Enterprise AI Architecture Library

Reference architectures for production Agentic AI, RAG, and LLM platforms — each with the diagram, the design decisions, and the trade-offs behind it. Drawn from systems that shipped, not whiteboards.

• Agentic AIReActMemory

  Production Agentic AI Architecture

  

  The six-layer anatomy of a single production agent — context engineering, a ReAct core, standardized tool connectivity, a self-critique gate, bounded human oversight, and a memory read/write cycle that compounds capability over time.

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• RAGKnowledgeSecurity

  Enterprise RAG Architecture

  

  Grounding LLM answers in company knowledge at enterprise scale — with access control enforced at retrieval, an evaluation harness, freshness pipelines, and cost controls treated as first-class architecture.

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• Document AIOCR + LLMAutomation

  AI Document Processing Architecture

  

  Turning invoices, POs, and receipts into validated, posted transactions — OCR plus schema-constrained LLM extraction, business-rule validation, a confidence router, and a human review queue whose corrections compound accuracy over time.

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• Text-to-SQLKnowledge GraphAnalytics

  Text-to-SQL with Knowledge Graph

  

  Letting non-technical users query business data in plain English — grounded in a knowledge graph of business concepts, rules, and aliases, so the model reasons from definitions instead of guessing at column names.

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• Agentic AIGovernanceHITL

  Human-in-the-Loop Agent Architecture

  

  Bounding agent autonomy by design — a policy gate that decides when the agent acts alone and when a human approves, with every decision feeding a trust dial that widens autonomy on evidence, not optimism.

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• Agentic AICloudStreaming

  Durable AI Agent Pipeline

  

  A pluggable, streaming gateway for AI agents built entirely on managed services — user input survives any failure, turns dispatch exactly once, tokens stream live, and onboarding a new agent is a registry entry, not a deployment.

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• Agentic AIOrchestrationGovernance

  Multi-Agent Enterprise Architecture

  

  Coordinating specialized AI agents under a central orchestrator to automate complex business workflows — with governance, memory, and human oversight built in from day one.

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• Agentic AIEvalsQuality

  Agent Evaluation Architecture

  

  Measuring whether an agent actually works — layered deterministic checks, golden datasets, judge models, and trajectory scoring, wired into CI and production so quality regressions are caught before users find them.

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• SecurityGuardrailsGovernance

  AI Guardrails & Security Architecture

  

  Keeping LLM systems safe to expose to real users and real data — input defenses, permission boundaries the model cannot cross, output validation, and least-privilege tool scoping, all built on one principle: the model is untrusted.

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• GovernanceFinOpsPlatform

  Enterprise AI Governance & Cost Architecture

  

  Running many AI use cases without losing control of risk or spend — one gateway all traffic flows through, a registry of approved models and prompts, risk-tiered policy, and cost treated as architecture instead of an invoice surprise.

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• PlatformDeliveryGovernance

  Pre-Approved AI Delivery Architecture

  

  Shrinking AI ideation-to-production from quarters to weeks — a platform pattern selected before demand arrives, a service stack approved once under stated guardrails, and a delivery pipeline where prompts and agent configs ship like code.

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