Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
Build AI agents for any domain - customer service, research, operations, creative work, or specialized business processes.
The model already knows how to be an agent. Your job is to get out of the way.
An agent is not complex engineering. It's a simple loop that invites the model to act:
LOOP:
Model sees: context + available capabilities
Model decides: act or respond
If act: execute capability, add result, continue
If respond: return to user
That's it. The magic isn't in the code - it's in the model. Your code just provides the opportunity.
Atomic actions the agent can perform: search, read, create, send, query, modify.
Design principle: Start with 3-5 capabilities. Add more only when the agent consistently fails because a capability is missing.
Domain expertise injected on-demand: policies, workflows, best practices, schemas.
Design principle: Make knowledge available, not mandatory. Load it when relevant, not upfront.
The conversation history - the thread connecting actions into coherent behavior.
Design principle: Context is precious. Isolate noisy subtasks. Truncate verbose outputs. Protect clarity.
Before building, understand:
CRITICAL: Trust the model. Don't over-engineer. Don't pre-specify workflows. Give it capabilities and let it reason.
Start simple. Add complexity only when real usage reveals the need:
| Level | What to add | When to add it |
|---|---|---|
| Basic | 3-5 capabilities | Always start here |
| Planning | Progress tracking | Multi-step tasks lose coherence |
| Subagents | Isolated child agents | Exploration pollutes context |
| Skills | On-demand knowledge | Domain expertise needed |
Most agents never need to go beyond Level 2.
Business: CRM queries, email, calendar, approvals Research: Database search, document analysis, citations Operations: Monitoring, tickets, notifications, escalation Creative: Asset generation, editing, collaboration, review
The pattern is universal. Only the capabilities change.
| Pattern | Problem | Solution |
|---|---|---|
| Over-engineering | Complexity before need | Start simple |
| Too many capabilities | Model confusion | 3-5 to start |
| Rigid workflows | Can't adapt | Let model decide |
| Front-loaded knowledge | Context bloat | Load on-demand |
| Micromanagement | Undercuts intelligence | Trust the model |
Philosophy & Theory:
references/agent-philosophy.md - Deep dive into why agents workImplementation:
references/minimal-agent.py - Complete working agent (~80 lines)references/tool-templates.py - Capability definitionsreferences/subagent-pattern.py - Context isolationScaffolding:
scripts/init_agent.py - Generate new agent projectsFrom: "How do I make the system do X?" To: "How do I enable the model to do X?"
From: "What's the workflow for this task?" To: "What capabilities would help accomplish this?"
The best agent code is almost boring. Simple loops. Clear capabilities. Clean context. The magic isn't in the code.
Give the model capabilities and knowledge. Trust it to figure out the rest.