NemotronOps: Multi-Agent Autonomous Enterprise Assistant

Multiagent enterprise assistant engineered to automate complex multistep workflows from automated web research and code execution to asynchronous background report synthesis without human intervention

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Inspiration

We wanted to eliminate the friction of navigating complex enterprise dashboards by bringing agentic AI directly to the everyday messaging channels people already use, such as WhatsApp and email. By turning chat threads and inbox messages into active control interfaces, routine tasks like checking status or querying data become completely effortless.

What it does

The platform connects autonomous AI agents directly to WhatsApp and email, allowing users to execute multi-step workflows—such as querying databases, triggering automated tasks, or processing records—using simple natural language messages.

How we built it

Ingestion Layer: Configured WhatsApp endpoints via AWS End User Messaging Social and email parsing via Amazon SES Orchestration: Deployed autonomous agentic control loops using Amazon Bedrock and AWS Lambda Storage & Security: Managed session state in Amazon DynamoDB and logged artifacts to Amazon S3, with all entry points secured via Amazon CloudFront and AWS WAF.

Challenges we ran into

​State Normalization: Synchronous API requests and multi-step email threads required an event-driven session manager in DynamoDB to maintain user context cleanly across asynchronous channels.
​Payload Formatting: Stripping unstructured HTML signatures from inbound SES emails while complying with WhatsApp template rules required custom string-sanitization adapters.
​Execution Latency: Optimizing prompt sizing and tool schemas was necessary to keep total response latency low for live messaging interactions.

Accomplishments that we're proud of

Successfully implemented a production-ready SES setup using the modern @aws-sdk/client-ses v2 client.
​Integrated AWS End User Messaging Social for real-time, interactive conversation and tool execution on WhatsApp.
​Generated the required Partner Central ACE Opportunity ID (O2026102809142) aligned with the hackathon marketing campaign.

What we learned

Mastering the orchestration of multi-step, asynchronous agentic workflows within transient serverless environments is crucial for maintaining messaging responsiveness.
​If an AI agent is reliable, a simple messaging interface is often preferred by users over a complex web frontend.
​Securing third-party hooks using AWS WAF and CloudFront ensures robust validation before triggering any backend execution.

What's next for NemotronOps: Multi-Agent Autonomous Enterprise Assistant

Expanded Model Tiering & Local Fallbacks: ​Integrate upcoming NVIDIA Nemotron model iterations on Nebius Token Factory to further optimize cost-per-query. ​Add local edge fallback using NVIDIA TensorRT-LLM on Jetson devices for zero-latency execution when network connectivity is lost. ​Advanced Multi-Modal Tooling: ​Upgrade the research agent from text-only retrieval to processing structured charts, technical diagrams, and visual enterprise reports using multi-modal open-source models. ​Deepen Tavily Search API integration with domain-specific web filtering and automated source credibility scoring. ​Enterprise Security & Granular RBAC: ​Introduce role-based access control (RBAC) to ensure agent actions (like triggering write operations to internal databases) are strictly bounded by user permissions. ​Implement automated safety and guardrail layers using NVIDIA NeMo Guardrails to sanitize input prompts and prevent data leakage. ​Production Deployment & Scaling: ​Graduate background jobs from prototype scripts to high-throughput, auto-scaling asynchronous pipelines using Nebius Serverless Jobs. ​Open-source core agent modules to the community to foster collaborative tool-building across the NVIDIA and Nebius developer ecosystems.

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