Long-Running Agents: From Idea to Deployed Landing Page in One Prompt

Executive Summary: Long-running agents are AI systems that execute 10+ minute (or hour-long) workflows autonomously, orchestrating dozens of steps from research to deployment. Unlike "quick agents" with 1-2 tool calls, they manage complex, end-to-end processes. This article demonstrates a concrete example: creating a complete landing page from one prompt to live URL in 16 minutes for $0.60. The technical architecture combines Daytona Sandboxes, MCP Server, Claude Sonnet, and Composio. The result: 80% directly usable, 20% need manual refinement. The future: multi-day agents that build complete SaaS products.

The Problem with Today's Agents: They Forget

Imagine having a brilliant employee. But every 30 minutes, they forget everything.

That's the reality of most AI agents today.

The Context Window Limitation

Today:

• Claude Sonnet: 200K tokens (~150,000 words)
• GPT-4 Turbo: 128K tokens (~96,000 words)
• Gemini 1.5 Pro: 1M tokens (~750,000 words)

Sounds like a lot? For short tasks, yes. For complex, long-term projects? Hopelessly insufficient.

Example: Software Development

An agent is tasked with building a SaaS product:

Day 1: Plans architecture, writes frontend
Day 2: Context full → forgets Day 1 details → must re-learn
Day 3: Context full → forgets Days 1+2 → code inconsistencies
Day 5: Context management overhead > actual work

Result: Agent spends 50% of time "remembering" instead of "building."

Today's Workarounds

To solve this, we currently use:

1. Hierarchical Memory

Working Memory → Short-Term → Long-Term → Archive

Problem: Information loss at each level.

2. Vector Databases

Important facts → Embedding → Storage → Retrieval when needed

Problem: Agent doesn't always know what to search for.

3. Summary Chains

After each step: Summarize what was important

Problem: Summaries lose nuance.

All workarounds = crutches. What we need: Unlimited context.

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What Are Long-Running Agents?

Definition:

Long-running agents are autonomous AI systems that execute complex, multi-step workflows over extended periods—typically 10+ minutes, sometimes hours or days.

Long-Running vs. Quick Agents

Quick Agents:

• Use Case: "What's the weather today?"
• Workflow: 1 tool call → Weather API → Answer
• Duration: 2-5 seconds
• Complexity: Low

Long-Running Agents:

• Use Case: "Create a landing page for my startup"
• Workflow: 50+ tool calls → Research, Write, Design, Code, Deploy
• Duration: 10-60 minutes
• Complexity: High

The fundamental difference:

Why They're the Future

Long-running agents are the next evolutionary step in AI development:

✅ 1. They Replace Entire Workflows, Not Just Tasks

Before: Human orchestrates tools
Today: Agent orchestrates tools autonomously

Example Web Design:

• Without Agent: Designer researches → writes copy → creates mockups → codes → deploys (8-40 hours)
• With Long-Running Agent: One prompt → Agent does everything (16 minutes)

✅ 2. They Scale Expertise

Problem: Good freelancers are expensive and booked solid
Solution: Long-running agent with the same knowledge

Example:

• Freelancer Landing Page: $2,000-5,000, 1-2 weeks
• Long-Running Agent: $0.60, 16 minutes
• Scaling: 1,000x faster, 5,000x cheaper

✅ 3. They're Available 24/7

Human: 8h/day, weekends off, vacation, sick
Agent: 24/7, no downtime, instant start

Business Impact:

• Idea at 11 PM → Landing page live at 11:16 PM
• 100 landing pages in parallel (impossible for humans)

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The Challenges

Long-running agents are technically demanding. The biggest challenges:

1. Context Management Over Time

Problem: LLMs have limited context windows

Example:

• Claude Sonnet: 200K tokens context window
• 16-minute workflow: Generates 500K+ tokens output (research, copy, code)
• Conflict: 500K > 200K = context gets lost

Solution: Hierarchical Memory

Don't keep everything in context, but selectively remember:

Agent Memory Structure:
├─ Working Memory (current step)
├─ Short-Term Memory (last 5 steps)
├─ Long-Term Memory (important facts)
└─ Archive (everything else, retrievable on demand)

Anewera's Approach:

• Working Memory: Only current task (e.g., "Write code")
• Short-Term: Relevant info from previous steps
• Long-Term: User goals, design decisions, brand guidelines
• Archive: Full research raw data (load only when needed)

2. Error Handling in Multi-Step Workflows

Problem: One error in Step 5 kills the entire 16-minute workflow

Error Scenarios:

• API rate limit reached
• Invalid tool call syntax
• Image generation fails
• Deployment error

Solution: Resilient Execution

Strategy 1: Retry with Backoff

Step failed → Wait 5s → Retry
Failed again → Wait 15s → Retry
Failed again → Wait 45s → Alternative route

Strategy 2: Fallback Options

Generate hero image with DALL-E → Error
→ Fallback: Search Unsplash for stock image
→ Workflow continues

Strategy 3: Partial Success

Steps 1-5 successful → Step 6 failed
→ Save progress
→ User can restart from Step 6
→ No waste of Steps 1-5

3. Cost Control (Many LLM Calls)

Problem: 16-minute workflow = 50+ LLM calls = high costs

Cost Breakdown:

• Research: 10 Exa Searches @ $0.01 = $0.10
• LLM Reasoning: 30 Claude calls @ $0.01 = $0.30
• Image Gen: 1 DALL-E call = $0.04
• Code Execution: Daytona Sandbox = $0.05
• Deployment: Vercel API = $0.01
• Total: $0.50

But: What if the agent gets stuck in loops?

Horror Scenario:

Agent tries to fix code → Error
→ New code attempt → Error
→ 100 iterations later → $50 burned

Solution: Cost Guardrails

Max Budget per Agent:

• User sets budget (e.g., $2.00)
• Agent stops automatically when exceeded
• Warning at 80% budget reached

Smart Routing:

• Simple tasks → Haiku ($0.0008/K)
• Complex tasks → Sonnet ($0.003/K)
• → 60% cost savings without quality loss

4. User Experience (Waiting vs. Progress Updates)

Problem: User waits 16 minutes—what's happening?

Bad UX:

User: "Create landing page"
System: [16 minutes silence]
System: "Done! Here's your page."

Good UX:

User: "Create landing page"
System: ✅ Market research running... (5%)
System: ✅ Market research complete (30%)
System: ✅ Copywriting running... (35%)
System: ✅ Copy done (60%)
System: ✅ Hero image generated (75%)
System: ✅ Code written (90%)
System: ✅ Deployment running... (95%)
System: ✅ Live! Here's your URL: example.com (100%)

Anewera's Progress System:

• Real-time streaming: User sees every step
• Estimated time: "About 8 minutes remaining"
• Pause/Resume: User can pause workflow
• Notification: Email/Slack when complete

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The Use Case: Landing Page in One Prompt

The Prompt:

Create a landing page for a startup that sells AI agents for dental 
practices. Research the target audience, create copy, generate a hero 
image, code the page, and deploy it live.

One sentence. 16 minutes later: Finished, live landing page.

Here's how it works step-by-step:

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Step 1: Market Research (5 Min)

What the agent does:

✅ Exa Search: Analyze dental practices

• Query: "Dental practice challenges patient management"
• Finds: Appointment management, patient communication, billing

✅ Competitor Analysis: Other Dental Tech Startups

• Query: "Dental Tech SaaS"
• Finds: Denteo, CareStack, etc.
• Analyzes: What do they offer? What's missing?

✅ Identify Pain Points

• Synthesizes from research:
  • ❌ "Too many no-shows for appointments"
  • ❌ "Manual recall emails time-consuming"
  • ❌ "Weekend patient inquiries go unanswered"

Output:

Target Audience: Dental practices (1-5 dentists)
Pain Points: No-shows, manual communication, weekend inquiries
Unique Value Prop: AI agent handles patient communication 24/7

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Step 2: Copywriting (3 Min)

What the agent does:

✅ Write headline (A/B variants)

Variant A:
"24/7 Patient Communication – Your AI Assistant for Dental Practices"

Variant B:
"Never Miss a Patient Inquiry Again. Your AI Agent Works Around the Clock."

Decision: Agent chooses Variant B (direct benefit)

✅ Formulate value proposition

"Our AI agent answers patient inquiries, confirms appointments, and sends automatic recall emails—even on weekends. Reduce no-shows by 40% and save 5 hours per week."

✅ Create CTA texts

• Primary CTA: "Start Free Trial"
• Secondary CTA: "Request Demo"

✅ Integrate SEO keywords

Keywords: "dental AI", "practice management", "patient communication"

Output:

Headline: "Never miss a patient inquiry..."
Value Prop: "Our AI agent answers..."
CTA: "Start Free Trial"
SEO: Keywords integrated

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Step 3: Design Concept (2 Min)

What the agent does:

✅ Choose color scheme (based on dental industry)

Research: Dental = Trust, Cleanliness, Professionalism

Colors:

• Primary: Light blue (#4A90E2) – Trust
• Secondary: White (#FFFFFF) – Cleanliness
• Accent: Green (#2ECC71) – Health

✅ Define layout

Sections:

1. Hero (Headline + CTA + Image)
2. Features (3 main functions)
3. Social Proof (Testimonials)
4. Pricing (Simple 1-tier)
5. Final CTA

✅ Select font pairings

• Headline: Montserrat (modern, professional)
• Body: Open Sans (readable, friendly)

Output:

Colors: Blue/White/Green
Layout: Hero → Features → Social Proof → Pricing → CTA
Fonts: Montserrat + Open Sans

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Step 4: Hero Image Generation (2 Min)

What the agent does:

✅ Create prompt for image generator

Image Prompt:

"Modern dental office with friendly female dentist smiling at camera, 
bright natural light, minimalist design, blue and white color scheme, 
professional photography, high quality, 16:9 aspect ratio"

✅ Generate image (via DALL-E API)

API call → 30 seconds → Image URL returned

✅ Optimize image

• Original: 2MB PNG
• Optimized: 200KB WebP (via Cloudinary)
• Dimensions: 1920x1080 → scales responsively

Output:

Hero Image: https://cdn.example.com/dental-hero.webp
Optimized: 200KB, WebP, responsive

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Step 5: Code Writing (3 Min)

What the agent does:

✅ Generate HTML/CSS/JS

HTML Structure:

• Semantic HTML5
• Responsive grid layout
• Accessibility (ARIA labels, alt tags)

✅ Ensure responsive design

• Mobile-first CSS
• Breakpoints: 768px, 1024px, 1440px
• Touch-friendly buttons (min 44x44px)

✅ Add SEO meta tags

<meta name="description" content="...">
<meta property="og:title" content="...">
<meta property="og:image" content="...">
<link rel="canonical" href="...">

✅ Integrate analytics

<!-- Plausible Analytics -->
<script defer data-domain="dental-ai-demo.com" 
  src="https://plausible.io/js/script.js"></script>

Output:

Code: HTML/CSS/JS complete
Responsive: Yes (Mobile-first)
SEO: Meta tags set
Analytics: Plausible integrated

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Step 6: Deployment (1 Min)

What the agent does:

✅ Push code to hosting

• Push code to Git repo
• Trigger Vercel deployment

✅ Configure domain

• Create subdomain dental-ai-demo.anewera.ch
• Set DNS records automatically

✅ Activate SSL certificate

• Request Let's Encrypt certificate automatically
• HTTPS active after 30 seconds

✅ Return live URL

Output:

✅ Deployment successful!
🌐 Live URL: https://dental-ai-demo.anewera.ch
🔒 SSL active
📊 Analytics running

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Total: 16 Minutes

Summary:

From one prompt to live page: 16 minutes. No human intervention.

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The Technical Architecture

How does Anewera orchestrate 6 complex steps in 16 minutes?

1. Daytona Sandbox for Code Execution

Why important:
Agent must execute code (not just generate it)

Daytona provides:

• Isolated Linux containers
• Root access for npm install, git push
• Snapshot function (save code versions)

Concrete:

Agent generates code → Daytona Sandbox starts
→ Code is executed → Build successful
→ Output returned to agent

2. MCP Server for Tool Orchestration

Why important:
Agent needs access to 10+ tools

MCP provides:

• Standardized tool interface
• Exa Search, DALL-E, Vercel API, Git, etc.
• Error handling per tool

Concrete:

Agent: "Need competitor analysis"
→ MCP: Execute Exa Search tool
→ Result back to agent

3. Claude Sonnet for Reasoning

Why important:
Agent must plan and decide

Claude Sonnet provides:

• 200K context window (for 16-min workflow)
• XML tool use (better orchestration)
• Self-correction (error recovery)

Concrete:

Claude plans: "Step 1 → Research, Step 2 → Copy, ..."
→ Executes tools
→ Evaluates results
→ Decides next step

4. Composio for External APIs

Why important:
Agent needs access to external services

Composio provides:

• Pre-built integrations: Vercel, GitHub, Slack
• OAuth handling
• Rate limiting

Concrete:

Agent: "Deploy code on Vercel"
→ Composio: Vercel API call with user OAuth
→ Deployment successful

5. Streaming for Progress Updates

Why important:
User waits 16 minutes—needs feedback

Streaming provides:

• Real-time updates to frontend
• Server-Sent Events (SSE)
• Progress percentage

Concrete:

Backend: "Step 1 starting..."
→ SSE stream to frontend
→ Frontend shows: "✅ Market research running... (5%)"

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The Cost Calculation

Transparency: What does a landing page via long-running agent cost?

LLM Costs: ~$0.50 per Landing Page

Breakdown:

Infrastructure: ~$0.10 per Landing Page

Breakdown:

Total: $0.60 per Landing Page

Comparison:

ROI Calculation:

Freelancer: $3,000 / Agent: $0.60 = 5,000x cheaper
Freelancer: 1 week / Agent: 16 min = 630x faster

But: Quality isn't 1:1 identical (see "Real-World Limitations")

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Real-World Limitations

Honesty: Long-running agents are not perfect.

1. Quality: 80% Good, 20% Need Manual Tweaking

What works well:

• ✅ Structure (HTML, layout, sections)
• ✅ SEO meta tags
• ✅ Responsive design
• ✅ Copy (basic quality)

What often needs tweaking:

• ⚠️ Design details (spacing, color nuances)
• ⚠️ Copy tone (too generic)
• ⚠️ Image selection (sometimes off-brand)
• ⚠️ CTA placement (not optimal)

Example:

Agent Output:

Headline: "Never miss a patient inquiry"

Human-optimized:

Headline: "Your practice answers even on Sunday—automatically"

→ 10% punchier, more emotional

2. Creativity: Agents Aren't (Yet) as Creative as Humans

Problem: LLMs generate probable outputs, not surprising ones

Example Design:

Agent chooses:

• Blue/White (standard for medical)
• Montserrat font (popular)
• Hero section on top (classic)

Human designer might:

• Choose surprising green/orange scheme
• Use custom illustrations instead of stock photos
• Asymmetric layout with wow effect

→ Agent = solid, but not "award-winning"

3. Edge Cases: Complex Requirements Overwhelm Agents

Example:

Simple Request (works):

"Create landing page for Dental Tech Startup"
→ Agent does it without problems

Complex Request (overwhelms):

"Create landing page with interactive 3D tooth model rotation, 
integrated appointment booking with calendar sync, multi-language 
support (EN/FR/IT), and custom scroll animations"
→ Agent fails at 3D integration

Rule of thumb:

• Simple to Medium: Agent manages autonomously
• High Complexity: Agent needs human co-pilots

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The Future: Even Longer Agents

Long-running agents today: 10-60 minutes
Long-running agents tomorrow: Hours to days

With Larger Context Windows (1M+ Tokens)

Today: Claude Sonnet = 200K tokens
Soon: Gemini 1.5 Pro = 1M tokens, GPT-5 = 1M+ tokens?

What this enables:

• Agents retain complete context for hours
• No memory compression needed
• More complex workflows without information loss

Example:

Today: "Create landing page" (200K tokens = 16 min)
Future: "Create complete marketing funnel with 10 pages, 
         email sequence, and social ads" (1M tokens = 2 hours)

Multi-Day Agents (e.g., "Build Me a SaaS Product")

Vision:

Prompt:

"Build me a SaaS product for dental practices: 
Patient CRM with AI chat, appointment booking, billing. 
Frontend in React, backend in Python, deploy on AWS."

Agent works for 48 hours:

• Day 1 Morning: Research, design, architecture
• Day 1 Afternoon: Write frontend code
• Day 1 Evening: Develop backend API
• Day 2 Morning: Create database schema
• Day 2 Afternoon: Integration testing
• Day 2 Evening: Deployment, security audit

Result: Working MVP in 2 days

Cost: ~$50-100 (vs. $50,000 agency)

Fully Autonomous Agents (Without Human Intervention)

Today: Agents need human approval for critical steps

Future: Agents work completely autonomously

Scenario:

Startup Founder:

"Agent, build me a product, launch it, and acquire first customers."

Agent (48h later):

✅ MVP built (www.product.com)
✅ Landing page live
✅ Google Ads campaign started ($500 budget)
✅ First 10 signups generated
✅ Stripe payments integrated
📊 Dashboard: 2 paid conversions ($200 revenue)

→ From idea to first customers: 48h, autonomous

Challenges:

• Trust: Will user let agent spend $500?
• Legal: Who's liable for errors?
• Safety: How do we prevent harmful actions?

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Frequently Asked Questions (FAQ)

How long does a typical long-running agent run?
10-60 minutes for standard workflows (landing page, report creation). Multi-day agents for complex projects (SaaS MVP) are in development.

What does a long-running agent run cost?
$0.10-2.00 depending on complexity. A landing page costs ~$0.60 (LLM + infrastructure).

Can I stop the agent during execution?
Yes, at Anewera you can pause workflows, save intermediate states, and resume later.

How good is the quality vs. human work?
80% of agent outputs are directly usable. 20% need manual tweaking for polish. Design and copy are "good" but not "excellent".

What happens with errors in the workflow?
Agent attempts self-correction (3 retries with backoff). With persistent errors: fallback options or human handoff. Progress is always saved.

When will multi-day agents be available?
First pilots Q2 2025. Public launch depends on context window upgrades (1M+ tokens) from LLM providers.

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The Bottom Line: Long-Running Agents Are the Future of Work

Summary:

✅ Long-running agents orchestrate complex, multi-step workflows over 10+ minutes (or hours)

✅ They replace entire workflows, not just individual tasks—from research to design to deployment

✅ Concrete: Landing page in 16 minutes for $0.60 instead of $5,000 in 2 weeks

✅ Technical: Daytona Sandboxes + MCP + Claude Sonnet + Composio + Streaming

✅ Limitations: 80% directly usable, 20% need human tweaking; less creative than top designers; edge cases overwhelm

✅ Future: Multi-day agents (SaaS products in 48h), fully autonomous (from idea to customers without humans)

The implication: Knowledge and execution become democratized. Anyone can create a landing page in 16 minutes, a SaaS in 48 hours, a company in a week with one prompt.

The question isn't if, but when.

Want to use long-running agents in your business? Contact Anewera for a free consultation.

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