Read Time
Before implementing the Multi-Agentic platform, a driver-reported damage incident initiated a lengthy chain of emails, phone calls, and manual approvals. The absence of automated coordination meant that resolution often took 24-48 hours, placing operations managers in a constant firefighting mode and delaying critical maintenance decisions.
With the new platform, a single incident automatically triggers a coordinated sequence of AI actions: The Vision Agent classifies the damage, the Compliance Agent verifies safety requirements, and the Communication Agent generates the necessary maintenance ticket. What once required days is now completed in under 30 seconds, enabling managers to focus on strategic oversight and continuous operational improvement rather than administrative coordination.
Technostacks developed a unified platform leveraging a Multi-Agentic framework to handle specialized tasks with precision and safety.
The following diagram illustrates the interaction between the multi-agent system and the various data sources:
Acts as the brain, routing intents to the appropriate specialized agent. Runs on Claude Sonnet 4.6 via AWS Bedrock.
Leverages Role-Based Access Control (RBAC) and RAG (Retrieval-Augmented Generation) to answer queries with citations and references. Runs on Claude Sonnet 4.6 via AWS Bedrock (Haiku 4.5 for simple FAQ).
Validates responses against fallback rules to eliminate hallucinations and ensure the user’s requirements are met. Runs on Claude Sonnet 4.6 via AWS Bedrock
Manages long-term state and session history, ensuring agents provide personalized, informed responses across interactions. Runs on Claude Haiku 4.5 via AWS Bedrock.
Intercepts actions before execution to cross-reference them against regulatory libraries and internal safety policies, ensuring all operations remain compliant. Runs on Claude Sonnet 4.6 via AWS Bedrock.
Monitors system performance and failure logs to dynamically handle human hand-offs, assigning urgent issues to relevant human managers and drafting context-aware communications. Runs on Claude Sonnet 4.6 via AWS Bedrock.
Provides standardized, helpful messaging during failure cases to maintain user trust. Runs on Claude Haiku 4.5 via AWS Bedrock.
Facilitates real-time, schema-less querying directly into the database to provide instant analytics and operational insights. The Analytics Agent runs on Claude Sonnet 4.6 via AWS Bedrock.
Combines keyword and semantic search (OpenSearch) with clickable references that link back to historical message contexts.
Every agent action, from ticket creation to database querying, is logged, ensuring a 100% auditable trail for compliance.
Data access is strictly compartmentalized; an agent querying the MongoDB for a local depot cannot access sensitive regional financial data.
The Evaluator Agent and Guardrails act as a final filter, ensuring all LLM outputs are cross-referenced with internal Knowledge Base truths before reaching the end-user.
LangGraph
Agentic Framework
AWS Bedrock – Claude Haiku, Sonnet & Llama 4 · RAG & Guardrails
Python
(FastAPI)
React.js
MongoDB
(MCP)
OpenSearch
(Semantic Search)
Amazon RDS
Amazon S3
AWS ElastiCache
(Redis)
Model Context Protocol
(MCP)
The Agentic AI Workflows tab provides stakeholders with a visual command center, showcasing how the platform automates complex operational decisions in real-time. This interface maps the flow of data and decision-making processes across three critical automation paradigms:
These workflows automate routine administrative and operational tasks, ensuring consistent organizational discipline and system health without requiring manual human intervention.
AI monitors workforce schedules and attendance in real time to identify staffing shortages, absenteeism, and operational coverage gaps.
AI analyzes asset data and incident reports to detect potential failures and trigger proactive maintenance workflows.
AI consolidates maintenance, uptime, and resource data into real-time operational insights for faster decision-making.
AI generates audit-ready compliance reports by validating operational data against regulatory and internal standards.
These workflows drive proactive enterprise management by automatically responding to specific data thresholds, telemetry inputs, or critical incidents without requiring constant human oversight.
AI detects critical system failures, isolates affected assets, and automatically triggers high-priority maintenance alerts.
AI tracks battery, fuel, and power levels, sending proactive alerts before assets reach critical operating thresholds.
Real-time sensor analysis identifies abnormal vibrations to prevent equipment failures and improve operational safety.
AI continuously monitors environmental conditions and operational parameters to optimize asset performance and extend equipment lifespan.
These workflows utilize an intelligent chat ecosystem to provide immediate access to enterprise intelligence, bridging the gap between unstructured company documentation and live transactional databases.
RAG-powered AI instantly retrieves accurate answers from company policies, SOPs, and internal documents with source-backed references.
MCP securely connects to live enterprise systems, enabling real-time operational insights through simple natural language queries.

The centralized dashboard provides a granular view of AI performance and operational health.
Visualize which agents (Q&A, Analytics, Maintenance) are most active and their respective accuracy scores.
Real-time monitoring of token consumption versus cached responses (ElastiCache) to show cost savings over time.
An AI-generated metric for each vehicle based on maintenance history and agent-analyzed image reports.
A dashboard view showing where the Evaluator Agent intervened, helping managers see how the Knowledge Base is evolving over time.
Track the “thinking time” of individual agents to optimize model selection (e.g., switching between Nova Lite and larger models).
Automated tracking of AI feature consumption on a per-company basis, enabling precise billing based on token usage and API calls.
A multi-agent system categorizes vehicle status based on driver-uploaded imagery.
The Vision Agent uses Meta Llama 4 Maverick (a natively multimodal model on AWS Bedrock) to analyze visual evidence and produce immediate damage report drafts, significantly reducing the administrative burden on managers.
“Before implementing this platform, my team spent 40% of their day manually coordinating maintenance tickets and verifying compliance. Now, we spend that time on strategic asset planning. The system doesn’t just manage data; it manages our peace of mind.” — Operations Manager, Enterprise Logistics Client
The next evolution of our platform will move beyond automating current workflows to predicting future operational constraints. We are currently developing a “Supply Chain Optimization Agent” that will integrate with external logistics market data to proactively order vehicle parts based on predicted usage patterns, moving us from “Automated Maintenance” to “Predictive Asset Lifecycle Management.”
If your enterprise is struggling with data silos, operational bottlenecks, or compliance risks, this Multi-Agentic platform offers a scalable, secure path to AI-driven autonomy. Contact the Technostacks team for a personalized architecture assessment and platform demonstration.
Have a challenge?Let us know.