Phase 5: Orchestrated Agentic System
A learned orchestrator trained on Phase 4's discovery data unifies division-specific intelligence into a single coordinated system
Deliverables:
- • Production-ready orchestrated system built on Agno framework
- • Fine-tuned Qwen2.5-7B orchestrator trained on 71,000 Phase 4 examples
- • Single-window access to all AI capabilities (embedding, task SLMs, division agents)
- • Model-agnostic architecture supporting local, AWS Bedrock, Azure, or Databricks backends
- • Learned routing from real usage patterns with ~150ms inference latency
The Problem
Rule-based orchestration requires constant manual updates as workflows change. Hardcoded routing becomes a maintenance burden that drifts from actual organizational needs. "If request contains X keyword, call Y agent" fails when requests are ambiguous, when optimal paths require context, or when organizational needs shift. The brittleness increases exponentially with the number of agents and capabilities being coordinated.
The Solution
Train a small orchestrator model (Qwen2.5-7B) on Phase 4's discovery data to route requests intelligently without predetermined rules. The orchestrator learns optimal routing patterns from 90 days of real usage: which agent sequences work for different request types, which information handoffs are necessary, which coordination patterns deliver value. Training costs $20-50 over 4-6 hours. This architecture creates a natural point for retraining and fine-tuning the network over time as the organization evolves. It also provides transparency into what the system is doing, making refinement and troubleshooting easier.
The Value
Single-window access to all AI capabilities. Users make requests without knowing which agents, models, or divisions are involved. Learned routing that reflects real usage patterns and improves through retraining as the organization evolves. Built on the Agno framework for deployment flexibility: run entirely local, on cloud platforms like AWS Bedrock or Azure AI Foundry, or any model backend without rewriting coordination logic. Total Direct Investment across all phases reaches $163.1K, achieving orchestrated multi-agent AI at a fraction of the $2M-$7M traditional vendor platform cost while preserving competitive advantage through company-specific intelligence that compounds with use.
Next: Scaling to Production
With orchestrated intelligence in place, the next step is deploying this system to enterprise-grade infrastructure. The Scaling Production section covers how every phase's training output maps directly to AWS managed services, preserving your $163.1K investment while gaining enterprise scale, security, and operational simplicity.
Your models, agents, and orchestrator transfer to SageMaker, Aurora PostgreSQL, and ECS without retraining or architectural changes. The modular design built across all phases ensures each component deploys independently: start with a pilot and scale to enterprise as adoption grows.
Continue to Scaling Production →