Architecture, guardrails and operations for models, agents, MCP and RAG
A practical platform view of AI engineering: central model policy, isolated agent capabilities, traceable knowledge, measurable quality and cost, and governance mapped to real controls.
AI engineering is more than choosing a model. Production systems need identity, data boundaries, controlled tools, resilient routing, observability, evaluation and evidence that can survive audits and incidents.
This page starts with the architecture and operating model I use, then keeps the broader tool ecosystem as a secondary reference rather than the product itself.
The page now follows the platform layers from agents through governance. Use this journey to jump directly to an architectural responsibility.
The goal is not to accumulate AI tools. It is to operate models, agents, MCP tools and knowledge pipelines behind explicit security, reliability, cost and governance boundaries.
Route local and remote inference through LiteLLM so clients share authentication, budgets, fallbacks, telemetry and provider policy instead of calling models directly.
Keep credentials server-side, constrain access by workload, and separate sensitive prompts, documents and tenant data from public or lower-trust execution paths.
Treat tools as privileged capabilities: scope what agents can call, isolate execution, minimize filesystem and network reach, and keep human approval for destructive actions.
Track source provenance, ingestion state and retention across Paperless, OCR and retrieval pipelines so generated answers remain traceable to controlled knowledge.
Trace prompts, latency, model selection and token cost with Langfuse and operational metrics, then evaluate quality separately instead of relying on anecdotal output checks.
Map technical controls to GDPR, ISO 27001 and ISO 42001 concerns: accountability, access control, evidence, data residency, supplier risk and model-use policy.
A local-first platform structured as layers rather than a product catalogue. LiteLLM is the central control plane connecting interfaces, models, tools, workflows, and observability.
Routine workloads prefer Ollama inside the homelab. Routing through LiteLLM keeps one shared routing and protection policy before any remote inference.
Redis caching, fallbacks, and centralized routing reduce unnecessary remote calls and avoid coupling clients to a single model.
Langfuse, Opik, and Prometheus separate LLM observability, quality evaluation, and operational metrics while preserving a coherent platform view.
Three complementary roles: n8n for rapidly composing automations, Temporal for durable execution, and Langflow for experimenting with and assembling AI flows.
Visual automation layer for rapidly connecting APIs, data, agents, and homelab services.
n8nDurable orchestration for stateful, long-running, critical workflows: retries, persistent state, timers, human-in-the-loop steps, and LLM activities.
TemporalVisual workbench for prototyping LLM chains, agents, tools, and RAG before productionizing them in orchestration and service layers.
LangflowOpenCommit complements the development workflow with consistent Conventional Commit messages; it intentionally remains outside the orchestration layer.
GitHubShared capabilities decoupled from agents: execution, knowledge, and isolation can be consumed by Open WebUI, agents, and workflows.
Self-hosted terminal exposing a simple API so agents and automation can execute commands and manipulate files in a controlled environment.
GitHubShared RAG layer fed by the vetted PDF corpus produced through Paperless. OpenRAG combines Langflow workflows, Docling parsing, and OpenSearch-backed retrieval to provide document ingestion, search, and chat in one stack. Its APIs can then be wrapped by a custom MCP server so AI interfaces and agents reuse a common retrieval surface instead of reimplementing knowledge-base access.
GitHubSandboxed runtime for autonomous agents with isolation, declarative policies, and controlled network, filesystem, process, and credential access.
GitHubSecondary reference material for technologies that can fit behind the platform controls above. Product names are examples, not architectural requirements.
A compact open model family useful when evaluating smaller local or edge inference footprints.
Learn moreContext and documentation retrieval tooling for development assistants and agent workflows.
WebsiteA curated directory of MCP servers and integrations for exploring standardized tool capabilities.
RepositoryA visual guide to reasoning LLMs that helps explain how modern models approach multi-step reasoning and where evaluation still matters.
Read articleTreat this list as replaceable implementation options. Keep authentication, routing, policy, telemetry and data lifecycle independent from any single provider.
A unified view of quality, traces, costs, tokens, and latency: evaluate LLM applications, monitor production behavior, and apply FinOps discipline to model consumption.
Open-source platform for evaluating, testing, and monitoring LLM applications, useful for comparing model outputs and tracking production quality.
DocumentationOpen-source LLM observability platform for traces, prompts, scores, costs, and latency. Langfuse collects telemetry from LiteLLM and the MCP server to provide a cross-platform view of AI calls.
LangfuseTrack ChatGPT and Codex usage across credits, tokens, models, activity, and consumption. Available analytics depend on the workspace plan; Codex also exposes usage and limit details in supported account settings.
ChatGPT / Codex analyticsReview Cursor account usage and token-consumption patterns to spot expensive workflows and plan limits before they become delivery friction.
Cursor analyticsUse LiteLLM as the metered gateway for routing, budgets, fallbacks and provider-independent usage evidence rather than distributing cost controls across every client.
LiteLLMA controlled PDF-to-RAG flow with explicit preparation, archive/OCR, enrichment, durable orchestration, private review and a shared retrieval surface.
Normalize, split, merge or clean PDFs locally before ingestion so the archive receives predictable inputs.
Stirling PDFArchive, OCR, tag and index the prepared documents in Paperless-ngx as the durable source corpus.
Paperless-ngxEnrich consumed documents with classification and metadata only after the canonical archive has accepted them.
Paperless-AIRun summarization, translation, retries and human-in-the-loop activities as durable workflows rather than fragile background scripts.
TemporalUse a private workspace to inspect and question the processed corpus while validating OCR, tags and downstream transformations.
AnythingLLMPublish the vetted corpus to a shared RAG layer, then expose controlled retrieval capabilities through an MCP surface instead of reimplementing access per agent.
OpenRAGThe platform controls matter only when they are paired with repeatable engineering habits and explicit evidence.
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