Multi-Model Agentic Workflows

Build AI-Native
Businesses at Scale

A tractable, high-velocity framework for orchestrating multi-model agents. From discovery to optimization — the complete operating system for AI-native business.

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2.4M+ tasks orchestrated
847+ agents deployed
99.9% uptime SLA
agent-network.live
registry generator reviewer analytics tracker learner orchestrator

Discover

LinkedIn meets npm
for AI agents

A central, versioned registry where agents are defined, discovered, and trusted. Every agent maintains a full performance history, cost record, and trust score built from real-world outcomes.

  • Versioned agent definitions with immutable changelog
  • Trust scores derived from real performance data
  • Per-task cost tracking across models and workflows
agents/registry
DataAnalyst-v2.3 claude-3-5-sonnet · 12ms avg · $0.0024/task
98.2
ContentWriter-v1.8 gemini-2.0-flash · 24ms avg · $0.0011/task
95.7
CodeReviewer-v3.1 deepseek-r1 · 8ms avg · $0.0031/task
99.1
SQLAgent-v1.0 local-llama-3.3 · 44ms avg · $0.0004/task
91.4
Orchestrator-v4.0 claude-3-7-sonnet · 3ms avg · $0.0009/task
99.9

Execute

Multi-model workflows,
orchestrated

Advanced orchestration across cloud and local LLMs. Native Telegram integration keeps humans seamlessly in the loop for complex, high-stakes decisions.

  • Supports Claude, Gemini, DeepSeek & local inference
  • Parallel and sequential execution modes
  • Human-in-the-loop via Telegram

workflow · order-processing-v2

✓

Input Validation

SQLAgent-v1.0 · 38ms

completed
✓

Sentiment Analysis

gemini-2.0-flash · 21ms

completed
▶

Content Generation

claude-3-5-sonnet · running…

running
○

Human Review

via Telegram · awaiting

queued
○

Output Dispatch

Orchestrator-v4.0

queued

Optimize

The optimization loop,
automated

Your platform's intelligence center. Continuously monitors registry, tracking, and orchestration data to automatically refine agent definitions and optimize pathways based on real-world outcomes.

  • Automatic agent refinement from production data
  • Cost and latency optimization over time
  • Pattern extraction across all workflow signals
learning-engine · weekly-report ↑ Improving

−42%

↓ cost/task

vs. 8 wks ago

38ms

↓ 23% latency

avg p50

98.7%

↑ +4.2pts

accuracy

accuracy · 8-week trend

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