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Atlas Reasoning Engine

Understand and visualize the "brain" behind Salesforce Agentforce - the six-stage AI processing pipeline that powers intelligent agent responses.

The Problem​

Understanding what happens between sending a query and receiving an AI response is crucial for optimization and debugging.

When working with AI agents, you need to:

  • 🔍 Understand Processing: See how your queries flow through the AI pipeline
  • ⚡ Identify Bottlenecks: Find which stages are consuming time or tokens
  • 🐛 Debug Issues: Trace where problems occur in the reasoning process
  • 📊 Analyze Performance: Monitor token usage, response times, and quality scores
  • 💰 Optimize Costs: Identify opportunities to reduce token consumption

In short: You need visibility into the AI reasoning process to build better, faster, and more cost-effective agents.

How GenAI Explorer Solves It​

Atlas Reasoning Engine visualization provides complete transparency into AI processing:

  1. Six-Stage Visualization: See the entire pipeline from chit-chat detection to safety gates
  2. Live Data Integration: Query Lab runs pre-built queries against your actual Data Cloud
  3. Performance Metrics: See processing time and token usage for each stage
  4. Complete Reasoning Traces: Follow a request through all six stages with full details
  5. Ready-to-Use Queries: 6+ pre-built SQL queries for common analysis tasks
  6. Interactive Gantt Charts: Visual timelines showing processing stages and timing

Think of it as observability for AI - understand what's happening inside the black box.

Overview​

The Atlas Reasoning Engine is Salesforce's proprietary AI orchestration system that processes every query through a sophisticated pipeline. GenAI Explorer provides interactive visualizations and live data integration to help you understand exactly how your AI queries are processed.

What is Atlas?​

Atlas is the reasoning engine that coordinates:

  • Query understanding and intent classification
  • Context enrichment from your Salesforce data
  • Action planning and execution
  • Information retrieval from multiple sources
  • Response generation with citations
  • Safety and quality checks

Think of it as the conductor of an orchestra, coordinating multiple AI models, data sources, and safety systems to produce intelligent, grounded, and safe responses.

The Six-Stage Pipeline​

Processing Timeline (Gantt Chart)​

The following Gantt chart shows the typical timeline and sequence of the Atlas Reasoning Engine's six-stage processing pipeline:

Typical Processing Times:

  • Stage 1 (Chit-Chat Detection): 50-100ms
  • Stage 2 (Query Evaluation): 100-200ms
  • Stage 3 (Context Refinement): 200-500ms
  • Stage 4 (Query Planning & Execution): 300-800ms (Critical)
  • Stage 5 (Advanced Retrieval): 400-1000ms (Critical)
  • Stage 6 (Quality & Safety Gates): 100-300ms

Total Average Processing Time: 1.2 - 3.0 seconds

Detailed Timeline Documentation

For more detailed Gantt charts showing the ReAct loop, parallel execution, and complete lifecycle, see the Stage 4 and Stage 6 documentation.

Pipeline Flow Diagram​

For the complete documentation including all stages, ReAct loops, Data Cloud architecture, Query Lab examples, and more, see the individual stage documentation pages in the sidebar.

Understanding Atlas helps you optimize your AI implementation for better performance, lower costs, and improved user experience.