Milo AI 🧢 recently shared something that caught my attention: "How to become an "AI-native agency" when applying for Y Combinator" and then immediately grounded it in a real operator story: a travel agency called DECADES, built for newly retired people who want to travel the world for three months.

What I like about Milo AI 🧢's framing is that it is not theoretical. He describes founders "HIGHLY structured" with "very little patience for repetitive work" who did the unglamorous part first: they "documented every manual process" so they could turn those steps into AI agents.

That is the heart of becoming AI-native: you are not sprinkling AI on top of a business. You are redesigning the business so the default way work gets done is by software and agents, with humans handling the decisions, exceptions, and customer experience.

What an AI-native agency actually means

A lot of teams say they are "AI-powered" because they use ChatGPT for copy, or they have a bot in Slack. An AI-native agency is different. AI-native means:

The core workflows are designed to be executed by agents, not just assisted by AI.
Data and process definitions come first, prompts come second.
Humans stay in the loop at the points that matter: approvals, edge cases, relationship building, and quality control.
Success is measured in operational outcomes: hours saved, error rates reduced, response times improved, and margins increased.

In Milo AI 🧢's example, the agency did not start with "let's build an agent." They started with "what is repeated every day that we should never do manually again?" That is the right starting point.

Key insight: AI-native is a workflow decision, not a tooling decision.

The DECADES example: from repetitive search to Margin Hunter

Milo AI 🧢 describes the first agent they are building with DECADES: "Margin Hunter." The pain is easy to recognize if you have ever booked complex travel:

The team has to look through 4-5 different booking platforms.
They manually search across 4-5 different locations per trip.
They repeat this multiple times a day.

This is the kind of work that feels necessary but is pure throughput cost. It also creates inconsistent quality, because humans get tired and stop checking "one more" option.

Margin Hunter flips the model. Instead of humans doing the scanning, the agent uses platform APIs to continuously search for deals that match the same accommodation quality at lower prices. When it finds something worth acting on, it notifies the team.

Milo AI 🧢 claims the result is meaningful: hours saved daily and $100k-$250k per year in savings that goes straight to profit margin. That range will vary by business size and booking volume, but the structure of the ROI is exactly right:

Save time (labor cost and speed)
Save money (direct COGS reduction)
Improve margin (compounding advantage)

Why this is a strong YC-style story

If you are applying to Y Combinator, "we use AI" is not a moat. YC cares about leverage, speed, and compounding advantage. An AI-native operating system can create all three.

Here is why the DECADES approach reads well in an application:

Clear user and use case: newly retired travelers booking long, complex trips.
Clear operational bottleneck: repeated cross-platform search.
Clear automation wedge: API-based deal scanning.
Clear business impact: fewer hours and lower costs, with numbers attached.
Repeatable pattern: once one workflow becomes an agent, you can do the next.

That last point matters. A single agent is a feature. A factory for turning processes into agents is a capability.

A practical playbook: turning manual processes into agents

Milo AI 🧢 mentions the key move: "documented every manual process." Let me expand that into a concrete, repeatable approach you can apply whether you run a travel agency, a recruiting firm, a creative studio, or a services startup.

1) Map the workflow like a checklist, not a paragraph

Do not write documentation like a blog post. Write it like a pilot checklist.

For each workflow, capture:

Trigger: what starts the work?
Inputs: what data is needed?
Steps: what are the exact actions?
Tools: where does the data live and where do actions happen?
Decision points: where does a human choose between options?
Output: what is produced and where is it stored?

If you cannot describe the work as a sequence of steps, you cannot reliably automate it.

2) Choose workflows that have both frequency and consequence

The best early agents sit at the intersection of:

High frequency: done many times per day or week
High consequence: errors or delays cost money or customer trust

Margin Hunter fits because booking search is frequent and directly impacts margin.

Quick filter: If you do it daily and it touches revenue or costs, it is a prime candidate.

3) Define the agent loop: sense, think, act, report

Most useful business agents follow a simple loop:

Sense: pull data (APIs, inboxes, CRM, spreadsheets)
Think: evaluate options (rules, scoring, LLM reasoning)
Act: take actions (create draft itineraries, send alerts, open tasks)
Report: log what happened and why (for auditability)

For Margin Hunter, the "sense" is pulling rates and availability. The "think" is comparing apples to apples (same quality, location, dates). The "act" is notifying the team. The "report" is a record of what was found and what changed.

4) Start with notifications and drafts, not full autonomy

A common mistake is trying to let an agent book or change things automatically on day one. In travel especially, a small error can cause a big customer problem.

A safer rollout:

Phase 1: agent finds opportunities and sends alerts
Phase 2: agent creates a recommended action and pre-fills forms
Phase 3: agent executes actions with human approval
Phase 4: selective autonomy in low-risk scenarios

This keeps humans in control while still capturing most of the time savings early.

5) Use APIs where possible, and browser automation where you must

Milo AI 🧢 notes that the agent will use platform APIs. That is ideal because APIs are more stable, faster, and easier to monitor.

If an important platform does not have an API, browser automation can work, but you should treat it as a temporary bridge, not a foundation. It is more fragile and harder to scale.

6) Measure outcomes that the business actually cares about

Do not measure "agent messages sent." Measure:

Hours saved per role per week
Quote-to-booking cycle time
Savings per booking
Gross margin improvement
Error rate and rework

Milo AI 🧢's savings estimate is a good model because it ties the agent to margin, not novelty.

What an AI-native travel agency can build next

Once you have one agent working, the second and third come faster because you now have patterns for data access, logging, approvals, and alerts.

For a travel agency like DECADES, logical next agents could include:

Itinerary assembler: drafts a trip plan from preferences and constraints
Customer concierge: answers common questions and collects missing details
Policy checker: validates visa, insurance, and cancellation constraints
Supplier follow-up: monitors confirmations, deadlines, and changes
Post-trip insights: turns feedback into operational improvements

The important part is sequencing. Start with the workflow that creates immediate leverage (Margin Hunter), then expand to adjacent steps.

The bigger takeaway from Milo AI 🧢

Milo AI 🧢 ends with a simple summary: "take manual processes, and turn them into agents" to become AI-native. I would add one sentence: do it in a way that makes the business measurably faster, cheaper to run, and easier to scale.

If you are building an agency and thinking about YC, this is a compelling path: pick one painful, high-frequency workflow, document it like a checklist, build an agent that runs the loop, keep humans in the approval path, and track margin impact. Then repeat.

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 →