Martijn Verbove@verboveEngineering · #6

AI agents are not “automations with a brain”. They’re outcome systems. Most people only see the chat UI. The leverage is underneath: the infrastructure. 2026 shift: not automation. Outcome-oriented Agent. A simplified map: 1. Compute (CPU/GPU) Training + inference + latency. No compute, no scale. 2. Runtime infrastructure Containers, orchestration, queues, retries, observability. This is what makes agents reliable instead of “demo-y”. 3. Data layer SQL + docs + vector search. Agents are only as useful as the data they can reach. 4. Pipelines (ETL) Ingest, clean, transform, refresh. Without this, your “smart agent” runs on garbage. 5. Models LLMs, smaller task models, multimodal. Reasoning + generation lives here, not in the UI. 6. Routing Pick the right model per task by cost, latency, quality. This is how you make it usable in production. 7. Orchestration + protocols Tool calling, workflows, multi-agent coordination. MCP / A2A are the plumbing, not the product. 8. Tools + memory APIs, permissions, state, personalization. This is where agents actually do work. 9. Interfaces Chat, dashboard, voice, embedded UI. The surface, not the system. If you want a moat, stop thinking “prompt”. Start thinking “outcome”.

Teaches comprehensive agent infrastructure architecture