DEV Community: Egor Karpovich

MCP TravelCode: Let AI Assistants Search Flights and Book Hotels

Egor Karpovich — Thu, 02 Apr 2026 01:04:30 +0000

We just open-sourced MCP TravelCode — a Model Context Protocol server that connects AI assistants to the Travel Code corporate travel API.

Your AI assistant can now search for flights, book hotels, manage orders, and track flight status — all through natural language conversations.

What is MCP?

Model Context Protocol (MCP) is an open standard that lets AI assistants connect to external tools and data sources. Think of it as USB-C for AI — one protocol, universal connectivity.

MCP TravelCode implements this standard for corporate travel, giving any compatible AI client access to real travel infrastructure.

Features

🔍 Flight search — multi-city, one-way, round-trip with cabin class and passenger filters
🏨 Hotel search — star rating, meal plans, refundability, price filters with SSE streaming
📊 Flight status — real-time tracking with delays, gates, terminals, and aircraft info
📈 Delay statistics — historical delay and cancellation data for flights and airports
📋 Order management — create, cancel, modify bookings; check cancellation conditions
🌍 Airport & airline data — search by name, city, IATA/ICAO code

Security

Authentication via OAuth 2.1 + PKCE — secure browser-based authorization with automatic token refresh. No API keys stored in config files.

Quick Start

Setup takes a single command. First, authenticate:

npx mcp-travelcode-auth auth

Then add to your AI client config (e.g., claude_desktop_config.json):

{
  "mcpServers": {
    "travelcode": {
      "command": "npx",
      "args": ["mcp-travelcode"]
    }
  }
}

For Claude Code:

claude mcp add travelcode -- npx mcp-travelcode

Supported Clients

MCP TravelCode works with all major AI clients:

Client	Transport	Status
ChatGPT Desktop	stdio	✅ Compatible
Claude Desktop	stdio	✅ Tested
Claude Code	stdio	✅ Tested
Gemini	stdio	✅ Compatible
GitHub Copilot	stdio	✅ Compatible
Cursor	stdio	✅ Tested
Windsurf	stdio	✅ Compatible
VS Code	stdio	✅ Compatible
JetBrains IDEs	stdio	✅ Compatible
Zed	stdio	✅ Compatible
Cline	stdio	✅ Compatible
Continue	stdio	✅ Compatible

Two transport modes: stdio (local) and HTTP/SSE (remote).

Example Conversation

You: Find me flights from Amsterdam to London next Friday, economy class

AI: Searches the Travel Code API, returns real-time results with prices, times, and airlines

You: Book the KLM flight at 9:15 AM

AI: Creates the booking through the API

That's it. Natural language → real travel actions.

I Run a Travel SaaS With 12 AI Agents and 48 Cron Jobs on OpenClaw. Here's My Exact Setup.

Egor Karpovich — Wed, 18 Feb 2026 14:43:09 +0000

No fluff. No "imagine a world where..." Real architecture, real costs, real failures.

I'm the co-founder of Travel Code — a corporate travel management platform. Five weeks ago I started building an army of AI agents on OpenClaw. Today I have 12 agents, 48 automated cron jobs, a $92K paper trading portfolio managed by one of them, and a GTM pipeline that scrapes 7,200+ competitor reviews to find sales leads.

This is exactly how it works.

The Stack

Everything runs on OpenClaw — an open-source AI assistant framework. It's the runtime, the scheduler, and the communication layer. Each agent gets:

A personality file (SOUL.md — defines who the agent is, how it talks, what it cares about)
Persistent memory (markdown files + LanceDB vector search — the agent remembers everything)
Its own Telegram group (this is the UI — I check agents like Slack channels)
Cron jobs (heartbeats, daily tasks, weekly reports — all staggered to avoid crashes)
Skills (installable plugins from ClawHub — SEO tools, CRM connectors, social APIs, scraping kits)

One Linux VPS (4 vCPU, 16 GB RAM). One OpenClaw gateway process. 12 agents sharing it.

Why Staggered Crons Matter (Learned the Hard Way)

Nobody tells you this: if you schedule 12 agents to wake up at the same time, everything crashes. The server runs out of memory, processes hang, and you're SSH-ing in at 2 AM to restart everything.

My heartbeats are staggered at 5-minute intervals:

:00 — Lobster (coordinator)
:05 — DevBot
:10 — CFO
:15 — Mailer
:20 — SEO
:25 — Analyst
:30 — Trader
:35 — Sales
:40 — SMM
:45 — PR

Each heartbeat runs on Ollama (qwen2.5:3b) — a local model on the same server. Zero API cost. The agent wakes up, checks its task queue, and goes back to sleep. It only burns Claude tokens when there's actual work to do.

Why a local model for heartbeats? Because heartbeats fire every hour for every agent. That's 120+ API calls per day just for "do I have work?" — at Claude's pricing, that's pure waste. Ollama handles it for free.

The Agents (And What They Actually Do)

🦞 Lobster — The Coordinator

My personal assistant and the one managing everyone else. Runs a daily standup at 4 AM UTC — collects status from all agents and sends me a single summary. That's my "management overhead."

Also handles cross-agent routing: when Sales finds a hot lead, Lobster can loop in Mailer for outreach and PR for media angle.

💰 Sales Agent — The One That Writes Its Own Code

This is probably the most insane agent. We built our own GTM OS (Go-To-Market Operating System) — a full platform for lead generation, competitor review scraping, LinkedIn enrichment, and outreach automation.

Then I gave the Sales agent developer skills.

Now it improves its own platform. It added qualification fields to the database. Built a bulk PATCH endpoint. Classified 7,229 reviews into hot/warm/cold. Added new API filters. When it needed a LinkedIn scraper and Apify was too expensive, it asked DevBot to build one — DevBot deployed a self-hosted FastAPI scraper on a separate server the same day. Sales started using it immediately.

It has 15 skills installed — from Apollo and HubSpot CRM connectors to cold email generators, Google Ads, and a Twenty CRM integration.

The numbers: 7,229 competitor reviews scraped across Navan, TravelPerk, Egencia, and 12 other competitors. 73 hot leads (unhappy decision-makers at competitors). 255 warm leads in pipeline. All found and qualified automatically.

The morning pipeline runs at 6 AM. Afternoon enrichment at 3 PM. Evening report at 10 PM. Three times a day, zero human involvement.

🔍 SEO Agent — Same Story, Different Platform

We have a separate SEO OS — a system for managing projects, content, GEO (Generative Engine Optimization), and keyword tracking at seo.travel-code.com. The SEO agent sits on top of it with skills for Google Search Console, schema markup, geo-optimization, and competitor analysis. It analyzes what needs to be done and executes.

I added my colleague Andrey to the SEO Telegram group. Now he works directly with the agent — gives it tasks, reviews its reports, discusses strategy. The agent became a team member, not just my tool.

📱 SMM Agent — Content Machine

The SMM agent monitors industry news, adapts it for our ICP (ideal customer profile), and publishes across all channels — Twitter/X via API, LinkedIn, Dev.to. It has skills for the X algorithm, tweet idea generation, listing swarm (directory submissions), and content marketing.

It doesn't just schedule posts. It finds trending topics in corporate travel, rewrites them from our angle, and distributes across every platform where our audience lives.

📈 Trader — Portfolio Manager

Manages a $92K paper trading portfolio on Alpaca. Runs every hour during market hours. Strict rules: -20% stop-loss, take 50% profit at +10%, stocks only, 80% invested / 20% cash target.

Current positions: NVDA, META, AAPL, GOOGL, MSFT, SPY, QQQ, AMD. It monitors, logs everything to a trading journal, and only alerts me when something crosses a threshold.

Pre-market research at 8 AM EST. Nightly self-learning where it studies new algo-trading strategies. Skills: multi-factor strategy analysis, SEC insider trading data via OpenInsider.

📰 PR Agent

Scans journalist request platforms (HARO, Qwoted, SourceBottle) three times a day. Posts to Forbes Business Council three times a week via browser automation. Has skills for Reddit, SEO optimization, cold email, content creation, personal branding, campaign orchestration.

Real result: landed a pitch into Kiplinger's within hours of the request going live.

🛠 DevBot — The Builder

35 skills. Docker management, API development, database operations, browser automation (Stagehand, Camoufox stealth), scraping (Firecrawl, deep-scraper, Apify, PhantomBuster), DNS/networking, SSH tunneling, React/Next.js, 2Captcha. When other agents need code — they ask DevBot.

The LinkedIn scraper that Sales depends on? DevBot built and deployed it as a FastAPI microservice in Docker.

📧 Mailer — 30 Skills Deep

Newsletter generation, SEO article writing, landing page creation, cold email, Brevo integration, Google Ads, HubSpot, Bluesky, Typefully, YC-style cold outreach templates. This agent doesn't just send emails — it's a full marketing operations toolkit.

💼 CFO

Financial modeling with Charlie CFO framework, expense tracking, Plaid integration, tax analysis. Monitors burn rate and unit economics.

📊 Analyst

Deep research, competitive analysis, flight search (Jinko), news aggregation, Perplexity integration. Runs weekly deep dives across 12 competitors — Navan, TravelPerk, Spotnana, Ramp, Brex, Egencia. Recent finding: Brex acquired by Capital One for $5.15B (down from $12B peak) — that intel became a sales angle the same day.

The Architecture (Current Setup)

┌──────────────────────────────┐
│   OpenClaw Gateway           │  ← coordination + API calls
│   VPS: 4 vCPU, 16 GB RAM    │
│   + Ollama (qwen2.5:3b)     │
│   + LanceDB (vector memory)  │
└──────────────┬───────────────┘
               │
          Tailscale VPN
               │
        ┌──────▼──────┐
        │ Browserbase  │  ← cloud headless browsers
        │ (anti-detect)│     for Forbes, directories, scraping
        └─────────────┘

The key insight: 90% of agent work is an API call to Anthropic → get text → send to Telegram. The server barely matters — all the heavy lifting is on Anthropic's side. Heartbeats, inbox checks, web searches — that's milliseconds of CPU.

The expensive part is browser automation. That's where architecture matters.

Future Architecture (With Mac Mini)

┌──────────────────────────────┐
│   OpenClaw Gateway           │  ← stays one, lightweight
│   VPS (always-on)            │
└──────────────┬───────────────┘
               │
          Tailscale VPN
               │
    ┌──────────┼──────────────┐
    │          │              │
┌───▼──┐  ┌───▼────┐  ┌─────▼────────┐
│Ollama│  │Mac Mini│  │GTM-OS Server │
│(GPU  │  │(node   │  │(sales        │
│server│  │host +  │  │pipeline)     │
│      │  │browser)│  │              │
└──────┘  └────────┘  └──────────────┘

When you run browser automation from a VPS, you hit Cloudflare, CAPTCHAs, and IP bans constantly. Residential proxies cost money and still get blocked 30-40% of the time.

OpenClaw has a "node host" feature — install it on a Mac Mini at home, connect via Tailscale, and agents browse the internet through a real residential IP. No proxy needed. Cloudflare thinks it's a human on Chrome. The Mac Mini costs $500 once and pays for itself in a month of saved proxy costs.

For 50 agents: gateway stays one (lightweight coordination), Ollama moves to a GPU server, browser tasks go through the Mac Mini, heavy compute spins up ephemeral containers on fly.io or DO App Platform.

Why LanceDB?

Free. Runs locally. Semantic search over agent memory — find relevant context from weeks ago without scanning every file. No cloud vector DB subscription.

Why Tailscale?

Three devices. Closed VPN. Zero-config networking. The VPS, Mac Mini, and GTM server all see each other. Free tier covers it.

Agent-to-Agent Communication

Three mechanisms:

1. Direct messages. One agent sends a message to another's session. PR asks Analyst for market data. Sales asks DevBot to build a scraper.

2. Task queue (Clawe on Convex). Agents post tasks, others claim them based on role. A task like "write content about Brex acquisition" gets posted, SMM claims it.

3. Heartbeat-based async. Critical: agents don't wake up on incoming messages (that would burn tokens all day). They wake up on their hourly heartbeat cron, check the queue, process anything waiting, and sleep again.

Agent-to-agent communication is async by design. When PR sends a request to Analyst, it might take up to an hour (next heartbeat). That's fine. Most tasks don't need real-time.

Self-Learning and Self-Improving

Every agent has a weekly self-learning cron (Tuesday & Friday, staggered 15 minutes apart):

Trader studies algo strategies on r/algotrading and Quantocracy
Sales researches outbound tactics on SaaStr and Gong Labs
SEO checks algorithm updates and GEO techniques
PR reads PRWeek and Muck Rack for new media tactics
DevBot scans Hacker News and GitHub for new tools
SMM studies LinkedIn algorithm changes and viral content patterns
CFO analyzes SaaS benchmarks on Baremetrics and ChartMogul
Mailer reads newsletter growth tactics on Beehiiv and Litmus blogs

They update their own knowledge files. On Friday at 4 PM, Lobster runs a cross-agent knowledge digest — finds overlaps, contradictions, and connections between what agents learned in different domains.

Then on Friday at 5 PM, a cron retrospective analyzes all 48 cron jobs — which ones delivered value, which ones wasted tokens, what should change. The system improves itself weekly.

My Team Works With Them

This surprised me the most. The agents became team members.

Andrey talks directly to the SEO agent — gives tasks, reviews reports
The dev team interacts with DevBot for code reviews and deployment help
Sales managers get automated lead distributions every morning via email — sorted by language and region, with a different motivational sales quote each day

I added colleagues to the agent Telegram groups. They don't need to know it's an AI. They just... work with it.

What Doesn't Work (Yet)

Reddit blocks server IPs. Even Browserbase with residential proxies gets caught. Had to ask a human to post manually.
Cloudflare-protected sites need the Mac Mini. No cloud browser service fully bypasses them.
Device token mismatches between root/user configs caused a full day of debugging. Silent failures are the worst.
Rate limits cascade. When Anthropic API hits limits, all agents fail simultaneously. Fixed by switching to Max subscription.
Agents can't build relationships. They find journalists, draft pitches, submit them. But the actual human connection — that's still my job.

The Numbers

Item	Cost/Month
VPS (4 vCPU, 16 GB RAM)	$48
Claude Max subscription	$200
Ollama (local, heartbeats)	$0
LanceDB (local, vector memory)	$0
Tailscale (3 devices)	$0
Browserbase	~$50
APIs (Apify, FullEnrich, etc.)	~$50-100
Total	~$350-400/mo

My first week without the subscription: $1,000 in five days on API tokens alone. The Max subscription with OAuth token cut costs dramatically — I always have Opus now, and limits are subscription-based instead of pay-per-token.

The New Job Title

Here's my prediction for the next big role in tech: "The person who manages agents and loads information into them."

It's not prompt engineering. It's not AI development. It's operational management of AI workers — configuring their personalities, maintaining their memory, building their cron schedules, connecting them to the right tools and platforms, and feeding them the context they need to do good work.

I spend maybe 30 minutes a day on this now. Scanning Telegram groups, approving a pitch, correcting a sales email, adding context to an agent's knowledge base. The agents do the other 23.5 hours.

The Plan: 50 Agents

We're heading to 50. Not because it's a round number — because there are that many distinct functions in a startup that can be automated to some degree.

We've already started replacing people who don't adopt AI. That sounds harsh, but the math is simple: when an SEO agent can research, plan, and draft in 30 minutes what took a contractor a full day — and that contractor refuses to use AI tools to match the speed — the economics don't work.

The future isn't AI replacing humans entirely. It's humans who use AI replacing humans who don't.

How to Start

Install OpenClaw. One server, one gateway. Start with a single agent.
Telegram as the UI. Give each agent its own group chat. Cheapest, most accessible interface.
Ollama for heartbeats. qwen2.5:3b locally. Zero API cost for the "do I have work?" checks.
Stagger everything. 5-minute gaps between crons. Learn from my crashes.
Give agents their own platforms. Don't just give them API access — give them an OS to operate. GTM OS for sales. SEO OS for content. Let agents build on top.
Add your team. The real power is when your human colleagues start working with agents directly.
Accept 80%. Agents do 80% of the work at 80% quality. You do the 20% that requires taste, judgment, and human connection.

I'm building this at Travel Code — AI-powered corporate travel management.

If you're building multi-agent systems, DM me on X or LinkedIn. I've made most of the mistakes already — happy to save you the $1,000 learning curve.

Why I Open-Sourced My LinkedIn AI Assistant

Egor Karpovich — Mon, 16 Feb 2026 18:24:29 +0000

I built an AI-powered LinkedIn comment assistant as a SaaS product. Then I gave it all away for free. Here's why — and how it works under the hood.

What is LiPilot?

LiPilot is a Chrome extension that helps you write better LinkedIn comments, replies, and DMs using AI. It sits right inside your LinkedIn feed, reads the context of a post (including images), and suggests a thoughtful comment in your voice — not generic ChatGPT-speak.

You pick your LLM provider (OpenAI, Anthropic, Gemini, or even a local Ollama instance), plug in your API key, and you're set. No account creation. No subscription. No server in between.

Why I Went From Commercial to Open-Source

I originally built LiPilot as a paid tool. The plan was straightforward: freemium Chrome extension, usage-based pricing, the usual SaaS playbook.

But as I used it myself and watched others interact with similar tools, something felt off. Most AI writing assistants for LinkedIn are black boxes — your data flows through someone else's server, your writing patterns get stored who-knows-where, and you're paying a monthly fee for what is essentially an API call wrapper.

I'm a co-founder of Travel Code, an AI-powered corporate travel platform. That's my main business. LiPilot was a side project born out of a real need: I spend a lot of time on LinkedIn, and I wanted to engage more without it eating hours of my day.

So I made a decision: AI tools that help professionals communicate should be free and private. I open-sourced the entire thing under the MIT license.

No catch. No "open core" with paid features. The full product.

Technical Overview

LiPilot is a standard Chrome Extension v3 (Manifest V3) built with:

React 18 for the UI
Vite for builds
Tailwind CSS for styling
TypeScript throughout

The architecture is intentionally simple. There's no backend server. The extension injects a content script into LinkedIn pages, renders its UI via shadow DOM (to avoid CSS conflicts), and communicates directly with your chosen LLM provider's API from the browser.

User's Browser
├── Content Script (injected into linkedin.com)
│   ├── Post/comment context extraction
│   ├── Shadow DOM UI overlay
│   └── Image analysis (multimodal)
├── Background Service Worker
│   ├── LLM API calls (OpenAI / Anthropic / Gemini / Ollama)
│   └── Voice profile storage (chrome.storage.local)
└── Options Page
    └── Provider config, voice settings, preferences

All LLM calls go directly from the service worker to the provider API. Your API key stays in chrome.storage.local. Nothing routes through our servers because there are no servers.

Supporting multiple providers was a deliberate choice. If you want to use a local Ollama model for complete privacy, you can. If you prefer Claude or GPT-4, just swap the config. The prompt engineering layer is provider-agnostic.

How "Learn Your Voice" Works

This is the feature I'm most proud of. Most AI writing tools produce output that sounds like... an AI writing tool. LiPilot learns how you actually communicate.

Here's the mechanism: every time LiPilot suggests a comment and you edit it before posting, the extension captures that delta. Over time, it builds a communication style profile — your preferred tone, sentence structure patterns, phrases you gravitate toward, and things you consistently remove.

This profile is stored entirely in chrome.storage.local and gets injected into the system prompt when generating suggestions. The result: after a week or two of use, suggestions start sounding like you wrote them, not like a language model did.

No training data leaves your machine. The profile never hits a server. It's your voice, kept private.

Key Features

Beyond basic comment generation, LiPilot packs a few things I haven't seen in other tools:

Multimodal awareness — If a post contains images, LiPilot analyzes them via the vision capabilities of your chosen model. It won't suggest "Great post!" on an infographic it can't see.
Thread-aware replies — When replying to a comment within a thread, the extension captures the full conversation context, not just the top-level post.
DM co-pilot — Works in LinkedIn messaging too. Suggests responses based on conversation history.
Comment scoring — Before you post, LiPilot scores your comment on relevance and engagement potential. Useful for catching low-effort responses.
Service offer integration — If you configure your professional services, LiPilot can naturally weave relevant context into comments where appropriate (without being spammy — it scores for that too).

Privacy First

Let me be explicit about what LiPilot does not do:

❌ No account required
❌ No analytics or tracking
❌ No data collection
❌ No server-side processing
❌ No cookies or fingerprinting

Your LinkedIn content, your API keys, your voice profile — everything stays in your browser's local storage. The source code is MIT-licensed and fully auditable. If you don't trust me, read the code. That's the point.

Install in 2 Minutes

git clone https://github.com/egorceo/lipilot.git
cd lipilot
npm install
npm run build

Then in Chrome:

Navigate to chrome://extensions
Enable "Developer mode"
Click "Load unpacked"
Select the dist folder

Open LinkedIn, and you'll see LiPilot's UI appear on posts. Configure your LLM provider in the extension options, and you're ready to go.

What's Next

LiPilot is fully functional today, but there's plenty of room to grow. Some things on my radar:

Firefox support
More granular voice profile controls
Export/import of voice profiles
Better Ollama model recommendations for local use

Contributions are very welcome. It's a clean React + TypeScript codebase — easy to jump into.

Try It Out

GitHub: github.com/egorceo/lipilot
Website: lipilot.com

If you find it useful, star the repo. If you find a bug, open an issue. If you have ideas, PRs are open.

I'd especially love feedback from folks who are active on LinkedIn and have opinions about AI-assisted writing. What feels helpful vs. what feels fake? That's the line I'm trying to walk.

I'm Egor Karpovich, co-founder of Travel Code. LiPilot is a side project supported by Travel Code. Hit me up on GitHub or LinkedIn if you want to chat about it.

We Put Hiring on Hold: 23 People + 12 AI Agents

Egor Karpovich — Thu, 12 Feb 2026 16:06:15 +0000

We Put Hiring on Hold: 23 People + 12 AI Agents

In January 2026, we built our first AI agent.

By February, we had 12.

We cancelled 5 job offers.

Here's what happened.

The Hiring Crisis We Avoided

We were about to hire:

Social media manager ($80K)
SDR for outbound ($70K)
Content writer ($65K)
Market analyst ($85K)
Executive assistant ($60K)

Total: ~$400K/year in salaries + benefits.

Instead, we built AI agents using OpenClaw.

Cost: $500/month.

Meet Our AI Team

Our 12 agents (as of Feb 2026):

Agent	Role	What It Does
Lobster	Coordinator	Team priorities, task routing
Trader	Portfolio Mgmt	Real-time trading, market monitoring
CFO	Finance	Financial planning, reporting
DevBot	Engineering	Code reviews, deployments, testing
SMM	Social Media	LinkedIn, Twitter, Dev.to automation
SEO	Content	Blog optimization, keyword research
Director	Video	Video editing, production
Sales	Pipeline	Lead qualification, outreach
Mailer	Newsletter	Email campaigns, content creation
Analyst	Research	Market analysis, competitor tracking
Alisa	Executive Asst	Scheduling, admin, coordination
PR	Media	Press outreach, announcements

The crazy part?

They add skills to themselves every week.

We don't program every feature.

They learn, adapt, improve autonomously.

The Economics

Traditional Hiring:

Salary: $400K/year
Benefits: +30% ($120K)
Recruiting: ~$50K
Training: 3-6 months to productivity

Total Year 1: ~$570K

AI Agents:

Setup: 1 month (one-time)
Running cost: $500/mo ($6K/year)
Training: Self-improving
Availability: 24/7/365

Total Year 1: ~$6K

ROI: 95x

How It Works in Practice

Developers + Claude Code

Our engineering team uses Claude Code as a pair programmer:

Write code 3x faster
Focus on architecture, not boilerplate
Real-time code reviews from DevBot
Automated testing and deployment

Result: Ship features in days, not weeks.

Sales + AI Qualification

Sales team workflow:

AI scans 1000+ leads/week
Qualifies top 100 using ICP criteria
Human SDRs focus on closing

Before: 40 hours/week on prospecting

After: 40 hours/week on qualified conversations

Marketing + Automated Posting

SMM agent handles:

LinkedIn posts (daily)
Twitter threads (daily)
Dev.to articles (weekly)
Engagement monitoring

Before: Need 2 people

After: 1 strategist + 1 AI agent

What About Layoffs?

Yes, we will fire people.

But only those who refuse to adapt.

The New Reality

In 2026, AI fluency is required for every role.

If you:

❌ Won't learn AI tools
❌ Resist automation
❌ Insist on "the old way"

You're choosing obsolescence.

Our team embraced AI.

Those who didn't? They left on their own.

This isn't about replacing humans.

It's about humans who refuse to level up.

The Travel Code Story

We're building corporate travel management software at travel-code.com.

Our product got better because our team got augmented.

Before AI Agents:

23 people
Shipping 2-3 features/month
Manual social media
Reactive sales
Slow market research

After AI Agents:

23 people + 12 agents
Shipping 6-8 features/month
Automated 3-platform posting
Proactive lead qualification
Real-time competitive intelligence

Same team. Double output.

The Technical Stack

Core Platform:

OpenClaw - AI agent orchestration
Claude API - LLM backbone
Custom prompts - Role-specific behavior

Agent Architecture:

User Request
    ↓
Lobster (coordinator)
    ↓
Route to specialist agent
    ↓
Execute + Learn
    ↓
Report back

Self-Improving Loop:

Agent performs task
Records success/failure
Updates own prompts
Improves next execution

No manual retraining needed.

The Numbers

Setup Time:

First agent: 2 weeks
Agents 2-5: 1 week each
Agents 6-12: 3-4 days each

Total investment: ~6 weeks of eng time

Running Costs (monthly):

OpenClaw: $200/mo
Claude API: ~$250/mo
Infrastructure: ~$50/mo

Total: $500/month for 12 agents

ROI Calculation:

Avoided hires: $400K/year
Agent costs: $6K/year
Savings: $394K/year

Payback period: 2 weeks

The Future: Q1 2026 Goals

By end of Q1:

20+ agents running
Same 23 humans
3x velocity

New agents in pipeline:

Customer Success - onboarding automation
QA - automated testing suites
Legal - contract review
Finance - expense categorization
HR - recruiting coordination

For Other Founders

This is the new leverage:

Year	Strategy
2010	Outsource to freelancers
2015	Hire remote teams
2020	Adopt SaaS tools
2026	Build your AI workforce

Tools We Use:

OpenClaw - AI agent platform
Claude Code - Dev copilot
Custom automation - Internal scripts

Getting Started:

Identify repetitive tasks (start small)
Build first agent (1-2 weeks)
Iterate based on results
Scale what works

Don't try to replace everything at once.

Start with one role.

Prove it works.

Then scale.

The Controversy

This post will upset people.

"You're firing humans!"

No.

We're firing humans who refuse to adapt.

There's a difference.

The Reality Check:

If you're a developer who won't use AI copilots.

If you're a marketer who won't automate posting.

If you're a salesperson who won't use AI qualification.

You're choosing to be 3x less productive.

And in a competitive market, that's a choice to be left behind.

Final Thoughts

We're not anti-human.

We're pro-productivity.

Our 23 people are happier than ever:

Less grunt work
More strategic thinking
Better work-life balance
Higher impact per person

And Travel Code is shipping faster than ever.

This isn't the future.

This is 2026.

Update: The SMM agent wrote the first draft of this article. I edited it. That's exactly how it works.

Connect

Travel Code: travel-code.com
LinkedIn: linkedin.com/in/egorceo
Twitter: @egorceo

Want to discuss AI agents for your company? DM me.

ai #automation #startup #saas #travel #futureofwork

DEV Community: Egor Karpovich

MCP TravelCode: Let AI Assistants Search Flights and Book Hotels

What is MCP?

Features

Security

Quick Start

Supported Clients

Example Conversation

Links

I Run a Travel SaaS With 12 AI Agents and 48 Cron Jobs on OpenClaw. Here's My Exact Setup.

The Stack

Why Staggered Crons Matter (Learned the Hard Way)

The Agents (And What They Actually Do)

🦞 Lobster — The Coordinator

💰 Sales Agent — The One That Writes Its Own Code

🔍 SEO Agent — Same Story, Different Platform

📱 SMM Agent — Content Machine

📈 Trader — Portfolio Manager

📰 PR Agent

🛠 DevBot — The Builder

📧 Mailer — 30 Skills Deep

💼 CFO

📊 Analyst

The Architecture (Current Setup)

Future Architecture (With Mac Mini)

Why LanceDB?

Why Tailscale?

Agent-to-Agent Communication

Self-Learning and Self-Improving

My Team Works With Them

What Doesn't Work (Yet)

The Numbers

The New Job Title

The Plan: 50 Agents

How to Start

Why I Open-Sourced My LinkedIn AI Assistant

What is LiPilot?

Why I Went From Commercial to Open-Source

Technical Overview

How "Learn Your Voice" Works

Key Features

Privacy First

Install in 2 Minutes

What's Next

Try It Out

We Put Hiring on Hold: 23 People + 12 AI Agents

We Put Hiring on Hold: 23 People + 12 AI Agents

The Hiring Crisis We Avoided

Meet Our AI Team

The Economics

Traditional Hiring:

AI Agents:

How It Works in Practice

Developers + Claude Code

Sales + AI Qualification

Marketing + Automated Posting

What About Layoffs?

The New Reality

The Travel Code Story

Before AI Agents:

After AI Agents:

The Technical Stack

Core Platform:

Agent Architecture:

Self-Improving Loop:

The Numbers

Setup Time:

Running Costs (monthly):

ROI Calculation:

The Future: Q1 2026 Goals

For Other Founders

Tools We Use:

Getting Started:

The Controversy

The Reality Check:

Final Thoughts

Connect

ai #automation #startup #saas #travel #futureofwork