agents

name	Agents
description	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)

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)