AI ArchitectureAI ArchitectNovember 2, 2026
What are the core layers of a production agentic AI architecture?
TL;DR
A production agentic AI architecture comprises six layers: an orchestration loop, context and memory stores, deterministic tool contracts (MCP), an LLM gateway, evaluation and observability gates, and human approval boundaries.
Shipping reliable agent systems requires engineering the harness around the model with the same rigor applied to distributed systems.
The six production layers
- 1.Context & instruction substrate — Version-controlled
CLAUDE.md/AGENTS.mdcontracts and progressive-disclosureSKILL.mdmodules so agents load exact repo invariants. - 2.Orchestration & state loop — Bounded task graphs with explicit iteration caps, checkpointing, and independent verification between maker and reviewer contexts.
- 3.Tool & data plane (MCP) — Typed JSON-RPC/HTTP tool interfaces with read/write separation and schema validation.
- 4.Model routing & LLM gateway — Task-appropriate model selection (frontier reasoning for architecture, fast models for mechanical checks) with fallback chains and spend limits.
- 5.Memory & retrieval — Hybrid lexical + vector indexing over verified operational decisions, separated cleanly from ephemeral scratchpads.
- 6.Governance & observability — OpenTelemetry trace capture, promptfoo regression suites, and fail-closed human gates on billing, production credentials, and external communications.
Explore reference diagrams and deployable topologies in the AI Architecture Hub.
#agentic-ai#ai-architecture#mcp#governance#observability