Agent Architecture Stack
The minimum tooling to design, run and observe a production agent.
/ quick answer
Build agents with explicit state, typed tools, persistent memory and full tracing from day one. The minimum tooling to design, run and observe a production agent.
- Claude or GPT-5 (reasoning model)
- AI SDK or LangGraph (agent loop)
- MCP (tool interface)
- Postgres + pgvector (memory)
- Langfuse (tracing and evals)
- Lovable (ship the UI around it)
- CrewAI
- OpenAI Agents SDK
- Mastra
What is the Agent Architecture Stack stack for?
Build agents with explicit state, typed tools, persistent memory and full tracing from day one.
Which tools are in this stack?
Claude or GPT-5 (reasoning model), AI SDK or LangGraph (agent loop), MCP (tool interface), Postgres + pgvector (memory), Langfuse (tracing and evals), Lovable (ship the UI around it).
Are there alternatives to this stack?
Yes — CrewAI, OpenAI Agents SDK, Mastra.
Some links support Onexial at no extra cost to you.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Agent Architecture
Agent architecture is the structural blueprint of an AI agent: the model, the planning loop, the tools, the memory layer and the guardrails that decide how it acts.
- →Agent Planning (ReAct, Plan-and-Execute)
Agent planning is how an AI agent decides its next step — reactively step-by-step (ReAct) or by drafting a full plan up front (plan-and-execute).
- →Agent Cost Control
Agent cost control is the practice of budgeting tokens, steps and model tiers per task so autonomous systems stay economically viable at scale.
- →Multi-Agent System
A multi-agent system splits a job across several specialised AI agents that coordinate through a shared plan, message bus or orchestrator.
Related workflows
Turn this into a repeatable process.
- →Design an Agent Architecture Before Writing Code
A one-page design process that prevents the most expensive agent rebuilds.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
- →Build a Multi-Agent Content Team
Researcher, writer and editor agents under a supervisor, producing publishable drafts.
- →AI Agent + Web3 Execution
Give an AI agent on-chain capability safely: read freely, propose openly, and sign only inside hard-coded limits.
Related tool stacks
The tools that run it in production.
- →Multi-Agent Orchestration Stack
Tooling for coordinating several specialised agents with reliable handoffs.
- →AI Employee Stack
Everything a role-owning agent needs: knowledge, tools, memory and reporting.
- →Autonomous Operations Stack
Run autonomous workflows with approvals, audit trail and a kill switch.
- →Coding Agent Stack
Run coding agents with executable feedback and reviewable diffs.
Related prompts
Reusable prompts for this job.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
- →Crypto Agent Design Prompt
Specifies an AI crypto agent's tools, permissions, guardrails and evaluation before a single line is written.
- →Wallet Monitoring Agent Prompt
System prompt for a read-only agent that watches addresses, filters noise and reports only decision-relevant activity.
Related use cases
How people apply it, and what came out.
- →SaaS Cuts First-Response Time from 6h to 4min with an Agent
A 12-person SaaS shipped a tier-1 support agent and kept humans on exceptions only.
- →Ecommerce Team Runs a Multi-Agent Catalog Pipeline
Three agents enrich, write and QA 12,000 product listings.
Comparisons & alternatives
Pick between the options.
- →Single Agent vs Multi-Agent System
One well-equipped agent beats a crowd for most jobs; multi-agent wins on genuinely separable, parallel work.
- →LangGraph vs CrewAI vs AI SDK for Agents
LangGraph for stateful control, CrewAI for fast role-based teams, AI SDK for shipping agents inside a product.
- →AI Agent vs Trading Bot
A trading bot executes fixed rules deterministically; an AI agent interprets context and decides which steps to take — powerful for research, risky for execution.