AI Agent Network

Your autonomous knowledge workforce

Beyond Chat — Autonomous Intelligence

ATLAS doesn't just store knowledge. It deploys six specialized AI agents that work autonomously on your behalf.

These agents aren't chatbots. They're workers with specific roles, capabilities, and outputs.

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The Agent Squad

🔬 Researcher Agent

Role: Deep investigation and analysis

The Researcher takes a topic and produces comprehensive analysis:

• Searches across your knowledge graph

• Identifies relevant concepts and content

• Synthesizes findings into structured reports

• Highlights knowledge gaps

Output: Research reports with citations to your own knowledge

📚 Curator Agent

Role: Knowledge organization and quality

The Curator maintains your knowledge graph health:

• Identifies duplicate or overlapping concepts

• Suggests merges and reorganizations

• Scores content quality

• Recommends archival or deletion

Output: Curation recommendations and quality scores

📝 Reporter Agent

Role: Content generation and synthesis

The Reporter transforms knowledge into outputs:

• Generates summaries and digests

• Creates newsletters from recent additions

• Produces topic-specific briefings

• Writes content drafts

Output: Ready-to-publish content pieces

⚡ Executor Agent

Role: Action management and completion

The Executor works through your action queue:

• Prioritizes pending actions

• Researches background for tasks

• Suggests next steps

• Tracks completion status

Output: Prioritized task lists with context

🧠 Memory Agent

Role: Retrieval and recall

The Memory Agent handles intelligent retrieval:

• Answers questions about your knowledge

• Finds relevant prior content

• Surfaces forgotten insights

• Provides contextual recall

Output: Precise answers with source citations

🛠️ Builder Agent

Role: Project execution (BUILDR ZONE)

The Builder turns ideas into implementations:

• Takes project briefs from your knowledge

• Produces code, documents, or artifacts

• Iterates based on feedback

• Delivers completed outputs

Output: Working implementations and artifacts

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How Agents Work

Context Injection (Not Tools)

Unlike typical AI assistants that call external tools, ATLAS agents receive pre-loaded context from your knowledge graph:

Traditional Approach:
Agent → Calls search tool → Waits → Processes results → Responds

ATLAS Approach:
Knowledge Graph → Injects relevant context → Agent analyzes → Responds

This means:

• Faster responses — No round-trip tool calls

• Better coherence — Full context available from start

• Lower cost — Efficient context window usage

Agent Orchestration

Agents can work independently or coordinate:

Solo Mode: Single agent handles a task

"Research everything I've saved about prediction markets"
→ Researcher Agent → Report

Squad Mode: Multiple agents collaborate

"Prepare a newsletter about my recent knowledge additions"
→ Curator (selects content) → Reporter (writes draft) → Output

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Agent Capabilities

Knowledge Tools

Tool	Function
search_knowledge	Full-text search across your graph
explore_graph	Traverse concept relationships
get_concepts	Retrieve specific concepts
get_actions	Access action queue
get_insights	Retrieve insights by topic

Web Tools

Tool	Function
web_search	Search the broader internet
fetch_url	Retrieve web content

File Tools

Tool	Function
read_file	Access local files
write_file	Create outputs
list_directory	Navigate file systems

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Agent Outputs

Research Report Example

# Research: Network Effects in Platform Business Models

## Summary
Based on 23 content items in your knowledge graph spanning 
2022-2024, here's what you've captured about network effects...

## Key Concepts Found
- Network Effects (importance: 0.94)
- Platform Strategy (importance: 0.87)
- Marketplace Dynamics (importance: 0.82)

## Top Insights
1. "Two-sided marketplaces have chicken-egg problems..."
2. "Winner-take-all dynamics depend on switching costs..."

## Knowledge Gaps
- Limited content on regulation impacts
- No content on network effects in AI/ML products

## Suggested Actions
- Research: "Platform regulation effects on network moats"
- Read: "Aggregation Theory" by Ben Thompson

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Running Agents

Via CLI

# Run researcher agent
atlas agent research "topic: prediction markets"

# Run curator agent
atlas agent curate

# Run reporter agent
atlas agent report --format newsletter

# Check agent status
atlas agent status

Via API

POST /api/agents/research
{
  "topic": "prediction markets",
  "depth": "comprehensive"
}

Via Dashboard

Click the "Agents" tab and select which agent to run with your parameters.

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Privacy & Control

Local First

Agents run on your infrastructure. Your knowledge never leaves your machine unless you explicitly expose it.

Audit Trail

Every agent action is logged:

• What agent ran

• What context was used

• What output was produced

• When it happened

Control Parameters

You decide:

• Which agents can access which knowledge

• How much context agents receive

• Whether outputs are auto-published or reviewed