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How to Set Up AI Agent Workflows (Without Losing Your Mind)

A practical guide from Ask Patrick — for people who want agents that actually work.

A practical guide from Ask Patrick — for people who want agents that actually work.

What Is an AI Agent Workflow?

An AI agent workflow is a sequence of automated steps where an AI model takes actions, makes decisions, and produces outputs — often chaining multiple tools or models together. Think of it as a smart assembly line where the workers are language models.

The key difference from a simple chatbot: agents act, not just answer.

Step 1: Define What "Done" Looks Like

Before writing a single prompt, answer this:

"What exact output do I need, in what format, delivered where?"

Examples:

Vague goal = vague agent = wasted time.

Step 2: Choose Your Agent Architecture

Single Agent

One model, one job. Best for simple, well-defined tasks.

Multi-Agent (Orchestrator + Workers)

One "manager" model routes tasks to specialized sub-agents.

Pipeline (Linear Chain)

Output of Agent A → Input of Agent B → Output of Agent C

Rule of thumb: Start single-agent. Add complexity only when you hit a real wall.

Step 3: Pick Your Tools

Every agent needs tools — the actions it can take beyond generating text.

| Tool Type | Examples | When to Use | |-----------|----------|-------------| | Web search | Brave Search API, Serper | Agent needs current info | | File I/O | Read/write local or cloud files | Document processing | | Code execution | Python sandbox, shell | Data analysis, automation | | APIs | Slack, Gmail, GitHub, Notion | Integrations | | Browser | Playwright, Puppeteer | Web scraping, form filling | | Memory | Vector DB, SQLite | Context across sessions |

Don't give agents tools they don't need. More tools = more hallucinated tool calls.

Step 4: Write a Tight System Prompt

This is where most people fail. Your system prompt is your agent's job description, personality, and operating manual rolled into one.

The Template

## Role
You are [specific role]. You [core responsibility].

## Task
[Exact description of what you do each run]

## Tools
You have access to: [list tools and when to use each]

## Output Format
[Exact format: JSON, Markdown, plain text, etc.]

## Rules
- [Constraint 1]
- [Constraint 2]
- If uncertain, [fallback behavior]

Common Mistakes

Step 5: Add Memory (If Needed)

Most agents don't need memory on day one. Add it when you see:

Types of Memory

Scratchpad (in-context): Store notes in the current context window. Simple, works great for single sessions.

File-based: Write a summary to a file at end of run. Read it at start of next run. Dead simple, surprisingly effective.

Vector DB: Semantic search over past interactions. Use when you have lots of history and need smart retrieval (Chroma, Pinecone, Qdrant).

Structured (SQL/KV): Store facts in a database. Best for user preferences, counters, states.

Step 6: Test Like a Skeptic

Agents fail in weird ways. Test for:

  1. Happy path — does it work when everything is normal?
  2. Empty input — what happens with no data?
  3. Malformed input — what if the API returns garbage?
  4. Tool failure — what if the web search times out?
  5. Edge cases — the thing you didn't think of

Log everything on first deploy. You can't debug what you can't see.

Step 7: Deploy and Monitor

Deployment Options

| Option | Best For | Tools | |--------|----------|-------| | Cron job | Scheduled tasks | GitHub Actions, cron, OpenClaw | | Webhook trigger | Event-driven | n8n, Make, custom server | | Always-on daemon | Real-time agents | Docker, PM2, Railway | | On-demand API | User-triggered | Modal, AWS Lambda |

What to Monitor

Set up a simple alert: if the agent fails 3 runs in a row, ping you on Slack/Discord.

Step 8: Iterate

The first version of your agent will be wrong. That's fine.

After each week, ask:

Update the system prompt. Tighten the tools. Adjust the memory. Repeat.

Most useful agents are the result of 10+ small iterations, not one brilliant design.

Common Patterns That Work

Daily Briefing Agent

Research Agent

Content Repurposing Agent

Customer Support Triage Agent

Quick Checklist Before You Launch

Resources

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