πŸš€ ARIA is now live! Experience dynamic AI orchestration powered by Skymel OA Try ARIA Free β†’
Skymel Orchestration Agent Coming Soon

The Autopilot for Dynamic AI Workflows

Unlike static agent builders, Skymel OA creates agents at runtime that adapt their entire workflow per request. Build agents from prompts that reason globally, optimize across cost/speed/quality, and route seamlessly between cloud and edgeβ€”automatically.

6 weeks to developer access
Autopilot orchestration layer
Runtime agent creation
skymel_agent.py
# Create agent from simple description
from skymel import OrchestratorAgent

agent = OrchestratorAgent(
    description="Customer support agent that researches issues
                and provides solutions",
    budget=5.00
)

# Agent plans and executes automatically
result = await agent.run(
    "Customer says their premium subscription isn't working"
)
print(result.solution)
🎯 Agent Planning
β†’
πŸ”„ Autopilot Execution
β†’
πŸ“Š Runtime Optimization

See Dynamic Agents in Action: ARIA

ARIA demonstrates Skymel OA's autopilot capabilitiesβ€”creating agents that plan, adapt, and execute complex workflows from simple prompts, in real time.

skymel.com/ai-assistant.html
What's the best health insurance plan for a freelancer in California? Compare top options and costs.
ARIA Runtime Workflow: Sonar-Pro β†’ Gemini-2.5-Pro β†’ Claude-4-Sonnet β†’ GPT-4.1
Creating specialized research agents at runtime to analyze California health insurance options for freelancers...
πŸ“‹ ACA Plan Research: Finding 2025 Covered California plans for 40yo non-smoker
πŸ” Alternative Options: Researching 3 health share ministries accepting CA residents
πŸ’° Tax Analysis: Calculating QSEHRA, HSA eligibility, medical expense deductions
πŸ“Š Final Integration: Creating comparison matrix with cost-sensitive recommendations
Try ARIA

Experience live AI orchestration

Agent Creation at Runtime vs Dynamic Agents

Understanding the fundamental difference in how Skymel OA approaches AI orchestration

Traditional Dynamic Agents

Adaptive Structure

Agents that adapt their behavior based on input, but require pre-defined structure:

  • Pre-built tool lists and workflows
  • Fixed execution patterns with flexible parameters
  • Adapt within predefined constraints
  • Agent design built ahead of time

Skymel OA: Runtime Creation

Generated On-Demand

Agents created entirely from scratch at execution time:

  • Builds execution graph live, per request
  • Selects tools, models, and routing dynamically
  • Optimizes globally for cost, latency, quality
  • No pre-built agent definition required

Capability Comparison

Use Case
Traditional Dynamic
Skymel OA
Fully automatic agent creation
Limited
βœ… Yes
Dynamic agent with developer control
βœ… Yes
βœ… Yes
Real-time constraint optimization
Basic
βœ… Advanced
Global workflow reasoning
❌ No
βœ… Yes

Real Example: Health Insurance Research

Traditional Dynamic Agent Approach

"Compare top 3 health insurance plans in California for freelancers"

1. Use pre-configured web scraper tool
2. Apply fixed data extraction pattern
3. Run predefined comparison template
4. Generate standard report format

Limited by pre-built tools and fixed workflow structure

Skymel OA: Runtime Agent Creation

"Compare top 3 health insurance plans in California for freelancers"

1. Analyzes prompt β†’ identifies research + comparison + summarization needs
2. Selects optimal tools: web research, data extraction, cost analysis
3. Builds custom execution graph for freelancer-specific factors
4. Optimizes for accuracy vs speed based on context
5. Generates tailored report with cost tiers and recommendations

Agent structure, tools, and logic built specifically for this request

How Skymel OA Differs from Other AI Orchestration Platforms

Skymel OA supports both fully automatic runtime agent creation AND dynamic agents with developer control, giving you the flexibility to choose your level of orchestration.

Aspect
Traditional Platforms
Skymel OA
Agent Creation Options
Pre-built static agents or manual scripting
Runtime creation + developer-controlled dynamic agents
Flexibility Level
Fixed workflows with limited adaptability
Full automation OR granular developer control
Tool/model selection
Static lists or basic next-step selection
Dynamic selection optimized for global outcomes
Developer Control
All-or-nothing: full control or no control
Configurable: set constraints, let OA optimize within them
Optimization Scope
Local optimization per step
Global: cost, latency, quality, privacy policies
Infrastructure
Rare, mostly cloud only
Cloud/edge/hybrid, OA routes steps per constraints
Observability
Weak, manual logs, little control
Built in: every decision traceable, policy enforced

Two Ways to Use Skymel OA

πŸš€ Fully Automatic

Runtime Agent Creation

Just describe your goal. OA creates the entire agent from scratch.

agent = oa.run("Analyze competitor pricing and write a report")
# OA builds tools, plan, execution automatically

Perfect for rapid prototyping and high-level automation

πŸŽ›οΈ Developer Controlled

Dynamic Agents

Set constraints and tools. OA optimizes within your parameters.

agent = oa.run(
  goal="Analyze competitor pricing",
  allowed_tools=["web_scraper", "pdf_reader"],
  max_cost=0.05
)

Ideal for production apps requiring specific control

The Missing Autopilot Layer for AI Applications

As every company moves from single-model LLM demos to real AI-driven workflows and agentic apps, Skymel OA is the autopilot that makes it all possible: reliably, observably, and at scale.

πŸ”„

Agent Creation at Runtime

Unlike traditional dynamic agents that adapt pre-built structures, Skymel OA creates the entire agentβ€”plan, structure, tools, and model choicesβ€”from scratch at execution time based on your prompt and constraints.

No pre-built agent definition required
🧠

Global Reasoning & Optimization

OA looks at the entire plan, not just the next tool to call. Optimizes for business outcomes: fastest overall with highest quality under budget, never violate policy, handle errors automatically.

Cost, latency, quality, policy all at once
🌐

Infrastructure-Aware Routing

Routes steps between cloud and edge devices live per request using NeuroSplit. Multi-model and multi-tool, each step uses the best possible model, API, or data source selected based on your constraints.

Cloud/edge/hybrid per constraints
πŸ“Š

Workflow Transparency

See exactly how agents plan and execute tasks. Complete visibility into model choices, execution steps, and decision paths for debugging and optimization.

Complete workflow visibility
πŸŽ›οΈ

Developer-Friendly Design

Built for developers who need both automation and control. Set constraints, observe decisions, and iterate on agent behavior without complex configuration.

Simple API, powerful control
πŸ”„

Incremental Integration

Drop OA into a single component of your system and it plays nicely with the rest. Progressive adoption: start getting value in hours without refactoring your entire codebase.

Start small, scale gradually

From Single Prompt to Production Agent

Skymel OA transforms how developers build AI applications, from static chains to dynamic, self-managing agents.

1

Create Agent from Prompt

dynamic_agent.py
from skymel import OrchestratorAgent

# Agent created from natural language description
agent = OrchestratorAgent(
    prompt="Customer support agent that can research issues,
           provide solutions, and escalate when needed",
    policies={
        "budget": 5.00,
        "privacy": "customer_data_local"
    }
)
2

Autopilot Handles Everything

execution.py
# Agent handles the request automatically
response = await agent.run(
    "Customer says premium subscription isn't working"
)

# See what the agent did
print(response.solution)
print(response.next_steps)

Autopilot Creates and Manages Dynamic Agents

πŸ€–
Agent Generation

Creates specialized agent from your prompt with the right capabilities, tools, and constraints

🎯
Dynamic Planning

Agent analyzes the request and creates an optimal execution plan with multiple steps and decision points

πŸ”„
Adaptive Execution

Autopilot executes the plan, adapting in real-time based on results, errors, and changing requirements

πŸ“Š
Runtime Optimization

Continuously optimizes performance, cost, and quality while maintaining full observability and compliance

πŸ”’
Security & Privacy

Enterprise-grade security designed for production workloads with data privacy controls

πŸ“ˆ
Scalable Architecture

Built to handle growing workloads with cloud and edge orchestration capabilities

πŸ› οΈ
Observability

Complete visibility into agent decisions and workflow execution for debugging and optimization

πŸ”„
Reliable Operations

Designed for production reliability with error handling and fallback mechanisms

Built for Enterprise Adoption

Start with one workflow. Scale gradually as you see value. Designed to work alongside your existing systems.

βœ…

Incremental Adoption

Begin with a single use case and expand gradually across your organization

βœ…

Audit & Transparency

Complete visibility into agent decisions and workflows for operational oversight

βœ…

Intelligent Cost Management

Automatic model selection and optimization helps control AI infrastructure spending

Your Applications
Customer Support Analytics Content Gen
Skymel OA Platform
Orchestration Engine Model Registry Policy Engine
AI Models & Services
OpenAI Claude Gemini Your APIs

Get Early Access

Be among the first to build with Skymel OA

βœ…

ARIA Public Demo

Experience Skymel OA capabilities through ARIA

Available Now
πŸ”₯

Design Partner Program

Work directly with our team to shape the platform

Accepting Applications
πŸš€

Developer SDK Release

Full access to Skymel OA APIs and development tools

6 Weeks

Ready to Build the Future of AI?

Join developers building intelligent, adaptive AI workflows with Skymel OA