From POC to Production: Architecture for AI Agents

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.

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)
• Non-deterministic: Reasoning workflows (AI controls flow)
• Hybrid: Most production systems combine both types

4. Core System Components

• System prompts (your control center)
• Input/output validation
• Orchestrator and child agents with guardrails
• Proper storage hierarchy: Session, Memory, Artifacts

5. Common Failure Points

• 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.