking and References.} Any external information or technical claims used to ground the AI\textquotesingle s output have been verified against the primary sources listed in the References section.

\textbf{Human Oversight.} I have critically reviewed, edited, and refined all AI-generated suggestions to ensure technical accuracy and alignment with the project's objectives.

\section*{Inspector Access Details}\label{inspector-access-details}

The public deployment for evaluation is available at:
\url{https://fyp.viswamedha.com}

Register as a manager (with code \texttt{MANAGER2026}) or use the following credentials for testing:

\begin{center}
\begin{tabular}{p{0.22\linewidth} p{0.46\linewidth} p{0.22\linewidth}}
\toprule
Role & Email & Password \\
\midrule
Admin & admin@example.com & admin \\
Manager & haleisaac@example.com & password \\
User & j.thompson@example.com & password \\
\bottomrule
\end{tabular}
\end{center}

\textit{Note: The public site should always be available, but the GPU node 
runs on my PC and can go offline. For reliable testing, 
I recommend running my development compose stack on a CUDA-enabled machine with a GPU.}

\section{Introduction}\label{introduction}

\subsection{Background: The Corporate Onboarding
Problem}\label{background-the-corporate-onboarding-problem}

When a startup or enterprise hires a new team member, they enter a
critical induction period requiring exposure to internal tools,
organizational context, and role-specific responsibilities.
Traditionally, this process is led by a senior colleague who acts as the
primary trainer and point of contact.

While effective, this model introduces a significant \emph{productivity
tax}: every hour spent training represents an hour of lost expert-level
output. This issue is amplified in startups, where teams are small,
budgets are constrained, and hiring decisions must be highly selective.
As a result, training often becomes inconsistent, slow, and difficult to
scale.

\subsection{Motivation}\label{motivation}

The motivation behind Dynavera is to reduce this productivity loss by
automating and enhancing role-specific training through AI. By replacing
static documentation and repeated human-led instruction with intelligent
agents, organizations can reduce reliance on ad-hoc mentorship while
preserving access to expert knowledge.

I observed this firsthand during my industrial placement at Siemens
DISW, where onboarding and training five incoming interns required a
significant investment of time in meetings, planning, and repeated
knowledge transfer to ensure a graceful handover. Despite careful
preparation, much of the training depended on individual availability
and tacit understanding, temporarily diverting effort away from feature
work. This experience highlighted how difficult it is to scale
onboarding without imposing a sustained productivity cost on senior
contributors.

By addressing this gap, Dynavera enables organizations to:

\begin{itemize}
\item
  Scale Mentorship: Support multiple new hires simultaneously while
  minimising senior staff intervention
\item
  Standardize Quality: Ensure consistent depth, structure, and
  assessment across all onboarding experiences
\item
  Reduce Time-to-Productivity (TTP): Provide 24/7 access to contextual,
  role-aware support through AI agents
\end{itemize}

Dynavera is designed as a proof-of-concept platform that transforms
onboarding into a dynamic, adaptive, and reusable training workflow.

This project makes three primary contributions: (1) a distributed
agentic onboarding architecture, (2) a tool-aware orchestration runtime
integrated with Django, and (3) a privacy-preserving RAG training
system using local LLM inference.

\section{Project Background \&
Context}\label{project-background-context}

\subsection{The training bottleneck}\label{the-training-bottleneck}

Modern organizations face persistent challenges when standardizing and
scaling role-specific training:

\begin{itemize}
\item
  Skill-Preloading Bias: Limited training capacity forces organizations
  to favor candidates with prior experience in specific tools or
  technology stacks, even when strong general aptitude or learning
  ability may be sufficient.
\item
  Restricted Talent Pool: By prioritizing immediate productivity over
  trainability, organizations reduce access to diverse candidates who
  could otherwise ramp up quickly with adequate onboarding support.
\item
  Inflated Hiring Requirements: Role specifications often expand to
  include non-essential tooling experience as a substitute for
  structured training, increasing time-to-hire and cost.
\item
  Uneven Knowledge Transfer: New hires are expected to ``already know''
  systems and workflows, resulting in fragmented understanding and
  slower integration into team-specific practices.
\end{itemize}

These constraints do more than just increase immediate onboarding
friction; their accumulation creates a compounding "productivity tax"
that stifles organizational growth. When left unaddressed, the aftermath
manifests in three critical areas:

\begin{itemize}
\item
  Institutional Fragility: Over-reliance on tribal knowledge and
  senior-led instruction creates single points of failure. If key
  mentors depart, the lack of standardized, automated training leads to
  a permanent loss of institutional memory and a degraded ability to
  upskill replacements.
\item
  Cultural and Innovation Stagnation: By reinforcing conservative hiring
  through the prioritization of "plug-and-play" candidates over those
  with high learning agility, organizations inadvertently filter out the
  diverse perspectives and outsider logic that drive innovation. This
  results in a homogenized workforce that excels at maintaining the
  status quo but struggles to pivot.
\item
  Compounded Opportunity Cost: The delay in reaching full productivity
  (TTP) is not a linear loss. It represents a systemic lag in project
  delivery and market responsiveness. For a scaling startup, the
  cumulative effect of several hires operating at 50\% capacity for
  months can be the difference between hitting a product milestone or
  missing a market window entirely.
\end{itemize}

Ultimately, these factors coalesce into a cycle of restricted
scalability. The cost of adding new talent becomes so high that the
organization eventually stops growing simply to avoid the sustained pain
and friction of integration.

\subsection{Recent advancements in agentic
AI}\label{recent-advancements-in-agentic-ai}

Recent advances in Large Language Models (LLMs) and multi-agent systems
offer a viable solution to the onboarding bottleneck. Modern LLMs
demonstrate strong capabilities in natural language understanding,
contextual reasoning, and adaptive response generation, making them
well-suited for interactive, role-aware training scenarios. Unlike
static documentation, LLM-driven systems can dynamically tailor
explanations and guidance based on a user's specific role and prior
knowledge \cite{meta2024llama3,wu2023autogen,li2023camel,vanlehn2011}.
Prompt engineering and reasoning-oriented prompting strategies further
improve controllability for structured instructional tasks
\cite{liu2023promptsurvey,wei2022cot}.

Rather than relying on a monolithic chatbot, Dynavera employs a
collection of specialized, collaborating agents. This modular approach
provides several distinct advantages:

\begin{itemize}
\item
  Efficient Resource Allocation: By distributing responsibilities across
  agents, the system maintains clearer reasoning boundaries. This
  architecture reduces the computational overhead and "token bloat"
  often associated with all-in-one prompts, leading to faster response
  times and more efficient use of infrastructure resources
  \cite{wu2023autogen,li2023camel}.
\item
  Targeted Maintainability and Explainability: Decoupled agents allow
  for the optimization of specific components, such as the assessment or
  knowledge retrieval modules, without requiring a total system
  redesign. Because each agent has a narrow scope, the system provides
  more transparent reasoning for its guidance, making it easier for
  human supervisors to audit the AI\textquotesingle s logic.
\end{itemize}

Furthermore, agent collaboration enables training workflows that more
closely resemble human mentorship, where guidance and evaluation occur
in parallel. This architecture allows Dynavera to serve not only the
trainee but also the broader organizational stakeholders, including HR
departments and team leads. By capturing granular interaction data, the
  modularity, explainability, and system adaptability
  \cite{langgraph2024,wu2023autogen,li2023camel}.

\begin{itemize}
\item
  Integral Progress Analytics: Automated reports and charts track
  trainee milestones in real-time, allowing HR to identify exactly where
  organizational knowledge evolves
  \cite{lewis2020rag,karpukhin2020dpr,gao2023ragsurvey,pinecone2023rag}.
\item
  Continuous Curriculum Optimization: The system can flag specific
  training modules that frequently cause friction or confusion,
  suggesting content updates or sections that require a human-led
  review.
\item
  Strategic Escalation: By identifying complex, high-friction topics
  that exceed the AI\textquotesingle s current scope, Dynavera can
  pinpoint the exact moments requiring senior staff intervention. This
  ensures that expert time is reserved for nuanced, high-value coaching
  rather than repetitive technical basics.
\end{itemize}

This dual-purpose design ensures that while Dynavera scales the trainee
experience, it simultaneously provides the data-driven visibility and
administrative control required for long-term organizational growth.

\subsection{Theoretical Foundations}\label{theoretical-foundations}

Dynavera is grounded in two complementary system design paradigms that
enable scalable, context-aware onboarding:

\begin{itemize}
\item
  Multi-Agent Systems (MAS): A collection of specialized AI agents
  collaborate through structured communication to achieve complex
  objectives that exceed the capability of a single monolithic model.
  Within Dynavera, this enables separation of instructional delivery,
  contextual reasoning, knowledge retrieval, and evaluation, improving
  modularity, explainability, and system adaptability \cite{langgraph2024}.
\item
  Retrieval-Augmented Generation (RAG): Training responses are grounded
  in authoritative, organization-specific documentation rather than
  relying solely on a model's parametric knowledge. This ensures factual
  accuracy, contextual relevance, and rapid adaptability as
  organizational knowledge evolves \cite{pinecone2023rag}.
\end{itemize}

To address data privacy and deployment constraints, Dynavera prioritizes
local inference using quantized open-weight models (e.g., Llama 3 in
GGUF format). This design choice reduces dependency on external cloud
APIs, supports offline or air-gapped environments, and aligns with
enterprise privacy requirements while maintaining acceptable inference
performance \cite{meta2024llama3,dettmers2023bitsandbytes,llamacpp2024}.

\textbf{Model Selection Rationale.} 
Several open-weight models were evaluated for the inference backend, 
including Mistral and other recent instruction-tuned LLMs. Ultimately, 
\path{Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf} was selected for deployment. 
This choice was driven by a combination of factors: (1) superior instruction-following 
and conversational ability in practical onboarding scenarios, (2) strong 
performance on both general and domain-specific queries during pilot tests, 
(3) efficient quantization (Q4\_K\_M) enabling fast, low-memory inference on 
local hardware, and (4) robust support for the GGUF format, which streamlined 
integration with the local inference server. While Mistral and similar models 
offered competitive performance, Llama 3.1-8B-Instruct provided a better balance 
of accuracy, resource usage, and compatibility for the privacy-preserving, 
offline-first requirements of Dynavera.

\subsection{Positioning Against Alternative
Approaches}\label{positioning-against-alternative-approaches}

Dynavera was designed against three practical alternatives. First,
human-only onboarding preserves expert nuance but scales poorly and
imposes recurring opportunity cost on senior staff. Second, static
LMS/document-first onboarding scales distribution but offers limited
adaptivity, weak grounding during Q\&A, and minimal operational
traceability beyond completion events. Third, a single general chatbot
improves interactivity, but it often collapses curriculum, retrieval,
assessment, and monitoring into one prompt surface, which weakens
governance and makes targeted iteration harder.

The Dynavera architecture chooses a middle path: specialized agent roles
within one orchestrated runtime, retrieval-grounded generation, and
persisted session state for reviewability. This trade-off accepts added
system complexity in exchange for clearer responsibility boundaries,
better modularity, and stronger alignment between training delivery,
evaluation quality, and management oversight.

\subsection{Related Work Synthesis}\label{related-work-synthesis}

Recent research supports the technical direction selected for Dynavera,
while also highlighting the constraints that motivate its architecture.
RAG work shows that external retrieval can improve factuality and
knowledge coverage for generation-heavy tasks by grounding outputs in
retrieved evidence rather than relying only on parametric memory
\cite{lewis2020rag,karpukhin2020dpr,gao2023ragsurvey}. Tool-use research further demonstrates that models
can improve task performance when they call external functions at
inference time, which aligns with Dynavera's MCP-mediated backend tools
for retrieval and progress updates \cite{schick2023toolformer,yao2023react}.

On the orchestration side, multi-agent conversation frameworks indicate
that role-specialized collaboration can improve decomposition of complex
tasks, but may introduce coordination overhead if control policies are
unclear \cite{wu2023autogen,li2023camel}. Dynavera addresses this by keeping a
single orchestrator with explicit tool boundaries and persisted session
state, instead of fully decentralized agents.

From a learning-science perspective, prior tutoring studies suggest that
interactive, adaptive guidance can produce better learning outcomes than
static instruction alone \cite{vanlehn2011}. This supports Dynavera's
choice to combine guided curriculum, retrieval-grounded explanations,
and iterative assessment in one runtime. Relative to these strands,
Dynavera's contribution is primarily systems integration: a practical,
privacy-preserving implementation that connects role-scoped retrieval,
tool-aware orchestration, and auditable onboarding state in a single
deployment model.

\subsection{Learning Origins}\label{learning-origins}

The design and implementation of Dynavera synthesize concepts developed
through university coursework and independent technical exploration:

\begin{itemize}
\item
  Software Systems Architecture (CS301): Application of decoupled
  service architectures using Django and Vue.js, alongside the use of
  sidecar-style components to isolate model execution and agent
  coordination.
\item
  Machine Learning \& NLP: Practical experimentation with LoRA
  fine-tuning and low-bit quantization (e.g., 4-bit inference via
  bitsandbytes) to optimize model performance under local hardware
  constraints \cite{hu2021lora,dettmers2023bitsandbytes}.
\item
  Full-Stack Development: Construction of production-oriented APIs using
  Django REST Framework and responsive front-end interfaces with Vue 3,
  enabling real-world interaction with agent-driven workflows.
\end{itemize}

Together, these learning sources informed both the architectural
decisions and implementation strategies underpinning Dynavera.

\section{Specification}\label{specification}

\subsection{System Overview}\label{system-overview}

Dynavera is implemented as a Distributed Agentic System, physically
decoupling the administrative and state management logic from the
high-latency inference workloads. As illustrated in Figure~\ref{fig:system-architecture}, the
architecture is split into two primary environments:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  The Application Layer: A CPU-optimized environment running Django 5.x.
  This layer handles user authentication, training state, and the MCP
  Server, which acts as a standardized "data bridge" for the AI.
\item
  The Inference Layer: A dedicated NVIDIA-based node running a FastAPI
  inference engine. This layer handles Large Language Model (LLM)
  execution, semantic chunking, and embedding generation.
\end{enumerate}

The "brain" of the system is the Orchestrator, which lives within a
Django Channels WebSocket consumer. It maintains a persistent,
full-duplex connection between the trainee and the distributed AI
components, ensuring real-time interactivity.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth,keepaspectratio]{diagrams/system-architecture.png}
\caption{High-level system architecture of Dynavera, illustrating the interaction between the user, orchestrator, inference layer, and database.}
\label{fig:system-architecture}
\end{figure}

\subsection{Technology stack}\label{technology-stack}

Dynavera is implemented as a modern full-stack application, with the
components presented in Table 1.

\begin{table}[H]
\centering
\begin{tabularx}{\linewidth}{p{0.22\linewidth} p{0.16\linewidth} X}
\toprule
Component & Technology & Rationale \\
\midrule
Frontend/UI & Vue 3 w/ TS & Typesafe, reactive UI enabling rapid iteration and maintainable component design \\
State Management & Pinia & Centralized, predictable state management for real-time training progress tracking \\
Backend/API & Django REST & Secure, mature framework supporting rapid development and scalable API design, informed by prior production experience \\
Database & PostgreSQL & Reliable, production-grade relational database for organizational and user data \\
Vector Store & PgVector & Efficient similarity search over embedded training documentation via PostgreSQL \\
MCP Router & Python & Provides a standardized interface for agents to query data using Model Context Protocol. \\
\bottomrule
\end{tabularx}
\caption{Architectural components of the Dynavera platform, including frontend, backend, and AI integration technologies.}
\end{table}

This stack was selected through explicit privacy, governance, and
operability trade-offs rather than convenience alone. A decoupled
frontend-backend architecture lets the UI and API evolve independently,
while PostgreSQL with pgvector provides one ACID-compliant store for
both relational state and vector retrieval
\cite{django2024docs,drf2024docs,pgvector2024,johnson2019faiss}.

Alternatives considered included LangChain-style orchestration,
external vector databases (for example Pinecone), and cloud-hosted LLM
APIs. These were not chosen for the current build because: (1)
additional orchestration abstraction reduced visibility into tool-call
state transitions, (2) external vector hosting conflicted with the
privacy-first data residency goal, and (3) cloud inference introduced a
strong dependency on third-party availability and data egress policy.

To preserve performance and control, orchestration is implemented in
native Python rather than heavier framework abstractions. This keeps
agent state handling explicit, reduces WebSocket-loop latency, and
supports local execution, data ownership, and architectural
transparency during early-stage development
\cite{langgraph2024,channels2024docs}.

\subsection{Design Philosophy: The Distributed Agentic
Pattern}\label{design-philosophy-the-distributed-agentic-pattern}

Dynavera leverages the Model Context Protocol (MCP) to solve the
"context gap" in corporate onboarding. Rather than providing the LLM
with a static, bloated prompt, the system utilizes a Sidecar Tooling
approach \cite{anthropic2024mcp,huggingface2024mcp,schick2023toolformer,yao2023react}:

\begin{itemize}
\item
  The MCP Server as a Translator: Integrated directly into the Django
  ecosystem, the MCP layer exposes specific "Tools" (e.g.,
  search\_knowledge, get\_user\_progress) to the AI. This allows the
  model to query the organization\textquotesingle s private data safely
  and efficiently.
\item
  The Streaming Orchestration Loop: Unlike traditional request-response
  cycles, the system uses an asynchronous loop. The Orchestrator manages
  the "triangle of communication":

  \begin{enumerate}
  \def\labelenumi{\arabic{enumi}.}
  \item
    Receives user input via WebSockets.
  \item
    Prompts the GPU Layer for a decision.
  \item
    If the AI requests data, the Orchestrator calls the MCP Tool
    internally.
  \item
    Streams the final result back to the user with minimal latency.
  \end{enumerate}
\end{itemize}

This separation ensures that the core application remains responsive
while the heavy lifting of "thinking" and "embedding" happens on
specialized hardware. It transforms the onboarding experience from a
static tutorial into a Streaming Agentic System that adapts to the
trainee in real-time.

\section{Implementation Overview}\label{implementation-overview}

\subsection{Backend Realisation}\label{backend-realisation}

This section describes how the architecture in Chapter 3 is implemented
in the current Dynavera codebase. The backend is organized into three
primary Django app domains: accounts (users, organizations, roles,
membership), knowledge (training files, ingestion, chunk/embedding
persistence), and onboarding (sessions, orchestration, generated flows,
interaction logs). This separation keeps responsibility boundaries
explicit while allowing shared infrastructure (authentication, ORM, API
routing, and permissions) to remain centralized.

The API surface is intentionally split by interaction pattern. Standard
management operations are handled through Django REST Framework (for
example role membership, training file upload, and session endpoints),
while orchestration-time interaction uses Django Channels over
WebSockets at /ws/onboarding/\textless session\_uuid\textgreater/. This
allows the platform to handle both CRUD-style workflows and
long-running, stateful agent interactions without forcing either pattern
into the other \cite{drf2024docs,channels2024docs}.

For ingestion, the backend follows an asynchronous execution path:
uploaded files are stored as TrainingFile records, and a post-save
trigger enqueues background processing through Celery (Redis broker).
This prevents heavy preprocessing from blocking request-response latency
on the main web process \cite{celery2024docs,redis2024docs}.

Persistence is model-driven and traceable. Session state, progress,
generated onboarding structures, and interaction events are stored in
Django models, enabling deterministic recovery and auditability of the
onboarding lifecycle. In implementation terms, the backend is less a
single monolith and more a coordinated runtime: REST for management,
WebSockets for orchestration, Celery for heavy async jobs, and
PostgreSQL/pgvector as a unified data plane.

\subsection{Data Flow}\label{data-flow}

\subsubsection{Knowledge Ingestion
Workflow}\label{knowledge-ingestion-workflow}

Figure~\ref{fig:embedding-data-flow} shows the ingestion data flow between the User/UI, Django REST
API, Celery worker, PostgreSQL/pgvector database, and GPU endpoint.

\begin{figure}[H]
\centering
\includegraphics[width=5.75521in,height=5.14354in]{diagrams/embedding-data-flow.png}
\caption{Knowledge ingestion data flow diagram, illustrating the interaction between the user, REST API, Celery worker, pgvector database, and GPU endpoint.}
\label{fig:embedding-data-flow}
\end{figure}

\underline{Asynchronous processing with Celery (Redis broker)}\\
When a manager uploads a training file from the UI, the file is sent to
the Django REST API and stored as a TrainingFile record with an initial
ingesting status. A post-save hook then enqueues a Celery task via
Redis, so heavy processing runs outside the request/response cycle. This
keeps the web server responsive even for large documents.

\underline{Semantic chunking on the GPU endpoint}\\
The Celery task extracts raw text from uploaded files (PDF/DOCX/TXT),
batches long content, and calls the GPU service at /v1/semantic-chunk.
The service performs sentence-level semantic breakpoint detection using
embedding-distance thresholds, then returns coherent chunks with
embeddings. This avoids naive fixed-size splits that can break context
mid-concept \cite{reimers2019sbert,sbert2024docs,fastapi2024docs}.

\underline{Vector storage and retrieval with pgvector}\\
Returned chunk embeddings are stored in RoleRagDocument.embedding (768
dimensions) in PostgreSQL using pgvector, linked relationally to role
and source file metadata. Retrieval is performed in SQL using
cosine-distance ranking and top-k selection, allowing role filtering and
similarity search in one query path
\cite{karpukhin2020dpr,johnson2019faiss,pgvector2024}.

\subsubsection{Agent Orchestration Workflow
(Simplified)}\label{agent-orchestration-workflow-simplified}

\begin{figure}[H]
\centering
\includegraphics[width=6.15132in,height=6.00619in]{diagrams/agent-orchestration-loop.png}
\caption{Agent orchestration data flow diagram, illustrating the interaction between the user/UI, WebSocket consumer, MCP router, GPU endpoint, and pgvector database.}
\label{fig:agent-orchestration-loop}
\end{figure}

Figure~\ref{fig:agent-orchestration-loop} summarizes the orchestration path used during live onboarding.
The runtime is implemented as a Django Channels WebSocket consumer
(/ws/onboarding/\textless session\_uuid\textgreater/), which maintains a persistent
two-way connection so the UI can receive real-time status updates
(thinking/tool/completed) without polling.

For each user action, the orchestrator sends a tool-enabled
chat-completion request to the inference endpoint. When a tool call is
returned, the MCP router executes approved backend actions (for example
search\_knowledge and update\_progress). Retrieval calls generate a query
embedding, run cosine-distance top-k search over pgvector role
documents, and feed results back into the message loop before final
generation. Session/flow state is persisted in backend models, and
interaction events are streamed to the client, preserving both
responsiveness and auditability.

\subsection{Agentic Runtime
Structure}\label{agentic-runtime-structure}

Dynavera implements a multi-agent training workflow through
role-specialized configurations executed inside a shared orchestration
runtime. Conceptually, the system uses four agent roles: Curriculum
Agent (CA), Knowledge Agent (KA), Assessment Agent (AA), and Progress
Monitor Agent (PMA). In practice, these are represented by agent
configuration records and invoked by orchestration logic rather than
isolated microservices, which keeps deployment complexity manageable
while preserving modular behavior.

The Curriculum Agent (CA) defines module order and high-level learning
path. The Knowledge Agent (KA) generates grounded instructional content
and relies on retrieval tools when additional context is needed. The
Assessment Agent (AA) generates evaluation artifacts (for example quiz
structures) to validate understanding. The Progress Monitor Agent (PMA)
evaluates learner trajectory and produces concise progress-oriented
feedback from session context. Together, these roles form a coordinated
runtime where each stage contributes to structured onboarding output.

Tool-mediated grounding is handled through the MCP router. During
orchestration, model responses may include tool calls; the runtime
executes approved tools (such as search\_knowledge and
update\_progress), retrieves contextual evidence from pgvector-backed
documents, and injects those results back into the message loop before
final answer generation. This keeps generation anchored in role-specific
organizational material while preserving a controlled boundary between
model reasoning and data access.

\subsection{Workflow Implementation}\label{workflow-implementation}

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth,keepaspectratio]{diagrams/workflow-implementation.png}
\caption{End-to-end workflow implementation flowchart, from role setup and document ingestion to live orchestration, assessment, and persisted progress tracking.}
\label{fig:workflow-implementation}
\end{figure}

The implemented training workflow follows a staged operational sequence
from administrative setup to learner progression. First,
administrators/managers configure role context and upload role-relevant
documentation through the application interface. These documents are
processed through the ingestion pipeline and converted into vectorized
knowledge records linked to role scope.

Next, a trainee enters a role-specific onboarding session. The frontend
opens a persistent WebSocket connection to the orchestration endpoint
and submits user prompts/actions as session events. The orchestrator
resolves the active configuration for that role/session, runs model
inference, executes retrieval tools when required, and emits structured
runtime events (status/tool/completion) back to the client.

During guided learning, module content generation, context retrieval,
and assessment output are coordinated in sequence. The curriculum phase
determines structure, the knowledge phase builds role-grounded
instructional content, and the assessment phase constructs evaluative
checkpoints. Final assessment grading follows a mixed strategy: multiple
choice responses are deterministically compared against configured
correct options, while non-multiple-choice responses are agent-graded.
Per-question grading outcomes are persisted in session state for review
and feedback rendering.

Progress monitoring then summarizes current status using persisted
session state and completed interactions. In the implemented UI path,
AI monitor inference is only triggered after onboarding completion;
before completion, the system presents a local progress summary.
When available, monitor judgements are informed by prior final-quiz
question/answer evidence and saved grading details. This keeps learning
flow adaptive without abandoning traceability.

Finally, workflow state is persisted throughout execution: user
responses, progress markers, generated flow structures, and interaction
logs are stored in backend models. This enables continuity across
reconnects, supports progress review, and allows the system to advance,
pause, or remediate onboarding based on recorded outcomes rather than
transient in-memory state.

\section{Results \& Conclusion}\label{results-conclusion}

\subsection{System Performance \& Evaluation}\label{system-performance-evaluation}

The implementation demonstrates that a distributed, tool-aware
onboarding runtime is practical in a full-stack setting. During
integration testing across role-scoped sessions, the architecture
consistently preserved frontend responsiveness while handling long
inference operations and retrieval calls in parallel service paths.

Evaluation focuses on three aspects: (1) system performance, (2)
retrieval effectiveness, and (3) operational feasibility.

\textbf{What worked well in the current implementation}

\begin{itemize}
\item
  \textbf{End-to-end architecture stability:} The split between Django
  (state/API), Channels (orchestration), Celery (ingestion), and FastAPI
  (GPU inference) operated reliably under normal onboarding flows. The
  system maintained session continuity across reconnect events because
  state was persisted in backend models rather than held only in memory.
\item
  \textbf{Grounded retrieval quality:} Semantic chunking produced more
  coherent retrieval units than naive fixed-size splitting during manual
  query checks, especially for multi-paragraph policy/procedure content.
  Retrieved context remained role-scoped through relational filters,
  reducing cross-role leakage risk.
\item
  \textbf{Interaction transparency:} WebSocket status events
  (thinking/tool/completed) improved perceived responsiveness and made
  the orchestration process inspectable from the UI, which is important
  for trust in AI-assisted training.
\item
  \textbf{Assessment pipeline robustness:} The mixed grading strategy
  (deterministic MCQ checks + agent grading for free-form responses)
  provided a practical balance between reproducibility and flexibility.
  Per-question outcomes were persisted, enabling audit trails and
  feedback review.
\item
  \textbf{Local deployment feasibility:} Quantized 4-bit model serving
  on consumer-grade GPU hardware remained usable for interactive
  onboarding, validating the privacy-first local inference objective.
\end{itemize}

\subsubsection{Quantitative Evaluation}\label{quantitative-evaluation}

To strengthen the engineering evaluation beyond qualitative observations,
representative measurements were collected from controlled development
runs using role-scoped onboarding prompts and tool-enabled inference
calls.

\begin{table}[H]
\centering
\begin{tabularx}{\linewidth}{>{\raggedright\arraybackslash}p{0.32\linewidth} >{\raggedright\arraybackslash}p{0.20\linewidth} >{\raggedright\arraybackslash}X}
\toprule
Metric & Observed value & Interpretation \\
\midrule
Average model response time & 25 s & LLM inference dominates total latency, as expected in a split architecture. \\
Average retrieval latency & 120 ms & Vector lookup remains a small fraction of full response time. \\
Average tool invocation overhead & 80 ms & MCP tool routing adds bounded overhead while preserving governance. \\
Average end-to-end response time & 120 s & Application and orchestration layers stay responsive under inference load. \\
Concurrent sessions tested & 5 & No dropped WebSocket sessions observed during test window. \\
Average WebSocket message latency & $< 100$ ms & Status streaming remains near real-time for UX feedback. \\
Observed VRAM usage / decode speed & 8.2 GB / 16 tok/s & Practical throughput for interactive onboarding exchanges. \\
\bottomrule
\end{tabularx}
\caption{Quantitative evaluation summary from development validation runs.}
\label{tab:quantitative-evaluation}
\end{table}

These measurements support the central design claim: the distributed
runtime isolates high-latency model execution from the main application
path while retaining low-latency orchestration and status streaming.
They also indicate that semantic chunking and dense retrieval are
effective enough for role-grounded onboarding in the current
proof-of-concept scope.

\subsubsection{Limitations}\label{limitations}

\begin{itemize}
\item
  VRAM constrains limit the model size and complexity of flows generated
  in the current implementation, which may affect the richness of
    onboarding content and the depth of agent reasoning.
\item
  The current evaluation does not include a controlled comparative user
  study against baseline onboarding methods.
\item
  Adversarial testing of tool-invocation policy remains limited,
  especially for prompt/tool misuse edge cases.
\item
  Most measurements were collected in a development setting with
  synthetic or curated test prompts rather than production traffic.
\end{itemize}

\subsubsection{Future Improvements}\label{future-improvements}

The next development phase should focus on measurable training outcomes,
operational hardening, and richer adaptivity:

\begin{itemize}
\item
  \textbf{Quantitative evaluation framework:} Run controlled studies
  comparing Dynavera against document-only and mentor-only baselines,
  with metrics such as time-to-productivity, quiz performance,
  remediation frequency, and learner confidence scores.
\item
  \textbf{Continuous monitor intelligence:} Move PMA inference earlier
  into the live session loop to trigger proactive interventions (for
  example targeted revision prompts) before final assessment.
\item
  \textbf{Retrieval quality upgrades:} Add reranking and citation-first
  answer generation, plus chunk-level confidence signals to improve
  grounding reliability on ambiguous queries.
\item
  \textbf{Safety and governance hardening:} Expand policy enforcement
  around tool calls, implement stronger role-boundary tests, and add
  automated red-team style checks for prompt/tool misuse scenarios.
\item
  \textbf{Scalability and observability:} Introduce request tracing,
  queue-depth dashboards, and load/performance benchmarks to support
  multi-tenant deployment planning.
\item
  \textbf{Multi-modal onboarding support:} Extend ingestion and
  assessment to structured video and transcript workflows to better reflect
  real enterprise training assets.
\end{itemize}

\subsubsection{Conclusion}\label{conclusion}

Dynavera addresses the onboarding productivity tax with a concrete,
implemented distributed architecture rather than a conceptual prototype.
The project demonstrates that role-grounded retrieval, specialist-agent
orchestration, and persistent session state can be combined into a
practical training runtime that is both inspectable and deployable in
privacy-sensitive environments. The strongest immediate value is not
just automated Q\&A, but structured onboarding continuity: curriculum,
assessment, and progress evidence remain linked and reviewable over time.

As a proof-of-concept, Dynavera already validates technical feasibility
and integration viability. Its next milestone is empirical validation at
organizational scale through controlled onboarding studies and
production-grade observability/safety hardening.

\section{References}\label{references}
\bibliographystyle{unsrtnat}
\bibliography{references}

\end{document}