Simulate Before You Ship. Govern Before You Execute.
AI-generated code is powerful but unverified. ElixirData provides sandboxed execution environments where AI agents test, validate, and simulate code changes against the Context Graph — verifying correctness, security, and compliance before any code reaches production
The Challenge
AI Generates Code Faster Than Humans
AI generates code faster than enterprises can reliably verify, overwhelming traditional review processes and exposing organizations to increased operational, security, and compliance risks
Manual review cannot match AI output speed
Production constraints remain invisible
Unit tests miss real-world workload interactions
Compliance checks occur too late in pipeline
Explore Governance and Transparency
Velocity vs Safety
AI coding agents produce changes faster than manual review can verify, forcing enterprises to choose between speed and controlled safety
Lacks Production Context
AI-generated code is pattern-based and unaware of database limits, API rate limits, memory budgets, and compliance requirements
Incomplete Testing Coverage
Unit tests validate components but fail to verify interaction with production state, real data volumes, and concurrent workloads
No Pre-Deployment Verification
Security and compliance checks occur after commit, with no structural verification before AI-generated code enters the pipeline
How It Works
How AI Agents and Context Graph Enable Safe Code Simulation
ElixirData creates governed simulation environments informed by the Context Graph — where AI-generated code is tested against production-like conditions, verified against policies, and deployed only when all gates pass
Context-Aware Simulation
Simulation environments are configured from the Context Graph: real schema shapes, API contract constraints and data distribution patterns
Production-shaped environments reflecting real infrastructure constraints
Schema-accurate testing ensures compatibility with databases and APIs
Load pattern simulation replicates realistic traffic and concurrency
Outcome: Contract verification ensures correct behavior across all dependencies
Pre-Flight Policy Gates
Before entering any deployment pipeline, policy gates verify security, performance, and compliance constraints
Security policy verification prevents potential vulnerabilities automatically
Performance constraint checks ensure memory and processing efficiency
Compliance requirement validation enforces enterprise data standards
Outcome: Dependency scanning identifies potential risks before production deployment
Governed Deployment Path
Code that passes simulation and policy verification follows a controlled deployment path. AI agents manage staging and rollout with Decision Traces recorded at every step
Staged deployment gradually introduces code to production safely
Canary analysis detects issues before full release
Progressive rollout minimizes risk during system-wide deployment
Outcome: Rollback governance ensures fast recovery after unexpected errors
Capabilities
What Agentic Code Simulations Gets With ElixirData
ElixirData provides governed simulation environments and AI agents that validate, test, and deploy code safely while respecting enterprise policies
Context-Aware Sandboxes
Simulation environments built from the Context Graph replicate production reality, including real schema shapes, API contracts, resource limits, and system configurations
These environments allow AI-generated code to be tested against accurate operational conditions instead of synthetic or idealized assumptions
Test code against real production conditions, not synthetic assumptions
Pre-Flight Policy Verification
Each code change undergoes thorough verification against enterprise security, performance, and compliance policies before entering any deployment pipeline
Non-compliant changes are automatically blocked, preventing unsafe code from reaching production and reducing human review bottlenecks
Non-compliant code is prevented before pipeline entry
Impact Simulation
AI agents simulate the full impact of code changes on the production system graph, mapping dependencies, affected services, and resource load patterns
This process predicts potential SLO violations and highlights which parts of the system may be at risk before deployment
Understand production impact before actual deployment
AI Code Review Agents
Governed review agents analyze every code change against the Context Graph, checking for adherence to established coding patterns, API contracts, and proper error handling
This automated review ensures that AI-generated code maintains quality and behaves consistently with similar production code
Code quality and reliability are verified automatically
Governed Progressive Deployment
Code that passes all simulations and policy gates follows a controlled, authority-bound deployment path with staging, canary, and progressive rollout
AI agents monitor metrics at each stage and automatically rollback any code that degrades performance or violates thresholds
Safe deployment with auto-rollback on performance or metric failures
Simulation Decision Traces
Every simulation run, policy check, and deployment action produces a detailed trace of decisions and validations
These traces create auditable evidence that AI-generated code was thoroughly verified before entering production environments
Complete traceability ensures accountability and compliance for all code changes
Use Cases
Agentic Code Simulations Scenarios
ElixirData tests AI-generated code in production-like environments, verifies policies, prevents errors, and ensures safe deployment
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 Generation
Testing
Security Scanning
Deployment
FAQ
Frequently Asked Questions
Pre-flight gates enforce any structurally verifiable policy, including security, performance, compliance, and architectural standards to ensure safe and reliable code
Within governance boundaries, agents can auto-approve safe staging deployments. Canary and progressive rollouts require higher authority levels, while full production deployments need human approval with full simulation, policy, and risk evidence
ElixirData validates AI-generated code through sandbox simulation and policy gates. The Context Graph feeds back context to improve future code quality
The AI agent reports test failures, policy violations, and expected versus actual behavior, suggests remediation, and can request code regeneration. All results are recorded in Decision Traces
Ready to Transform Agentic Code Simulations?
See how ElixirData's Context OS and AI agents deploy over your existing agentic code simulations stack in 4 weeks