SECTION A-1: OVERVIEW
The Simulation Environment for Agentic Systems.
- Deterministic simulation environments
- Generates realistic synthetic data
- Tests agents end-to-end in isolation
FIG 1.0 // ARCHITECTURE
Synth_Data
Orchestrator
Sandbox_A
Sandbox_B
Project ID: FABRIK_SIMULATION_ENV
FABRIK
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REV: 0.0IN DEVELOPMENT
Technical Specifications: Hard Problems
P-01
REALISTIC SYNTH_DATA
Generating high-fidelity user interactions that cover edge cases without leaking PII.
P-02
CONTEXT PRESERVATION
Maintaining state coherence across multi-step agent workflows for accurate replayability.
P-03
DETERMINISTIC REPLAY
Ensuring that identical inputs yield identical execution paths, independent of external API entropy.
COMPARATIVE ANALYSIS // REVISION HISTORY
REF: TRADITIONAL_EVALS VS. FABRIK_PLATFORM
Depreciated (Traditional)
- Manual QA
Slow, unscalable human review of logs.
- Ad-hoc Scripting
Brittle Python scripts that break on schema changes.
- Production Testing
Testing in prod creates risk and corrupts data.
Current Standard (Fabrik)
- Automated Simulation
Parallel execution of thousands of user scenarios.
- Deterministic Environment
Sandboxed runtimes with full network mocking.
- Governance & Audit
Golden datasets and regression testing built-in.
SYSTEM VISION
Fabrik becomes the system of record for AI behavior. A unified trust layer for the agentic future.
Read Our ResearchROADMAP_ARC.DWG
PHASE_1
Deterministic Simulation Environments
Complete isolation infrastructure for reproducible agent workflow testing with mocked tools and controlled scenarios.
PHASE_2
Runtime Simulation Layer
Real-time simulation environments that mirror production conditions, enabling pre-deployment failure detection.
PHASE_3
Universal Simulation Standard
Open framework for simulating any agent workflow across any environment, establishing reproducibility as the industry foundation.
Simulation Viewport
Workflow Steps
1
fetch_order
2
validate_refund
3
process_refund
4
send_confirmation
Results
Log
Interactive Demo
Walk through how Fabrik catches silent agent failures that traditional evaluation misses.
How Fabrik Works
Fabrik simulates your agent workflows before production, catching failures that step-level evals miss.
1
Phase 1
Discovery → Workflow Spec
Index the repo, build a knowledge graph, and generate a simulation spec: entry points, required mocks, data needs, and the simulation matrix.
2
Phase 2
Preparation & SDK Integration
Fabrik proposes changes per file-group; you review/approve; Fabrik implements and validates—repeat until integration is complete.
3
Phase 3
Synthetic Data → Simulations
Auto-generate test data from workflow requirements and run persona × workflow simulations—tracking success rate, errors, and external/mock interactions.
Continuous iteration and improvement
1
Discovery → Workflow Spec
Phase 1
Index the repo, build a knowledge graph, and generate a simulation spec: entry points, required mocks, data needs, and the simulation matrix.
2
Preparation & SDK Integration
Phase 2
Fabrik proposes changes per file-group; you review/approve; Fabrik implements and validates—repeat until integration is complete.
3
Synthetic Data → Simulations
Phase 3
Auto-generate test data from workflow requirements and run persona × workflow simulations—tracking success rate, errors, and external/mock interactions.
Continuous iteration
Pre-production
All simulation occurs before deployment
Workflow-level
End-to-end execution, not isolated steps
Deterministic
Every run is reproducible and traceable
This architecture is required because agent failures are emergent properties of multi-step workflows under variable conditions—they cannot be detected by testing individual components in isolation.
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