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AI & Models · production tested · 19 components

AI Application

From idea to production. An AI application inside a real organisation, drawn end to end, so you can read it as data, networking, security, identity, integration, compute, operations and cost.

Pick a lens to read it as one discipline. Select a component to see what it means and what is written about it.

StackDetail

How to read this

Each box is a role. Pick a lens to see the system as a networking, data, security or identity problem. Pick a stack, or mix stacks per component, to see the product that fills each role.

Request
Async or batch
Telemetry and audit
Trust boundary
Cross-platform seam
Seam not drawn as a line

Adds identity carried end to end, secrets in a vault, an audit record for every call, and real systems of record behind the tools.

Components

What an AI application is

Strip away the demos and an AI application is a normal enterprise system with three new parts: a model you do not control, retrieval that feeds it context, and tools it is allowed to call. Everything around those three is engineering you already know: networks, databases, identity, integration, operations and cost.

That is why the map above has lenses. Pick one and the same system turns into a networking problem, a data problem or an identity problem, with the components that matter for that discipline lit up.

The shape that survives production

Edge and API gateway own the way in: TLS, WAF, authentication, rate limits.
Agent decides what happens next. In most systems it is a supervisor with a small set of specialist workers.
LLM gateway is the only way out to a model: routing, budgets, redaction and one place to trace every call.
Retrieval supplies the knowledge the model was never trained on, filtered by who is asking.
Ingestion keeps that knowledge fresh from the systems that own it.
Tools are typed MCP servers with explicit permissions in front of real enterprise APIs.

Where teams get hurt

Observability arrives too late, evaluation is a spreadsheet, identity stops at the web app, and the deployment story is "it runs on my laptop". The guides attached to each component address these in order.

Node by node

Web App

Frontend3 linked

Understand

ArticleA capable model is not a product yet

Implement

Build logBuild log: shipping Unseen UI to npm

Use

ToolUnseen UI

Open Web App on the map

Identity

SSO · OBO2 linked

Understand

ExplainerYour agent should not be a superuser

Operate

ChecklistSecurity review checklist for an AI feature

Open Identity on the map

API Gateway

AuthN · limits4 linked

Understand

ExplainerYou are already building a control plane, badly

Operate

ArticleWhat an AI gateway actually costs to run

Use

ToolOpenTelemetry

Reference

ReferenceOpenTelemetry Documentation

Open API Gateway on the map

Secrets

Vault · KMS1 linked

Operate

ChecklistSecurity review checklist for an AI feature

Open Secrets on the map

Agent

Orchestration34 linked

Understand

ExplainerAnswer complex questions in parts, and check each part
ExplainerReflection helps only when it checks against evidence
ExplainerGive an agent an outcome, a boundary and a stop condition first
ExplainerAgent behavior is a loop, not a prompt
ExplainerPlan before acting when actions cost money or carry risk
19 more on the map

Implement

Decide

ComparisonChatbot, workflow or agent: choose by how much autonomy the task can bear
ComparisonMost teams reaching for multiple agents do not need them
ComparisonMost agents are workflows wearing a costume
ComparisonLangGraph vs the OpenAI Agents SDK

Use

ToolOpenAI Agents SDK
ToolLangGraph

Reference

ReferenceBuilding Effective Agents

Open Agent on the map

Audit

Every call7 linked

Understand

ExplainerAgents need arbitration rules before they disagree
ExplainerGovernance is architecture, not a document after launch
ExplainerYou cannot instruct a model into data protection
Architecture patternPattern: the outbox for agent actions

Operate

ChecklistSecurity review checklist for an AI feature
ChecklistWhen to bring in a compliance review
ArticleWhat an AI gateway actually costs to run

Open Audit on the map

LLM Gateway

Route · budget10 linked

Understand

Open LLM Gateway on the map

Observability

Traces · evals18 linked

Understand

ExplainerEvaluate RAG in parts: retrieval, grounding, generation, citations
ExplainerIf you cannot trace it, you cannot run it
ExplainerEvaluate the workflow users see, not just the model
ExplainerThe 3am agent run that nobody is watching
ExplainerYou cannot run RAG on user complaints
11 more on the map

Use

ToolOpenTelemetry

Reference

ReferenceOpenTelemetry Documentation

Open Observability on the map

Model

Provider API13 linked

Understand

ExplainerModel choice is rarely your first production problem
ExplainerLLMs generate one token at a time, so design for it
ExplainerTokenization quietly sets your cost, limits and reliability
ExplainerMore context can make an AI system worse
ExplainerVague prompts become vague systems
3 more on the map

Implement

ChecklistWorking with Claude, practices that hold up

Operate

ChecklistWhen to bring in a compliance review

Use

ToolLiteLLM
ToolGuardrails

Reference

ReferenceAttention Is All You Need

Open Model on the map

Sources

Docs · tickets · DBs5 linked

Understand

ExplainerRetrieval quality is decided before anyone searches
ExplainerProduction knowledge lives in systems of record, not only in PDFs
ExplainerA stale answer is a wrong answer
ExplainerYour vector index does not know who is allowed to read it

Operate

ChecklistWhen to bring in a compliance review

Open Sources on the map

Ingestion

Chunk · embed8 linked

Understand

ExplainerRetrieval quality is decided before anyone searches
ExplainerChunk boundaries decide what your RAG system can know
ExplainerSimilar is not the same as correct: the limits of embeddings
ExplainerWhen the evidence is a table, a chart or a recording
ExplainerA stale answer is a wrong answer
3 more on the map

Open Ingestion on the map

Vector Index

Embeddings · hybrid4 linked

Understand

ExplainerSimilar is not the same as correct: the limits of embeddings
ExplainerA vector store is infrastructure, not magic memory
ExplainerGood RAG needs the precise snippet and the context around it
ExplainerSome answers live in relationships, not documents

Open Vector Index on the map

Retrieval

Hybrid · rerank23 linked

Understand

ExplainerConfidence is not evidence
ExplainerRAG is an evidence design problem
ExplainerProduction retrieval needs exact words and meaning
ExplainerHybrid search is the practical default
ExplainerRelevant data can still be data the user may not see
12 more on the map

Operate

ChecklistRAG production-readiness checklist
Failure storyThe retrieval cache that served stale policies

Use

Reference

ReferenceRAG at Scale: Architecture and Lessons

Open Retrieval on the map

Tools

MCP servers20 linked

Understand

Decide

Operate

ChecklistSecurity review checklist for an AI feature

Use

ToolInstructor

Reference

ReferenceModel Context Protocol
ReferenceOWASP Top 10 for LLM Applications

Open Tools on the map

Enterprise APIs

ERP · CRM · ITSM2 linked

Understand

ExplainerAn approve button is not human oversight

Decide

ComparisonWhen MCP earns its overhead, and when a direct API is better

Open Enterprise APIs on the map

Memory

Session · long-term6 linked

Understand

Open Memory on the map

Human review

Approval queue3 linked

Understand

ExplainerHuman review belongs where it has timing, authority and context
ExplainerAgents need arbitration rules before they disagree
ExplainerAn approve button is not human oversight

Open Human review on the map

On this map