Most “AI agent” advice is either super abstract or painfully over-engineered. Five days ago I saw a concise blueprint on Reddit that nailed the path. This post paraphrases and expands that idea into a copy-paste plan you can actually ship.
Credit: Inspired by a great post on r/AgentsOfAI by u/Icy_SwitchTech titled “Building your first AI Agent; A clear path!”. I’ve adapted and expanded it with my own notes, checklists, and code stubs.
Why agent projects stall
The problem is vague (“general agent” syndrome).
Tooling decisions come before the use-case.
Teams jump to frameworks before the basic loop works.
Memory/vector DBs get added on day one (you probably don’t need them yet).
The cure is boring and effective: ship one tiny, single-purpose agent end-to-end.
The 8-Step Path (with mini checklists)
1) Pick one very small, very clear job
Examples you can ship this week:
Book a doctor’s appointment from a hospital website.
Summarize unread emails from the last 24 hours and send a recap.
Watch job boards and forward matches with a relevance score.
Rename and file PDFs in a set folder using simple rules.
Checklist
The job has a binary “done/not done”.
One user, one trigger, one output.
You can demo it in under 2 minutes.
2) Choose a base LLM (don’t train anything yet)
Use a capable hosted model (e.g., GPT, Claude, Gemini) or a solid open-source model if you’re self-hosting. Focus on structured outputs (JSON) and tool-use ability.
Checklist
Model supports function/tool calling or you can structure prompts to emit JSON.
You’ve tested a few sample prompts and validated cost/latency.
3) Decide how it touches the outside world (the part most people skip)
List exactly which actions your agent is allowed to do:
Web scraping/browsing (Playwright, Puppeteer, or site APIs).
Email API (Gmail/Outlook).
Calendar API (Google/Outlook).
File I/O (read/write PDFs, CSVs, disk).
Internal service calls (HTTP/GraphQL).
Checklist
Every action has one clear function with strict inputs/outputs.
You’ve written mock versions you can test offline.
4) Build the skeleton workflow (before any frameworks)
The heartbeat is: model → tool → result → model until you get a final answer.
Checklist
System prompt defines role, constraints, JSON schema, and stop conditions.
A loop executes selected tools and feeds results back to the model.
Clear exit condition (e.g., “status: done” with a final payload).
5) Add memory carefully (later, and only if needed)
Short-term chat context is enough for many tasks. If you must remember across runs, start with a tiny JSON/SQLite file keyed by user/task. Bring in vector DBs only when retrieval becomes the bottleneck.
Checklist
Start with ephemeral memory (last few messages).
If persistence is required, begin with JSON/SQLite, then consider RAG.
6) Wrap it in a simple interface
CLI first to prove behavior, then a minimal UI:
A one-screen web dashboard (Flask/FastAPI/Next.js).
A Slack/Discord bot if your users live there.
A cron/worker for scheduled runs.
Checklist
One button to run, one place to read results.
Logging visible in the UI (status + latest tool step).
7) Iterate in tiny cycles
Run real tasks, find the brittle spots, patch, repeat. Expect many cycles!
Checklist
Log every tool call and model message.
Keep a “golden set” of 5–10 tasks; the agent must pass them all before you add features.
8) Keep scope under control
A single, boring, reliable agent beats a “universal agent” that fails randomly.
Checklist
No new tools until the agent is stable for a week.
Any new feature must ship behind a flag and pass the golden set.
Minimal code skeletons
A. Python (requests + your tools)
# minimal_agent.py
import json, time
from typing import Dict, Any
# 1) Your tools
def get_unread_emails() -> Dict[str, Any]:
# TODO: call Gmail/Outlook API
return {"emails": [{"from": "a@b.com", "subject": "Hi", "body": "…"}]}
def send_summary(text: str) -> Dict[str, Any]:
# TODO: send via email/Slack
return {"sent": True, "length": len(text)}
TOOLS = {
"get_unread_emails": {"fn": get_unread_emails, "schema": {"type": "object", "properties": {}}},
"send_summary": {"fn": send_summary, "schema": {"type": "object", "properties": {"text": {"type": "string"}}}},
}
# 2) LLM call (replace with your provider’s SDK)
def llm(messages):
# TODO: call your model with function/tool calling enabled
# Return either {"tool": {"name": "tool_name", "args": {...}}} or {"final": {...}}
raise NotImplementedError
SYSTEM = {
"role": "system",
"content": (
"You are an email-summarizing agent.\n"
"Allowed tools: get_unread_emails(), send_summary(text).\n"
"Always return JSON. Finish with: {\"final\": {\"status\": \"done\", \"summary\": \"...\"}}."
)
}
def run_agent():
messages = [SYSTEM, {"role": "user", "content": "Summarize unread emails from the last 24h and send me a recap."}]
for _ in range(10):
out = llm(messages)
if "final" in out:
return out["final"]
tool_name = out["tool"]["name"]
args = out["tool"].get("args", {})
result = TOOLS[tool_name]["fn"](**args)
messages.append({"role": "tool", "name": tool_name, "content": json.dumps(result)})
time.sleep(0.1)
if __name__ == "__main__":
print(run_agent())
This is intentionally minimal: replace
llm()with your provider, wire real tool functions, and control the loop exit condition in the system prompt.
B. TypeScript (Node) shape you can grow
// agent.ts
type Message = { role: "system" | "user" | "assistant" | "tool"; name?: string; content: string };
type ToolResult = Record<string, unknown>;
type ToolFn = (args?: Record<string, unknown>) => Promise<ToolResult> | ToolResult;
const tools: Record<string, { fn: ToolFn }> = {
getUnreadEmails: { fn: async () => ({ emails: [] }) },
sendSummary: { fn: async (args) => ({ sent: true, length: (args?.text as string)?.length || 0 }) },
};
async function llm(messages: Message[]): Promise<any> {
// Call your LLM with tool/function calling enabled.
// Return either { tool: { name: string, args: object } } or { final: object }.
throw new Error("Implement me");
}
const SYSTEM: Message = {
role: "system",
content:
"You are an email-summarizing agent. Allowed tools: getUnreadEmails(), sendSummary(text). " +
'Always output JSON. Finish with {"final":{"status":"done","summary":"..."}}.',
};
export async function runAgent() {
const messages: Message[] = [SYSTEM, { role: "user", content: "Summarize unread emails from the last 24h and send me a recap." }];
for (let i = 0; i < 10; i++) {
const out = await llm(messages);
if (out.final) return out.final;
const { name, args } = out.tool;
const result = await tools[name].fn(args);
messages.push({ role: "tool", name, content: JSON.stringify(result) });
}
throw new Error("Loop limit reached");
}
Prompts that keep agents honest
Use a terse system prompt that sets role, allowed tools, JSON schema, exit rule, and guardrails:
You are a single-purpose agent that <do the one job>.
Allowed tools: <toolA(args)>, <toolB(args)>. Never invent tools.
Always speak JSON. On each step, either:
{"tool":{"name":"<toolName>","args":{...}}}
or
{"final":{"status":"done","data":{...},"notes":"..."}}.
Stop when status=="done". If you cannot proceed safely, return:
{"final":{"status":"blocked","reason":"..."}}
A few good first agents to build
Calendar concierge: turn natural language into real calendar events + email invite.
Inbox digester: summarize unread emails into one daily note + links.
Lead enricher: take a CSV of company names, fetch sites/socials, return a profile sheet.
Content filer: rename/organize PDFs based on detected title/date/vendor.
Pitfalls & guardrails
Don’t add memory too early. JSON or SQLite beats a whole vector stack at the start.
Constrain tools. Narrow inputs, validate aggressively, and log everything.
Define “done”. Without a crisp exit condition, agents meander and cost you tokens.
Golden set. Keep a fixed set of scenarios the agent must pass before adding features.
What this buys you
Once you’ve shipped one specific agent end-to-end, every next agent gets easier:
you already know how to frame the problem, wire tools, design the loop, and ship a tiny UI.
If you try this path, I’d love to see what you build. Drop a comment or ping me. Happy to review prompts or tool boundaries.
PS: Attribution
This article paraphrases and extends a concise framework shared by u/Icy_SwitchTech on r/AgentsOfAI: “Building your first AI Agent; A clear path!” All mistakes, expansions, and code here are mine.
Top comments (2)
dopeee dm ?
sure @benztechies