How to Build AI Agents in Practice

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## Practical workflow

### Step 1: define the job
Before writing any prompt, answer:
- what real task should this agent help with?
- who is the user?
- what counts as success?
- what mistakes would be costly?

### Step 2: choose the simplest topology
Start with the smallest useful setup:
- one orchestrator + one specialist
- or one specialist + one verifier

Do not begin with a swarm.

### Step 3: write an Agent Design Spec
Use this template:
- Name
- Mission
- When to use
- Inputs
- Outputs
- Allowed tools
- Guardrails

### Step 4: decide memory boundaries
Ask:
- does this agent only need the current task?
- does it need working memory?
- does it need personal long-term memory?

### Step 5: minimize tools
Examples:
- family reminder agent → calendar + read access, not file editing
- research helper → read/search/fetch only
- content drafter → read + write to local docs, but not publish externally

### Step 6: create the first version
Make it useful, not perfect. Test on one narrow task.

### Step 7: add a verifier
For anything important, add:
- fact-checker
- schema validator
- reviewer
- or human checkpoint

### Step 8: log failures
If an agent fails in a repeated way, update the spec, prompt, or checklist.

## Starter examples

### For yourself
- chief-of-staff
- project researcher
- writing assistant
- memory curator

### For family
- reminder / planning helper
- shopping or logistics assistant
- learning assistant for kids

### For friends or clients
- meeting prep agent
- follow-up drafter
- industry scanner
- proposal builder

## Practical anti-patterns

- too many agents too early
- full memory for everyone
- no approval boundary
- no output structure
- no logging of repeated failures

## Tool ecosystems to study

- Anthropic Claude Code / Cowork
- OpenAI Codex
- OpenHands
- GitHub Copilot
- VS Code skills ecosystem

## Suggested next step
Pick one real personal workflow and build one simple agent for it this week.