Every developer building their first AI agent hits the same wall:

  • Which model do I use?
  • What framework?
  • How do I handle memory?
  • What about tool use?
  • How autonomous should it be?

You can spend days researching. Or you can answer five questions and get a complete architecture blueprint in 30 seconds.

The Architecture Problem

Building an AI agent isn't like building a CRUD app. There's no single "right" stack. The choices cascade:

Model selection affects cost, speed, and capability ceiling. Claude Opus for complex reasoning. Haiku for high-volume simple tasks. Ollama for privacy and zero API cost.

Framework choice affects developer experience and lock-in. Claude Agent SDK for Anthropic-native. LangChain for multi-model flexibility. Vanilla for maximum control.

Capability scope determines complexity. An agent that reads files is simple. An agent that reads files, calls APIs, sends emails, and remembers context across sessions is an architecture challenge.

Most tutorials show you the happy path with one model, one tool, one framework. Real agents need real architecture decisions made upfront.

What the Agent Builder Does

The Agent Builder asks you:

  1. What does your agent do? — Name it. Describe its job in one sentence.
  2. What capabilities does it need? — File system, web, database, APIs, email, search, code execution, memory, scheduling, MCP.
  3. Which model? — Claude Opus/Sonnet/Haiku, GPT-4o, or Ollama local.
  4. Which framework? — Claude Agent SDK, LangChain, or vanilla.
  5. How autonomous? — Human-in-the-loop to fully autonomous.

Then it generates:

  • Complete project structure
  • Boilerplate code for your chosen framework
  • Tool definitions for each capability
  • Configuration files
  • A system prompt tuned to your agent's role
  • Memory setup if you selected persistence

Why Blueprint-First Matters

The #1 failure mode for AI agent projects: starting to code before deciding on architecture.

You pick a model, wire up one tool, get excited, add three more tools, realize your framework doesn't support streaming, switch frameworks, lose your tool definitions, start over.

Sound familiar?

A blueprint forces the architecture decisions upfront. Before you write line one, you know:

  • Your token budget per interaction
  • Your tool boundary (what the agent can and cannot do)
  • Your autonomy level (when does a human approve?)
  • Your persistence strategy (stateless vs. session vs. permanent memory)

Model Selection Guide

| Model | Best For | Cost | Speed | |-------|----------|------|-------| | Claude Opus 4.6 | Complex reasoning, multi-step tasks | $5/$25 per 1M | Slower | | Claude Sonnet 4.6 | Balance of quality and speed | $3/$15 per 1M | Fast | | Claude Haiku 4.5 | High-volume, simple tasks | $1/$5 per 1M | Fastest | | GPT-4o | Multi-modal, vision tasks | $2.50/$10 per 1M | Fast | | Ollama (Local) | Privacy, no cost, offline | Free | Varies |

The builder includes this context in your blueprint so you can make informed tradeoffs.

Real Examples

Customer support agent: Haiku + email + database + memory. Low cost per interaction, remembers customer history.

Code review agent: Opus + file system + code execution. Needs deep reasoning to catch subtle bugs.

Research agent: Sonnet + web + search + memory. Balances quality with volume of pages processed.

Personal assistant: Sonnet + email + calendar + scheduling + memory. The Swiss Army knife.

Try It

Build your agent blueprint →

Free. No signup. Architecture decisions in 30 seconds instead of 3 days.

The best code starts with the right architecture. We just made that step instant.