Simple Guide to Building AI Agents for Startup Founders in 2025
Simple Guide to Building AI Agents for Startup Founders
1. What is an Agent?
Agent = A smart software powered by artificial intelligence (AI) that can accomplish multi-step tasks on its own.
It goes beyond answering simple questions. Imagine a virtual assistant that can solve customer issues, manage appointments, or process a purchase—all with minimal human involvement.
NOT agents: Chatbots that just answer FAQs or sentiment checkers.
2. When Should You Use Agents?
Agents are most valuable when:
Tasks are complex, with exceptions, decision-making, or when things aren’t black-and-white.
The workflow uses tons of unstructured data (like emails, documents, conversations).
Existing rules or automation break easily or require constant updates.
Example Use Cases:
Reviewing refund requests in customer support.
Summarizing and processing customer feedback.
Automating onboarding or verification of new users.
If your workflow is simple or clear-cut, traditional automation may suffice.
3. Key Ingredients of an Agent
You don’t need to code, but it helps to understand these parts:
a) Model
Brain of the agent. Usually a large language model (LLM) like ChatGPT.
b) Tools
Abilities the agent can use. For example:
Accessing your database
Sending emails/messages
Fetching weather info, etc.
c) Instructions
Clear policies/scripts/guides on how the agent should act step-by-step.
4. How the Agent Works
Imagine your agent is like a smart intern:
Receives a job (“Answer this customer’s question”)
Thinks about the best way to tackle it (using the LLM)
Follows instructions (like a standard operating procedure)
Reports results or asks for human help if stuck
5. Step-by-Step: How To Build an Agent (No Code! Strategy Level)
Step 1: Pick a Painful, Valuable Workflow
Choose a workflow that would benefit from autonomy and is too complex for simple scripts.
Talk to team members doing repetitive, decision-based work.
Step 2: Write Out the Steps
Pretend you’re onboarding a new hire.
List every possible step, decision, and exception in the task.
Example: Refund approval process: Check if order was delivered, amount under $100, timeline within 30 days, etc.
Step 3: Gather Your Tools
Do you have databases, APIs, calendars, or emails the agent should use?
If you don’t have APIs, look for no-code/low-code tools (Zapier, Make, Airtable, OpenAI’s API with agents support).
Ask your tech team or vendors about what integrations exist.
Step 4: Write Clear Instructions (“Prompt”)
Use everyday language.
Spell out what to do in each scenario.
For example:
“If the customer asks for a refund, check purchase details. If the item was delivered and refund window is open, approve up to $100. Otherwise, hand over to a human.”
Step 5: Add Guardrails (Safety Nets)
Why? To prevent mistakes, security leaks, or reputation risks.
Examples of guardrails you should set:
Block certain actions (e.g., agent can’t delete user data, issue large refunds, or send sensitive information)
Detect and stop if the input seems weird or dangerous (e.g., someone trying to trick the bot or send harmful content)
Require human review for high-impact steps (e.g., refunds above $100, account cancellations)
Automatically filter inappropriate language or requests
Tip: Imagine the worst-case scenarios and make sure the agent asks for help or stops before doing something risky.
Step 6: Start Small and Test
Don’t try to automate everything at once.
Build a simple prototype that does one clear job.
Run “tabletop” tests—feed it real-world tasks and see what it does.
Watch for mistakes, confusion, or times when it doesn’t know what to do.
Iterate: Refine your instructions and add or improve guardrails as needed.
Step 7: Add Human-in-the-Loop
For anything risky, make sure your agent can “escalate” to a human.
That might look like:
Notifying a support agent for review
Sending incomplete tasks to your team’s dashboard
Asking the user to confirm before acting on certain things
This is key early on, so you build trust and fix issues before going fully autonomous.
Step 8: Gradually Expand Capabilities
Once the agent is safe and reliable for the first task, add more tools or steps.
Consider breaking large or complex workflows into segments, with different agents or subprocesses (“mini-experts” that can hand off to each other).
Regularly review performance—track successes, failures, and edge cases.
Helpful Tips for Non-Technical Founders
Work closely with your tech team. You decide what the agent should do, they’ll decide how to connect systems and tools.
Write pseudo-code or flowcharts. Even in plain English, describing steps is extremely helpful for technical colleagues.
Leverage no-code/low-code platforms. Tools like Zapier, Make, and even OpenAI’s own agent features increasingly let you build workflows with little or no code.
Start with internal use. Test agents on back-office or staff workflows before exposing to customers.
Monitor and log actions. Always be able to trace what the agent did and why.
Handy Resources
Read tutorials and guides: OpenAI, Zapier, and others have easy step-by-step docs.
Talk to vendors: Many SaaS tools are now building agent features, and third-party vendors can help implement.
Example: Refund Workflow Agent (Summary)
User emails about a refund
Agent reads the request, checks order database (tool)
Follows decision tree from your instructions
If rules are met, proceeds; if not, escalates to a human
Logs everything it does, and never exceeds its authority
Final Thought
You don’t have to be technical to drive agent adoption. Your most valuable contribution is clarity: articulate the process, rules, desired outcomes, and boundaries. Think of an agent as a very smart, very literal team member: if you’d worry about a new hire making a decision, have the agent ask for help, too.
Start simple, stay safe, and grow with confidence!