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How It Works

Deductive AI uses a sophisticated multi-agent reasoning engine that orchestrates specialized agents to investigate, analyze, and resolve issues across your entire production stack.

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Architecture Overview

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Core Components

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1. Reasoning Orchestrator

The orchestrator is the central intelligence that:

• Parses user queries to understand intent
• Plans multi-step investigations
• Coordinates specialized agents in parallel
• Synthesizes evidence from multiple sources
• Builds causal timelines linking events

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2. Specialized Agents

Each agent is optimized for a specific domain: Code Analysis Agent

• Searches codebases using semantic understanding
• Tracks code changes and their impact
• Analyzes pull requests and commits
• Understands code relationships and dependencies

Metrics Agent

• Queries Prometheus, Datadog, and other metric stores
• Identifies anomalies and trends
• Correlates metrics across services
• Understands service health indicators

Logs Agent

• Searches logs using natural language
• Identifies error patterns
• Correlates log events across services
• Extracts structured information from unstructured logs

Infrastructure Agent

• Monitors Kubernetes clusters
• Tracks AWS resource changes
• Understands infrastructure topology
• Correlates infrastructure events with application behavior

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3. Multi-Hypothesis Reasoning

Instead of pursuing a single theory, Deductive:

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4. Causal Timeline Builder

Deductive builds timelines that link:

• Code Changes - Commits, PRs, deployments
• Infrastructure Events - Scaling, config changes, resource updates
• Telemetry - Metrics spikes, log errors, alert triggers
• External Events - User actions, traffic patterns, third-party changes

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Data Flow

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Learning & Adaptation

Deductive continuously learns from:

• User Feedback - Corrections and confirmations
• Incident Patterns - Historical resolution paths
• Team Knowledge - Runbooks, wikis, chat history
• Production Behavior - Normal vs. abnormal patterns

This learning enables:

• Faster resolution of similar issues
• Better hypothesis generation
• Improved tool usage
• Context-aware recommendations

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Security & Privacy

All data processing happens with strict isolation:

• Customer Data Isolation - Each customer’s data is completely isolated
• In-Context Learning Only - Data is used only to enhance your experience
• No Cross-Training - Your data never trains models for other customers
• Encryption - All data in transit and at rest

Learn more about security

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Next Steps

• Connect Integrations - Set up your data sources
• Explore Use Cases - See Deductive in action
• API Reference - Integrate with Deductive programmatically