Atlas AI
Our decision engine for autonomous analysis.
Atlas is Online
Version 2026.2.24 — Running autonomously
Overview
Atlas is our core AI engine that powers BenchmarkMD's autonomous analysis capabilities. It combines large language models with rule-based systems to provide accurate, evidence-based analysis of AI agents and their performance.
Features
- Persistent Memory — Remembers previous analyses and builds on them
- Multi-agent Coordination — Can orchestrate multiple sub-agents
- Real-time Threat Detection — Identifies risks in AI deployments
- Cost Optimization — Recommends ways to reduce AI spending
- Quality Scoring — Evaluates code and output quality
Architecture
Atlas uses a hybrid approach combining:
- LLMs — For reasoning and analysis
- Rule-based Systems — For deterministic tasks
- Feedback Loops — Continuous improvement
- Vector Storage — Semantic memory
Capabilities
| Capability | Status | Description |
|---|---|---|
| Code Analysis | ● Active | Static and dynamic code analysis |
| Cost Tracking | ● Active | Real-time API cost monitoring |
| Threat Detection | ● Beta | Anomaly detection in agent behavior |
| Report Generation | ● Active | Automated benchmarking reports |
Integration
from benchmarkmd import Atlas
atlas = Atlas()
# Ask Atlas
response = atlas.analyze("What is the best AI coding agent?")
print(response)Chat with Atlas
You can chat with Atlas directly from the website using the chat widget in the bottom-right corner. Atlas can help you with:
- Questions about AI agents
- Recommendations for your use case
- Cost optimization advice
- Analysis of your AI setup
Roadmap
- Q1 2026 — Enhanced cost analysis
- Q2 2026 — Multi-agent orchestration
- Q3 2026 — Custom agent training
- Q4 2026 — Enterprise features