How to Create AI Agents from Scratch (A Step-by-Step Guide)

The rise of large language models and flexible AI tooling has made building custom AI agents more accessible than ever. Whether you want an agent to help automate tasks, assist with research, support user interactions, or power new services — starting from scratch and designing for your needs often yields the most flexible and powerful results. In this guide, we walk through a nine-step process to build an AI agent from scratch — from defining purpose to building a UI or API around it.

Step 1: Define Your Agent’s Purpose and Scope

Before writing a single line of code or prompt, you must be clear on what your agent is supposed to do. This means:

• Specifying the exact task the agent will handle (e.g. “qualify sales leads,” “draft outreach emails,” “summarize support tickets,” “recommend books based on user preferences”).
• Identifying the target users — are they internal team members, end customers, or other agents?
• Clarifying the deliverables — what output the agent should produce (e.g. a JSON object, a formatted report, a draft message, a decision, etc.).

Example: Suppose you want a “sales assistant” agent. You might define that it will: take a lead’s profile data as input, research the lead’s public info, score lead fit, and output a draft outreach email. With this scope clearly defined, everything else — from prompts to data flow — becomes easier to plan.

Step 2: Establish Clear Input / Output Schemas

Once the purpose is clear, design structured input and output schemas rather than leaving everything free-form. This gives your agent a stable “contract,” similar to how APIs define request and response structures.

• Use tools like Pydantic (in Python), JSON Schema, or TypeScript interfaces to formally define inputs and outputs (this point was also emphasised by RDD).
• Define exactly what fields the agent expects (with types, required vs optional, value constraints, etc.).
• For outputs, specify not only the data (e.g. “email_subject”, “email_body”, “lead_score”) but also metadata (e.g. timestamp, model_version, processing_time) if helpful — especially useful for logging, debugging, or chaining agents.

from pydantic import BaseModel, Field
from typing import Optional, List

class LeadProfile(BaseModel):
    name: str
    email: Optional[str]
    company: Optional[str]
    description: Optional[str]

class OutreachEmail(BaseModel):
    subject: str
    body: str
    lead_score: float = Field(..., ge=0, le=1)

# Example usage:
lead = LeadProfile(name="Alice Johnson", email="alice@example.com", company="Acme Corp")
print(lead.json())

This schema-first approach ensures consistency, makes it easier to validate outputs, and simplifies integration with other systems or UIs.

Step 3: Write the System Instructions

With schema in place, write detailed role definitions and system instructions for your agent. Essentially, you tell the AI: “You are X. Here are your responsibilities, constraints, style, tone, and output format.”

• Define behavioral rules (e.g. “always return JSON matching schema,” “if data missing, respond with an error object,” “be polite, concise, and professional”).
• Use consistent prompting / instruction templates to reduce variation in responses. Many agents benefit from stable “system prompt + user prompt + schema enforcement” structure.
• Try different instruction styles — some agents respond better to highly explicit instructions, others to more flexible or conversational ones.

You can use any LLM that supports this style — e.g. GPT-4, Claude, or other models. Many builders embed the system instructions directly in their agent initialization.

Step 4: Enable Reasoning & External Actions

An agent becomes much more powerful when it can reason logically and interact with external systems — databases, APIs, tools, web search, code execution, etc.

• Use frameworks like ReAct (Reasoning + Action) or similar patterns: the agent reasons, then chooses an action (like calling an API), then observes the result, then reasons again, and so on.
• Provide the agent with tool functions/interfaces it can call, with clearly defined inputs and outputs (matching schema), such as “search_web(query)” → returns results; “send_email(payload)”; “query_database(params)”; etc.
• For tasks like data retrieval, calculations, database operations, web scraping, document processing — connecting these external actions makes the agent capable of more than just generating text.

This step turns your agent from a “smart text generator” into a real “agent” that can act, not just “reply.”

import openai, os, json

openai.api_key = os.getenv("OPENAI_API_KEY")

SYSTEM_PROMPT = """
You are a helpful assistant. Use the available tools when needed.
Return output in JSON with keys: {action, action_input} or {final_answer}.
"""

TOOLS = {
    "search": lambda query: f"[search results for: {query}]",
    # add more tools as needed
}

def call_llm(messages):
    resp = openai.chat.completions.create(
        model="gpt-4o",
        messages=messages
    )
    return resp.choices[0].message["content"]

def agent_loop(user_input):
    messages = [{"role":"system","content":SYSTEM_PROMPT},
                {"role":"user","content":user_input}]
    while True:
        reply = call_llm(messages)
        data = json.loads(reply)
        if "action" in data:
            result = TOOLS[data["action"]](data["action_input"])
            messages.append({"role":"assistant","content":reply})
            messages.append({"role":"tool","content":result})
        elif "final_answer" in data:
            return data["final_answer"]

if __name__ == "__main__":
    answer = agent_loop("Find the population of France and compute 10% of it.")
    print(answer)

Step 5: Orchestrate Multiple Agents (If Needed)

For complex workflows — for example, a multi-step sales funnel, data analysis + reporting pipeline, or multi-department workflows — you may want multiple agents working together, each with a defined role.

• For instance: a Planner agent decides the steps, a Worker agent executes tasks (e.g. data fetch, calculations), and a Verifier agent reviews results for quality.
• Build coordination logic (orchestrator) that assigns tasks to agents, sequences actions, handles dependencies, and aggregates results.
• Use frameworks or orchestration libraries, or write custom logic. It's often helpful to treat this orchestration like the “controller” layer in an application — passing tasks, results, status, and coordinating agents.

This makes your system modular, maintainable, and capable of handling complex or large-scale tasks.

Step 6: Add Memory and Context

Many useful agents — chat assistants, support bots, research agents, personal assistants — need to remember previous interactions or persistent knowledge over time. Without memory, every interaction is stateless and context-less.

• Implement short-term memory (conversation history, session context), for tasks that involve multi-turn interaction.
• Implement long-term memory/knowledge base — store facts, user preferences, past decisions, external data — often using vector databases or other storage solutions.
• For memory retrieval and grounding, consider using retrieval-augmented generation (RAG): when the agent needs context, fetch relevant past data or documents, embed them with current prompt, then generate.

By adding memory, your agent can provide continuity, personalization, and increasingly useful behavior.

class ConversationMemory:
    def __init__(self):
        self.history = []

    def add(self, message: str):
        self.history.append(message)
        # Optional: trim if too long

    def get_context(self) -> str:
        return "\n".join(self.history)

mem = ConversationMemory()

def run_conversation(input_text):
    mem.add(f"User: {input_text}")
    # pass context to agent
    # agent generates response...
    response = "..."  # from LLM
    mem.add(f"Agent: {response}")
    return response

# Example usage
run_conversation("Hello, who are you?")
run_conversation("Remember my name is Alice.")

Step 7: Integrate Multimedia Abilities

Depending on the agent’s purpose, you may or may not want to add support for images, voice, video, or file/document processing (depending on the AI Agent you're trying to create this step could be optional for others, but for most it's quite necessary).

• For voice or audio: integrate speech-to-text / text-to-speech tools (e.g. Whisper, other ASR/TTS systems).
• For images / visuals: enable image generation or vision-capable models (if needed), so agent can analyze images or produce visuals.
• For document processing: parse PDFs, Word docs, or other data formats, and let the agent read or produce structured outputs.

Multimedia support broadens the range of tasks your agent can handle — from document summarization to image-based analysis or interactive UI tasks.

Step 8: Format and Deliver Output

Your agent’s output should be well-structured, clean, and usable — both for humans and for other programs or systems.

• Use structured output formats (JSON, XML, typed schema) when output is consumed programmatically.
• If the agent produces reports, logs, or human-readable summaries — format them clearly (Markdown, HTML, PDF, etc.).
• For debugging or introspection — include metadata (timestamps, tool call logs, token usage) as part of the output.

This ensures outputs are reliable, parsable, and easier to integrate into UIs, pipelines, or downstream systems.

Step 9: Build a User Interface or API Layer

Finally, wrap your AI agent in a user-facing interface or API so it can be used by others — whether internal users, customers, or other systems.

Options include:

• A REST API (test all your API endpoints with Apidog) or HTTP endpoint (e.g. using frameworks like FastAPI) so external applications can call the agent programmatically. (more code examples at Real Python)

• A simple chat UI (web or desktop), or a command-line interface for users to interact with.
• Embedding in existing applications, Slack bots, dashboards, or custom front-ends.

This final step turns your agent from a “project” into a usable tool — effectively, a product that delivers value.

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class AgentRequest(BaseModel):
    prompt: str

class AgentResponse(BaseModel):
    result: str

@app.post("/api/agent", response_model=AgentResponse)
def call_agent(req: AgentRequest):
    response = agent_loop(req.prompt)  # assume agent_loop is defined
    return {"result": response}

Frequently Asked Questions

Q1. Why define structured input/output schemas instead of using free-form text?
Structured schemas (via Pydantic, JSON Schema, etc.) provide guarantees — ensuring the agent receives expected fields and returns predictable, machine-readable outputs. This reduces the chance of malformed data, simplifies validation, and makes integration with other systems far more robust.

Q2. What is ReAct and why is it useful?
ReAct stands for “Reasoning + Action.” It's a design pattern where an agent alternates between thinking (reasoning) and doing (calling a tool or performing an action), then observes the result and continues reasoning as needed. This allows agents to perform multi-step logic, call external tools or APIs, and base subsequent steps on results — making them far more powerful than simple one-shot prompt-and-respond bots.

Q3. When should I use multiple agents instead of a single agent?
Use multiple agents when the task is complex and involves distinct sub-tasks that benefit from specialization — for example planning, execution, validation, or different domains like data fetching, reasoning, and reporting. Multi-agent setups improve modularity, clarity, and robustness. (practicle guide at Empathy First Media)

Q4. How does memory improve an agent — and what kind of memory is best?
Memory enables continuity — allowing agents to remember previous interactions, user preferences, past decisions, or accumulated knowledge. Short-term (session context) helps with multi-turn conversations; long-term (vector databases, document stores) supports knowledge retrieval, personalization, and reasoning across time. For many applications, a combination is ideal.

Q5. How do I safely deploy an AI agent — and avoid runaway loops or unsafe behavior?
Before deployment, add safety and monitoring: limit the number of reasoning or tool-call loops per request; implement logging, error handling, and human-in-the-loop checkpoints for sensitive actions; monitor usage, cost, and performance; and test edge cases thoroughly.

Conclusion

Building an AI agent from scratch is a rewarding — and increasingly accessible — endeavour. By following a structured process — defining purpose, designing clear schemas, writing solid instructions, enabling reasoning and tool-use, optionally orchestrating multiple agents, adding memory and context, formatting outputs correctly, and exposing a usable interface — you can create powerful, reliable agents tailored to your specific needs.

No matter what you're building (a sales assistant, a research tool, a chatbot, or an automation engine), this step-by-step guide gives you the blueprint. With thoughtful design and good architecture, your AI agent can evolve from a prototype into a useful, maintainable, and scalable tool.

If you’re ready to build your first agent — pick a simple purpose, write its schema, and give it a try. Once the basics are working, you can layer on memory, tools, and interface, and watch your creation grow into something truly powerful.