Agents

Design, build, and deploy AI agents with architecture patterns, framework selection, memory systems, and production safety.

When to Use

Use when designing agent systems, choosing frameworks, implementing memory/tools, specifying agent behavior for teams, or reviewing agent security.

Quick Reference

Topic	File
Architecture patterns & memory	architecture.md
Framework comparison	frameworks.md
Use cases by role	use-cases.md
Implementation patterns & code	implementation.md
Security boundaries & risks	security.md
Evaluation & debugging	evaluation.md

Before Building — Decision Checklist

• Single purpose defined? If you can't say it in one sentence, split into multiple agents
• User identified? Internal team, end customer, or another system?
• Interaction modality? Chat, voice, API, scheduled tasks?
• Single vs multi-agent? Start simple — only add agents when roles genuinely differ
• Memory strategy? What persists within session vs across sessions vs forever?
• Tool access tiers? Which actions are read-only vs write vs destructive?
• Escalation rules? When MUST a human step in?
• Cost ceiling? Budget per task, per user, per month?

Critical Rules

1. Start with one agent — Multi-agent adds coordination overhead. Prove single-agent insufficient first.
2. Define escalation triggers — Angry users, legal mentions, confidence drops, repeated failures → human
3. Separate read from write tools — Read tools need less approval than write tools
4. Log everything — Tool calls, decisions, user interactions. You'll need the audit trail.
5. Test adversarially — Assume users will try to break or manipulate the agent
6. Budget by task type — Use cheaper models for simple tasks, expensive for complex

The Agent Loop (Mental Model)

text

OBSERVE → THINK → ACT → OBSERVE → ...

Every agent is this loop. The differences are:

• What it observes (context window, memory, tool results)
• How it thinks (direct, chain-of-thought, planning)
• What it can act on (tools, APIs, communication channels)