Debug With Full Context — Not Just Stack Traces
Debugging is a context problem. Engineers spend 70% of debugging time understanding the system. ElixirData's Context Graph gives AI debugging agents the full codebase context and system state — so they identify root causes in minutes, not hours
The Challenge
Debugging Fails Because AI Agents Can't See the Full System
AI debugging fails because assistants lack full system context, runtime insight, and historical bug patterns
File-level context is insufficient for system-wide issues
Execution context is missing during runtime analysis
AI lacks overall system awareness
Historical patterns of bugs are ignored
See How Agentic Debugging Works
File Context
AI assistants see only the file under investigation, missing changes in deployment, updated configurations, or upgraded dependencies
Execution Context
AI analyzes code statically and cannot observe runtime values, executed branches, or external service responses during failures
Historical Patterns
Recurring bugs are invisible without a Context Graph, forcing AI to start every investigation from scratch
System Awareness
Debugging fails when AI cannot combine file, execution, and historical context for accurate root cause analysis
How It Works
How AI Agents and Context Graph Transform Debugging
ElixirData compiles codebase structure, execution traces, deployment history, and debugging patterns into a Context Graph that AI debugging agents reason over
Code Context Graph
Maps the complete codebase structure including module dependencies, API contracts, data flow paths, recent changes, test coverage, and known issues
Cross-service dependency relationships across all modules
Change history correlation across all deployments
API contract tracking across multiple services
Outcome: Test coverage and known issues mapped consistently
Governed Diagnostic Agents
Debugging agents operate within strict boundaries. Read-only production analysis auto-approves, while state-changing actions require authorization
Read-only production system access for safe debugging
Governed log and trace collection across systems
State modification actions require explicit approval
Outcome: Evidence and audit trails preserved for compliance purposes
Diagnostic Decision Traces
Every hypothesis tested and every root cause identified produces a trace. This builds a knowledge base of debugging patterns that future agents can search
Diagnostic step recording for every action
Root cause evidence captured for failures
Hypothesis tracking for all debugging scenarios
Outcome: Pattern knowledge base enables faster resolution of recurring issues
Capabilities
What Agentic Debugging Gets With ElixirData
ElixirData empowers AI agents to trace, analyze, and resolve bugs across services using the Context Graph. Agents connect code, configuration, runtime execution, and historical debugging patterns to systematically identify root causes, prevent recurring issues, and accelerate safe production fixes
Cross-Service Root Cause Analysis
AI agents trace bugs across service boundaries, identifying upstream causes like timeouts, connection pool issues, or schema changes
They analyze dependencies across modules and services to ensure no cause is overlooked
Identify root causes spanning multiple services automatically
Change Correlation
Agents query the Context Graph for all changes in affected dependencies, including commits, config updates, and infrastructure modifications
This correlation helps pinpoint the exact changes likely responsible for the reported failure
Quickly pinpoint relevant code or system changes causing failures
Hypothesis-Driven Investigation
Agents formulate hypotheses from Context Graph patterns and test them systematically against execution data
Each hypothesis is validated or eliminated, with every step recorded in a Decision Trace for transparency
Systematic debugging with full step-by-step recording
Debugging Precedent Search
Search the knowledge base of past debugging sessions for recurring errors, resolutions, and root causes
AI agents learn from organizational history to speed up investigation and prevent repeated mistakes
Reuse historical solutions to speed up issue resolution
Execution Context Compilation
Agents compile runtime context, including request traces, logs, metric anomalies, and error patterns
They correlate this data with code paths and system changes to fully understand failures
Runtime context provides complete insight into failures
Bug Pattern Analytics
Aggregate analysis across sessions identifies recurring root causes, fragile modules, and change-correlated failure patterns
This helps teams proactively address high-risk modules and prevent future system failures
Recognize high-risk modules and patterns to prevent future bugs
Use Cases
Agentic Debugging Scenarios
ElixirData enables AI agents to investigate production bugs, detect regressions, manage cross-team handoffs, and prevent recurring failures using the Context Graph and Decision Traces
Integrations
Connects to Your Existing Stack
ElixirData seamlessly integrates with the tools your development teams already use, including code generation, testing frameworks, security scanners, and deployment platforms
Code Platforms
Observability
Issue Tracking
CI/CD
FAQ
Frequently Asked Questions
Within governed boundaries, read-only diagnostics auto-approve. State-changing actions require proper authority, while production modifications escalate to humans
Decision Traces record all diagnostic steps and root causes. Agents search past patterns to quickly present previous resolutions and evidence
ElixirData complements APM tools like Datadog and New Relic, adding code context, change correlation, debugging history, and governed workflows
The Context Graph identifies recurring bugs and recommends tests, monitoring, architectural changes, or safeguards with supporting evidence
Ready to Transform Agentic Debugging?
See how ElixirData's Context OS and AI agents deploy over your existing agentic debugging stack in 4 weeks