# AI Agent Building Guide — Complete Reference

Source: Dr. Joerg Storm & Paul Storm (LinkedIn)

## Step 1: Choose a Task

**Goal:** Stop thinking "AI in general" — pick one painful workflow.

- Choose a task you repeat weekly that follows steps
- Examples: qualifying leads, summarising meetings, drafting reports, cleaning data
- Define success in one sentence: "Given X, the agent should output Y so that Z happens."

### Selection Criteria
- The task must be **repeatable** (weekly or more frequent)
- It must **follow steps** (not purely creative or one-off)
- It should have a **clear input and output**
- It should be **painful enough** that automation saves real time

## Step 2: Write Down the Steps

**Goal:** Turn that job into 4–7 clear steps.

Map the flow:
```
INPUT → ACTIONS → DECISION → OUTPUT
```

### Classify Each Step

Mark which steps are:
- ⚖️ **Pure rules** — Deterministic logic, no LLM needed (if/then, lookups, formatting)
- 📖 **Heavy reading or writing** — LLM-powered (summarisation, drafting, extraction)
- 🎯 **Judgement calls** — Needs reasoning and context (prioritisation, classification, recommendations)

### Choose Infrastructure

**Goal:** Decide where this thing lives so you're not reinventing infrastructure.

Pick one based on skill level:

**No/low code:**
- OpenAI Agent Builder
- Zapier
- Make
- n8n

**Dev friendly:**
- LangChain or LangGraph (Python)
- OpenAI Agents SDK
- CrewAI

**You only need three things:**
1. Access to a strong model
2. Tool calling capability
3. Basic logs

## Step 3: Specify Inputs, Outputs & Tools

**Goal:** Treat the agent like an API, not a vague chatbot.

### Inputs
Define what fields are required:
- Text (prompts, descriptions, queries)
- Files (documents, images, data files)
- URLs (web pages, API endpoints)
- IDs (database records, user IDs, ticket numbers)

### Outputs
Define JSON fields or a fixed template the rest of your system can trust:
- Structured format (JSON, CSV, markdown)
- Required fields with types
- Error/fallback responses
- Confidence scores where appropriate

### Tools
Attach the tools the agent needs:

**Data tools:**
- Search over docs
- Database queries
- CRM lookups

**Action tools:**
- Send email
- Post to Slack
- Create task/ticket

**Orchestration tools:**
- Schedulers
- Webhooks
- Queues

## Step 4: Assign the Agent a Clear Role

**Goal:** Write the brain.

### System Prompt Components

Create a clear system prompt that covers:

1. **Role:** "You are a [job title] focused on [task]."
2. **Boundaries:** What it must never do (list explicit prohibitions)
3. **Style:** Concise, structured, specific tone/language preferences
4. **Examples:** 1–2 example conversations showing expected behaviour

### ReAct Pattern

Use a think-then-act pattern so the agent reasons before calling tools:
1. **Observe** — What information is available?
2. **Think** — What's the best next step and why?
3. **Act** — Execute the chosen tool/action
4. **Reflect** — Did the action produce the expected result?

## Step 5: Incorporate Memory & Context

**Goal:** Stop the agent forgetting everything after each message.

### Three Memory Layers

🧠 **Conversation state:**
- Pass recent messages so it stays on topic
- Sliding window of recent exchanges
- Summary of older context when window is exceeded

🧠 **Task memory:**
- Store key decisions or variables for the current run
- Track which steps have been completed
- Remember user preferences within the session

🧠 **Knowledge memory:**
- Connect a vector store or built-in file search over your docs
- Index company documents, FAQs, policies
- Enable retrieval-augmented generation (RAG)

**Key question:** What does this agent need to remember for the next step to be smarter than the last one?

## Step 6: Apply Safeguards & Human Checks

**Goal:** Make it trustworthy.

### High-Risk Actions (require approval)
Mark actions that always need human approval:
- Sending emails
- Changing data
- Spending money
- Deleting records
- Publishing content

### Simple Rules
- Never invent logins or IDs
- Ask for clarification when the brief is ambiguous
- Refuse to proceed with incomplete critical information
- Default to the safer option when uncertain

### Audit Trail
Log every tool call and decision so you can audit behaviour:
- What tool was called and why
- What input was provided
- What output was received
- What decision was made based on the output

## Step 7: Create a Simple Interface

**Goal:** Turn the agent into something people will actually use.

### Interface Options

💬 **Internal chat interface** — For conversational workflows

▶️ **Button inside an existing app** — For single-action triggers

💬 **Slack / Teams command** — For team-accessible tools

🖥️ **Lightweight web form** — Using Streamlit, Gradio, or your React app for structured input

### Selection Criteria
- Match the interface to where users already work
- Keep it as simple as the task requires
- Don't build a dashboard when a chat command will do

## Step 8: Test It

**Goal:** Catch issues at the step level, not after it breaks something.

### Testing Protocol

For each real example:
1. **Watch the trace** — Which tools did it call and in what order?
2. **Score 3 things:**
   - **Correctness** of the final result
   - **Steps it took** (efficiency)
   - **Time saved** vs doing it manually

### Iteration
- Tighten the prompt, tools, or rules where it failed
- Use your platform's built-in observability if available
- Compare: TASK → HUMAN TIME vs RESULT ← AGENT TIME
- Repeat until the agent consistently saves time and produces correct results

## Quick Checklist

Before declaring an agent "done":

- [ ] Task defined with clear success criteria
- [ ] 4–7 steps mapped with classifications
- [ ] Inputs/outputs specified as structured formats
- [ ] Tools attached and tested individually
- [ ] System prompt covers role, boundaries, style, examples
- [ ] Memory layers configured (conversation + task + knowledge)
- [ ] High-risk actions gated behind human approval
- [ ] All tool calls logged for audit
- [ ] Interface built where users already work
- [ ] Tested with real examples, scored on correctness/efficiency/time-saved