---
name: agents-f03b943aa0ff
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)


---
Source attribution: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/agents/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 skill-library-mcp contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/agents/SKILL.md
Attribution basis: Source repository record

Fields of work: Design, Development & Engineering, Product Management
Roles: Growth Product Manager, ML / AI Engineer, Product Manager, Technical Product Manager, UX Designer
Specialties: API
Tools: Mcp Server
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: dceb68e72293e0e42f2638ac55e7341ca2c56a31a962ebc11b83d90087066f0b