OpsPulse

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% Header Block \begin{tcolorbox}[colback=secondary,colframe=secondary,arc=4mm,boxrule=0pt,top=15pt,bottom=15pt,left=15pt,right=15pt] \centering {\Huge \bfseries \color{white} OpsPulse \par} \vspace{6pt} {\Large \color{accent} Automated Admin Task Handler \par} \vspace{10pt} {\small \color{white} \textbf{Hackathon Pitch} \,$\cdot$\, Built with Strands Agents SDK & Model Context Protocol (MCP)} \end{tcolorbox}

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% Executive Summary Card \begin{tcolorbox}[colback=cardbg,colframe=primary,arc=2mm,boxrule=1pt,title=\textbf{Executive Summary}] \textbf{Tagline:} Eliminate the daily administrative tax using model-driven Strands AI agents.\[4pt] OpsPulse leverages the Strands Agents SDK to build an autonomous administrative assistant that intelligently triages unstructured input, coordinates tools, and automates multi-step repetitive operational workflows. \end{tcolorbox}

\section*{1. The Problem Statement} Teams consistently waste \textbf{10--15 hours per week} on manual "glue work"—triaging incoming customer emails, syncing unstructured data across disparate platforms (Jira, Slack, Salesforce), and compiling status reports. \begin{itemize}[leftmargin=*,noitemsep] \item \textbf{Legacy Automation Limitations:} Rule-based tools (e.g., Zapier) fail when inputs are unstructured or variable. \item \textbf{High Maintenance Costs:} Custom script pipelines require constant developer intervention and updates. \end{itemize}

\section*{2. The Solution} OpsPulse delivers an autonomous, model-driven workflow engine powered by the \textbf{Strands Agents SDK}. Instead of brittle hardcoded paths, it uses continuous agentic reasoning loops to analyze tasks, select appropriate tools dynamically, and execute workflows seamlessly in the background.

\section*{3. Technical Architecture & Key Features}

\begin{description}[font=\sffamily\bfseries\color{primary},leftmargin=0pt] \item[Model-Driven Execution:] Operates using Strands' dynamic decision-making loop to evaluate next steps without predefined static logic. \item[Tool Integration (\texttt{@tool}):] Native Python extensions built via Strands \texttt{@tool} decorators for lightweight local actions. \item[Model Context Protocol (MCP):] Direct integration with external software ecosystems and database endpoints via standard MCP servers. \item[Multi-Agent Hand-offs (A2A):] Leverages Agent2Agent (A2A) protocols to transfer domain-specific workloads (e.g., separating parsing from messaging). \item[Multi-Model Support:] Seamless execution across Amazon Bedrock (Claude) for complex reasoning and lightweight local models (Ollama/LiteLLM). \end{description}

\section*{4. Live Demonstration Workflow}

\begin{enumerate}[leftmargin=*,itemsep=2pt] \item \textbf{Trigger:} Customer submits an unstructured support query containing a raw log attachment. \item \textbf{Reasoning:} The primary Strands agent parses the payload and queries the customer profile via an MCP database tool. \item \textbf{Delegation:} The log file is routed via A2A protocol to a specialized diagnostic sub-agent. \item \textbf{Execution:} The agent updates the ticket status, drafts a response, and dispatches a Slack summary—completing a 15-minute manual task in under 5 seconds. \end{enumerate}

\section*{5. Impact & Deployment} \begin{itemize}[leftmargin=*,noitemsep] \item \textbf{Efficiency:} Reduces manual administrative workload by over \textbf{80\%}. \item \textbf{Production Readiness:} Easily scales from local development environments to serverless AWS infrastructure (Lambda/Bedrock). \end{itemize}

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Built With

• a2aprotocol
• agenticai
• aiworkflowautomation
• autoloopai
• autonomousagents
• awsbedrock
• enterpriseautomation
• modelcontextprotocol
• multiagentsystems
• strandsai