AI Systems Atlas
A visual map of how production AI systems actually work.
This is the public explainer layer of my portfolio: agent routing, RAG, MCP tool contracts, caching, queues, vector search, context optimization, Kubernetes scaling, databases, observability, and the data flows that turn a model call into a real software system.
Public, non-confidential examples
All diagrams and examples are generalized, recreated, and sanitized for public demonstration. They do not include proprietary code, internal documents, private data, or confidential implementation details.
User asks
Knowledge returns evidence
Agents take safe tool actions
Ops can debug every step
Full Software System
The LLM is only one box. The product is the whole loop around it.
A serious AI product still needs frontend state, backend APIs, auth, databases, caching, queues, retrieval, tool permissions, model routing, observability, evals, and rollout controls. The model is powerful, but the system makes automation dependable.
public.system.map
Click Any Box
System detail
Think of this as the front door where a user gives the system a job.
User asks
System box
Useful result
Incoming user request.
The system validates, routes, retrieves, executes, and logs.
Traceable output for the user or operator.
trace.viewer
> request_id: req_8fa2
> route: rag_only | bounded_agent | async_job
> evidence: 5 chunks | tools: 1 approved | cost: tracked
> eval: grounded=true | rollback_ready=true
I use agent patterns when a system must plan steps, inspect intermediate results, call tools, retry safely, or coordinate long-running work. If retrieval alone solves it, I keep it RAG-only.
When the stack allows it, I use graph-style state machines for planner-executor, evaluator, human-review, and recovery paths with max steps and typed state.
Tool contracts make it clearer what an agent can call, what input shape is required, what permissions are needed, and what audit trail must be preserved.
Redis can cache embeddings, retrieved context, session state, tool results, rate-limit counters, and hot read models so the system does not recompute everything.
Kafka or queue-based systems help with fan-out, retries, replay, backpressure, dead-letter handling, and workflows that continue after the browser tab closes.
Embeddings turn text into numeric vectors. A vector DB finds semantically similar chunks; filters, permissions, versions, and citations make those matches safe to use.
Vector search catches meaning. Keyword/BM25 catches exact terms, IDs, acronyms, SKUs, ticket numbers, and policy names. Reranking combines both signals.
Good systems compress history, prune irrelevant chunks, summarize tool outputs, preserve citations, and keep only the evidence the model needs to answer.
Kubernetes pods, read replicas, partitioned databases, caches, queues, bulkheads, and rate limits all prevent one bottleneck from taking down the whole product.
Concept Deep Dive
Agents
Click any concept above to see the production version of how it works.
Input
System
Guardrails
Output
A user asks for a task, answer, or workflow.
The system routes, validates, executes, and observes the work.
The user gets a result with evidence and traceability.
It turns an AI demo into a dependable product behavior.
Use contracts, permissions, evals, logs, rollbacks, and operator visibility.
Dry Run 01
Document ingestion: from PDF in object storage to searchable knowledge.
This is the path that turns messy files into reliable RAG memory. The important idea: the vector DB is not the starting point. The pipeline before it decides whether answers are accurate, searchable, permission-safe, and debuggable.
Example Policy Guide v7.pdf
Click a Stage
Ingestion detail
Think of this as a factory line that turns a messy file into clean evidence cards.
Raw file
Ingestion stage
Searchable evidence
A raw file enters the ingestion system.
The pipeline extracts, cleans, chunks, embeds, and indexes it.
Searchable evidence with metadata and citations.
Plain-English Version
A PDF goes in. Evidence cards come out.
Instead of asking the model to read a whole messy PDF at question time, the system prepares small trusted evidence cards ahead of time, then retrieves only the most relevant cards when a user asks.
Example Policy Guide v7.pdf with tables, headings, footers, and role-based access rules.
Refunds over $500 require manager approval within 24 hours.
chunk_id=c_77, page=4, section=Approvals, tokens=212
vector=[0.12,-0.44,...], keywords=[refund, approval], ACL=support
Dry Run 02
Agentic flow: a user asks the system to create a Jira ticket.
Agents become valuable when the request is not just “answer a question.” Here the system must understand the goal, check permissions, gather context, choose a tool, call it safely, and show the user exactly what happened.
"Create a Jira ticket"
Click a Stage
Agent detail
Think of this as a careful robot coworker that checks rules before it touches tools.
User goal
Agent stage
Completed action
A user asks for a task, not just an answer.
The agent plans, retrieves context, validates tools, and executes safely.
A completed action with link, evidence, and audit trail.
Why Not Just RAG?
RAG answers. Agents coordinate work.
RAG can say how to create a ticket. An agent can create it after policy checks, context retrieval, schema validation, and user-safe confirmation.
Create a Jira ticket for checkout failures on iOS. Severity high.
[classify, retrieve_runbook, draft_payload, call_jira, confirm]
{project:"PAY", severity:"high", owner:"payments", idempotency_key:"req_8fa2"}
Created PAY-1842. Evidence: runbook v12. Trace: agent_91bc.
Scaling Notes
The “large systems” pieces that make AI work under real traffic.
These are the parts I think about when a demo has to become a product: where pressure builds, where data lives, where latency hides, and how operators keep control.
API and worker pods can scale horizontally with CPU, queue depth, latency, or custom model-serving metrics. The trick is keeping state outside the pod.
Use indexes for access patterns, replicas for read-heavy traffic, partitions for large tables, and migrations that do not lock the product during peak usage.
Events let services move at different speeds. Replay, dead-letter queues, consumer groups, and idempotency turn failure into recoverable work.
Cache retrieval results, model responses when safe, user sessions, feature flags, tool output, rate limits, and expensive intermediate computations.
That revolution is real, but production search still needs ACL filters, metadata, dedupe, reranking, citations, and index version control.
Compress chat history, retrieve fewer better chunks, summarize tool output, remove duplicate facts, and reserve tokens for the final answer.
Combine vector search with keyword search for tickets, names, error codes, policy numbers, and rare terms. Then rerank for answer quality.
A production AI trace should connect user request, retrieved chunks, prompt, tool calls, model output, cost, latency, eval score, and user outcome.
Click a Scale Concept
Kubernetes
Think of Kubernetes as adding more checkout lanes when the store gets crowded.
Traffic spike
Kubernetes
More healthy pods
Traffic spike: API requests and worker jobs increase faster than one server can handle.
Run stateless app code in pods, scale replicas horizontally, restart unhealthy pods, and keep state outside the pod.
More healthy workers handle the load without one machine becoming the bottleneck.
Plain-English Version
Scaling is pressure management.
Large systems stay alive by moving pressure to the right place: pods handle compute, queues absorb bursts, caches protect hot paths, replicas serve reads, and traces make the whole machine understandable.
Operating Principle
My default question: what breaks when this becomes real?
Can it be evaluated?
Can it be rolled back?
Can it explain its evidence?
Can it survive retries?
Can operators debug it at 2 AM?
Can users understand the flow?