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Building enterprise-class AI systems requires careful orchestration of multiple components to ensure quality, reliability, and safety. The diagram below provides a high-level overview of the key components that comprise AI agents.
Sentrix Labs uses Google ADK to build advanced enterprise-class Agentic systems and the diagram below reflects our technology choices.
Security / Auth
Prompts
System Prompt + User Prompt
Input Validation
Guardrails
Orchestrator Agent
Guardrails
Tools
Guardrails
Child Agents
Guardrails
Storage
Session
State
Memory
Artifacts
Workflow Types
Sequential
Parallel
Loop
Reasoning
Custom
Evaluations
Trajectory: Sequence verification
Response: Output verification
Eval Types
Eval Case: Unit Test (Single Session)
Eval Set: Integration Test
Observability
Logging
Metrics
Tracing
Output Validation
Return Response to User
Press enter or space to select a node. You can then use the arrow keys to move the node around. Press delete to remove it and escape to cancel.
Press enter or space to select an edge. You can then press delete to remove it or escape to cancel.
The Core Challenge
Most AI projects fail due to choosing projects that are not well suited for AI or due to poor execution, not due to limitations in AI
AI systems are non-deterministic (Input A → Output ???) which makes small errors cascade unpredictably
Every component must be rock-solid or the entire system fails
Critical Success Factors
1. Pick the Right Project
Align with what AI can actually do today, not vendor promises
Most failures happen before coding even starts
2. Build Infrastructure First (Not AI)
Security: Bulletproof authentication at every entry point, tool, and output
Test Automation: Need comprehensive unit, integration, and end-to-end tests ("good enough" is not good enough, you need rigorous execution)
Evaluations: Absolutely critical. Without evals, zero chance of production success
Trajectory evals: Check if the right steps happen
Response evals: Verify outputs are within acceptable ranges
Observability: Need Google/Netflix-level rigor
Metrics
Structured logging
Tracing is non-negotiable for understanding AI paths
3. Workflow Architecture
Deterministic: Sequential, parallel, or looping (you control flow)
SaaS-level quality won't cut it (low test coverage, basic logging is not enough)
Without proper infrastructure, debugging becomes impossible
Model drift, user behavior changes, and version updates require continuous monitoring
The Success Checklist
Right project selected
Security infrastructure complete
Test automation comprehensive
Evaluation framework built
Excellence in observability
Workflow types understood
Guardrails at every layer
Storage/state properly architected
Bottom Line: Your AI system is only as strong as its weakest component. In non-deterministic systems, weak components cause unpredictable, cascading failures that are impossible to debug.