Milo AI 🧢 recently shared something that caught my attention: "This travel agency applied to Y Combinator as an "AI-native travel agency" (and how you can too)." He then introduced DECADES, a travel agency for newly retired people who want to travel the world for three months, and explained how the founders turned repetitive operations into AI agents.

That framing matters because it is not about sprinkling AI on top of a business. Milo AI 🧢 is pointing to a more structural shift: documenting the work, extracting the steps, and rebuilding the company around systems that can execute those steps reliably.

In this post, I want to expand on Milo AI 🧢's example and turn it into a practical playbook you can apply, whether you run a travel agency, a services business, or any operation with repeatable workflows.

The DECADES example: start with the work, not the model

DECADES was founded by Laura and Marie, who Milo AI 🧢 describes as "highly structured people with very little patience for repetitive work." Their response was not to hire more coordinators or accept the busywork as the cost of doing business. Instead, they documented every manual process with the explicit goal of turning those steps into AI agents.

This is the key move: write down what happens today in enough detail that a system could do it tomorrow.

If you skip the documentation step, you end up with vague automation goals like "use AI to help with bookings". If you do it well, you get a set of precise agent jobs like:

Collect traveler preferences and constraints
Propose a short list of itineraries
Search for equivalent accommodations at better prices
Validate cancellation policies and edge cases
Notify a human when action is required

The first agent: "Margin Hunter" and why it is a great starting point

Milo AI 🧢 shared that the first agent they are working on is called Margin Hunter. Its job is simple and high leverage: it replaces the need to look through 4-5 different booking platforms and manually search across 4-5 locations per trip, multiple times a day.

That is exactly the kind of workflow that begs for an agent:

It is repetitive
It follows rules
It depends on structured data (prices, dates, locations, filters)
It creates measurable outcomes (time saved, dollars saved)

Milo AI 🧢 also described the mechanism clearly: the agent uses platform APIs to search for deals, aiming for the same quality of accommodations but at lower prices. When a deal is found, the team is notified to take action.

Key insight from Milo AI 🧢: take one painful, repeatable process and build an agent that runs it end to end, then loop in humans only at decision points.

The result he cited is not small: hours saved every day and an estimated $100k to $250k per year in savings that directly improves profit margin.

What "AI-native" actually means in practice

A lot of companies call themselves AI-native when they really mean AI-assisted. Milo AI 🧢's story points to a stronger definition.

An AI-native business is designed so that:

Workflows are explicit (documented and measurable)
Systems can execute the workflows (agents with tools and permissions)
Humans supervise exceptions and make judgment calls (not copy and paste)
The operating model improves continuously (metrics, feedback loops, iteration)

In other words, AI-native is an operating system choice, not a marketing label.

A practical playbook: turning manual ops into agents

Here is a concrete, repeatable approach for doing what DECADES is doing, without getting lost in AI hype.

1) Inventory the repetitive work

Start with a 1-2 week audit. Ask your team to list tasks that are:

Performed daily or weekly
Annoying or time consuming
Already based on checklists
Dependent on copying data between tools

In DECADES' case, one clear candidate was "search multiple booking platforms repeatedly".

2) Write the workflow like a machine needs it

Document the process at two levels:

The human-friendly version: steps, screenshots, decision rules
The agent-ready version: inputs, outputs, tools used, acceptance criteria

For Margin Hunter, the agent-ready definition might look like:

Inputs: trip dates, target locations, accommodation constraints, budget bands
Tools: booking platform APIs, internal customer preference database
Output: list of candidate alternatives with price delta and policy notes
Acceptance criteria: same or better rating threshold, required amenities, cancellation policy constraints

3) Choose a narrow first agent with clear ROI

Your first agent should be boring and valuable. Avoid "answer any question about our business". Instead, pick something like:

Quote generation
Lead qualification and routing
Invoice follow-ups
Price monitoring and deal detection (like DECADES)
Internal knowledge retrieval with citations

The best early agents have outcomes you can measure in dollars or hours.

4) Design the human-in-the-loop handoff

Milo AI 🧢 highlighted a critical design choice: notify the team when a deal is found so humans can take action. That is the right posture.

Define:

When the agent can act autonomously
When it must ask for approval
What context it must include in the notification
How humans provide feedback (approve, reject, adjust constraints)

This reduces risk and makes adoption easier because the team stays in control.

5) Get serious about data and access

Agents are only as good as the tools they can use.

If you want an agent to search platforms, you need:

API access (or approved integrations)
Rate limit handling
Consistent identifiers (properties, locations, dates)
A place to store results and decisions

Treat this like product infrastructure, not a one-off script.

6) Add evaluation and guardrails before you scale

For a deal-finding agent, evaluation can be straightforward:

Precision: how often a suggested "deal" is actually acceptable
Savings: average delta per booking
Time saved: reduction in manual search time
False positives: how often humans reject suggestions

Add guardrails such as minimum ratings, policy requirements, and brand safety constraints.

7) Iterate into a network of agents

Once one agent works, the temptation is to build ten more immediately. Resist that. Expand deliberately:

Agent 1: find and surface opportunities (Margin Hunter)
Agent 2: validate constraints and policies automatically
Agent 3: draft customer-facing options and explanations
Agent 4: monitor post-booking changes (price drops, schedule changes)

This turns operations into a compounding system.

Why this story fits a Y Combinator narrative

Milo AI 🧢's post ties the outcome to Y Combinator because YC tends to reward:

Clear wedge: one painful workflow solved extremely well
Defensibility: process knowledge plus integrations plus data feedback loops
Scale path: expand from one agent into an operating system for the industry

In the DECADES case, the wedge is margin and time savings. The scale path is a travel agency that runs on agents, with humans focused on high-touch service and complex exceptions.

If you want to do this in your business

Milo AI 🧢 mentioned you can use tools like Riff or work with a team like internalOS.ai to accomplish similar outcomes. Whether you build in-house or partner up, the main idea remains the same:

Pick one repeatable process
Document it
Build an agent that can use the right tools
Keep humans in the loop
Measure impact and iterate

If you do that, "AI-native" stops being a buzzword and becomes a practical operating advantage.

This blog post expands on a viral LinkedIn post by Milo AI 🧢, doing cool stuff (and telling stories about it) - join 16k. View the original LinkedIn post →