Everyone’s talking about agents. Few are building them well.
The hardest part? Not the tech, it’s knowing where to start, what to build, and how to validate it.
This guide walks you through a clear, example driven process for turning a vague idea into a reliable agent. We’ll use the case of an email assistant to illustrate every step.
Step 1: Start with a Job, Not Just an Idea
Define a task that makes sense for an agent something a sharp intern could realistically do with time and tools.
Your goal here:
- Choose a task that’s not trivial, but not magical either
- Come up with 5–10 real examples to define the scope and test performance
Email agent example:
- Identify and respond to urgent stakeholder emails
- Schedule meetings using calendar availability
- Ignore irrelevant emails
- Answer basic product questions from docs
Avoid:
- Tasks too vague or broad to define
- Situations where normal software is faster and cheaper
- “Magic” tasks that rely on tools or data that don’t exist yet
Step 2: Write Out the Manual Version
Before building anything, describe exactly how a person would do the job. That’s your standard operating procedure (SOP).
Why this matters:
- Confirms you understand the task
- Reveals decisions your agent will need to make
- Highlights required data and tools
Email agent SOP:
- Read email and evaluate urgency based on sender and content
- Check calendar availability if a meeting is needed
- Draft a reply using context
- Send only after human approval
Step 3: Build a Prompt-Driven MVP
Don’t build everything at once. Focus on the reasoning core first—usually a single prompt that handles classification or decision-making.
Your goal here:
- Build confidence in LLM performance before full orchestration
- Use manual inputs to validate the agent’s thinking
- Stick to your test cases from Step 1
Email agent example:
Start with classifying emails by intent and urgency.
Prompt input:
Email: “Can we meet next week about Jutsu?”
Sender: Jeff Bezos, CEO of Amazon
→ Output:Intent = Meeting Request,Urgency = High
Tool tip: Use something like LangSmith to iterate on prompts and track performance.
Step 4: Connect the Dots
Now, feed real inputs into your prompt and begin building orchestration.
Think through:
- What data does the prompt need?
- Where is that data coming from (APIs, databases, etc.)?
- What logic connects it all?
Email agent example:
- Use Gmail API to get new emails
- Query CRM for sender context
- Use calendar API to suggest meeting times
- Run the full prompt with context
- Draft response → human review → send
Step 5: Test Everything
Start with manual testing using your original examples. Then build toward automation.
Look for:
- Consistency across test cases
- Obvious blind spots or logic gaps
- LLM behavior across variations
Email agent test criteria:
- Responses are safe, respectful, and hallucination-free
- Emails are categorized correctly
- Tools are only used when needed
- Replies are relevant and readable
Track all this. Use real user inputs to discover what breaks.
Step 6: Deploy, Then Improve
You’re ready to launch but that’s not the end. It’s the start of real learning.
After launch:
- Monitor how people actually use the agent
- Look for gaps in coverage or common failure modes
- Add new capabilities slowly, re-test each one
Email agent post-launch:
Let usage data guide you. Maybe users expect FAQ replies. Maybe you missed a common sender pattern. Expand based on demand not speculation.
Tools like LangGraph help with deployment and scaling. Tools like LangSmith help you trace what’s happening under the hood.
Final Thought
Most agents fail because they were never clear, scoped, or tested in the first place.
Start small. Stay grounded in examples. Think like a builder not a dreamer.
If you do, you’ll end up with something that actually helps people work smarter.
