AI Agents

What Are AI Agents?

AI Agents are specialized, customizable AI assistants built on top of Nova that you can train with your own domain-specific knowledge. Unlike Nova’s general-purpose scientific capabilities, each agent becomes an expert in your specific research area by learning from the documents, papers, and data you provide.

Think of it this way:

• Nova: A knowledgeable scientific generalist ready to help with any research task
• AI Agents: Domain experts you create—a LAMMPS specialist, a genomics pipeline expert, or a quantum chemistry advisor—each trained on your specific documentation

Prerequisites: Active CamberCloud account with platform access. No coding experience required, though familiarity with your research domain is helpful.

Core Capabilities

AI Agents combine Nova’s computational power with your domain expertise through three key capabilities:

1. Custom Knowledge Bases

Build agents that understand your research context:

• Document ingestion: Upload PDFs, papers, manuals, and technical documentation
• Multi-source integration: Connect to URLs, Stash files, Google Drive, SharePoint, and Amazon S3
• Automatic indexing: Retrieval-Augmented Generation technology processes and indexes your documents
• Contextual retrieval: Agents automatically find and reference relevant sections from your knowledge base

2. Specialized Instructions & Behavior

Configure how your agent thinks and responds:

• Custom system prompts: Define your agent’s role, expertise, and communication style
• Workflow guidance: Embed standard operating procedures and best practices
• Domain-specific reasoning: Train agents to approach problems using your field’s methodologies
• Team-shared expertise: Create consistent expert assistants accessible to your entire research group

3. Seamless Nova Integration

Access your agents directly within Nova conversations:

• @mention invocation: Call agents using @username.agent_alias syntax
• Contextual responses: Agents automatically retrieve relevant knowledge while answering
• Transparent citations: See which documents informed each response
• Full platform access: Agents inherit Nova’s capabilities for job management, file operations, and app launching

How to Use AI Agents

Step 1: Create Your Agent

1️⃣ Navigate to the AI Agents section in the platform sidebar 2️⃣ Click “Create Agent” to open the configuration dialog 3️⃣ Define your agent’s identity:

• Agent Name: Human-readable name like “LAMMPS Simulation Expert”
• Agent Alias: Short handle for @mentions like “lammps_agent”
• Description: Explain what the agent does and when to use it
• Instructions: Write the system prompt that defines your agent’s expertise and behavior. Keep in mind that longer prompts do not necessarily improve performance, and it’s best to keep the prompt length to several thousand characters.

Step 2: Build the Knowledge Base

1️⃣ Select the “Knowledge Base” tab on your agent’s page 2️⃣ Click “Add Resources” to choose your integration method:

• Add from URL: Directly ingest web-hosted documentation
• Add from Stash: Use files already in your Stash workspace
• Add from Google Drive: Connect to shared team documentation
• Add from SharePoint: Access enterprise knowledge repositories
• Add from Amazon S3: Integrate cloud-stored research data
• Supported File Formats: pdf, docx, txt, md, xlxs , csv

Monitor indexing status—documents move from “Processing” to “Indexed” when ready. 3️⃣ Deploy the agent

Step 3: Invoke Your Agent

Open a Nova chat, type @ to view available agents, select your agent or continue typing its alias, then ask your question: @username.agent_alias how do I configure a LAMMPS NVT ensemble?

Step 4: Review Knowledge-Enhanced Responses

The agent responds using both its base knowledge and your custom documentation.

Agent Use Cases

Domain-Specific Research Assistants

1. Molecular Dynamics Expert Scenario: Your lab runs frequent LAMMPS simulations but team members have varying expertise levels.

Solution: Create a LAMMPS agent trained on:

• Official LAMMPS documentation
• Your lab’s simulation protocols
• Published papers using your methods
• Troubleshooting guides from past projects

Benefits: New students get expert guidance instantly; consistent methodology across the team.

2. Genomics Pipeline Specialist Scenario: You’ve developed custom bioinformatics workflows with specific tool configurations.

Solution: Build an agent with knowledge of:

• Your pipeline documentation
• Tool-specific parameter explanations
• Quality control thresholds and interpretation
• Common error resolutions

Benefits: Team members can query best practices without interrupting senior researchers.

3. Quantum Chemistry Advisor Scenario: Complex quantum chemistry calculations require choosing between multiple methods and basis sets.

Solution: Create an agent informed by:

• Computational chemistry textbooks
• Benchmark studies for your molecule types
• Lab-specific computational resources and constraints
• Published methodologies from your field

Benefits: Make informed method decisions with guidance grounded in authoritative sources.

Best Practices

Building Effective Knowledge Bases

1. Start with authoritative sources: Official docs, peer-reviewed papers, textbooks
2. Add lab-specific context: Standard operating procedures and internal protocols
3. Keep it current: Update regularly with new findings

Writing Effective Instructions

Do: Clearly state domain expertise, specify terminology conventions, define when to defer to humans
Don’t: Use overly broad instructions or assume unstated lab conventions

Agent Management

• Deploy incrementally: Test with a few documents before adding your entire library
• Share across teams: Grant access to colleagues in the same domain
• Iterate: Refine instructions based on how the agent performs

Related Documentation

• Nova AI Assistant - Interactive AI for general scientific computing
• Apps Catalog - Applications that agents can launch and configure
• Stash File System - Store agent knowledge base documents
• Job Attributes Reference - Configure compute resources for agent-launched jobs
• Data Connectors - Integrate external data sources into agent workflows

Getting Started

Ready to build your first specialized research assistant?

1. Identify your domain: What specific expertise do you need to encode?
2. Gather resources: Collect 3-5 key documents to start your knowledge base
3. Create your agent: Define its role and upload your initial documents
4. Test and refine: Try example questions and adjust instructions as needed
5. Share with your team: Grant access and gather feedback

Transform your hard-won expertise into an AI assistant that’s available 24/7. Create your first agent today.

Need help? Join our Slack community or contact support.