Jeremy Longshore

Transforming a Generic Orchestrator into a Business Product

I recently completed fine-tuning a hierarchical multi-agent AI system deployed on Google’s Vertex AI Agent Engine. The challenge wasn’t just improving performance—it was transforming a generic orchestrator into a deployable business product with clear identity, decision-making frameworks, and professional standards.

This is the kind of architectural refinement that separates experimental AI from production-ready systems.

The Business Context

Product: IAM1 (Regional Manager AI Agent) Platform: Vertex AI Agent Engine Business Model: Deployable per client with specialist team members

IAM1/IAM2 Hierarchy:

Revenue Strategy:

Each deployment is isolated with client-specific knowledge grounding via Vertex AI Search.

The Problem: Misalignment Between Architecture and Business Model

Initial State:

What I Needed:

This wasn’t a prompt engineering exercise. It was systems architecture work.

Solution Architecture

1. Identity-Driven Design

I rewrote the IAM1 instruction to establish clear identity and role:

Core Identity:

Why This Matters: Business products need clear boundaries. An IAM1 deployed to Sales can coordinate with Engineering IAM1 but can’t command it. This mirrors real organizational structures.

2. Decision Framework Implementation

I implemented a step-by-step decision framework:

1. Simple questions (greetings, basic info) → Answer directly
2. Knowledge questions (facts, documentation) → Use RAG tool
3. Complex specialized tasks → Route to IAM2 specialist
4. Multi-step tasks → Coordinate multiple IAM2s, synthesize results

Impact:

3. Professional IAM2 Standardization

I upgraded all 4 IAM2 specialists with:

Example - Research IAM2 Deliverable Format:

  1. Executive summary
  2. Findings with evidence
  3. Citations/sources
  4. Recommendations

Result: Consistent, professional outputs across all specialists.

4. Enhanced Error Handling and Transparency

I improved the routing function with:

Implementation Results

Deployment:

Files Modified:

Deployment Time: ~3 minutes via automated pipeline

Key Methodologies Applied

1. Systems Thinking

I didn’t just optimize individual components. I designed a system with:

2. Product-Oriented Architecture

The agent isn’t just “smart”—it’s a deployable product:

3. Professional Standards

I implemented quality standards across the system:

4. Iterative Refinement

This work built on prior experience:

Business Impact

Before Fine-Tuning:

After Fine-Tuning:

Next Client Deployment Steps:

  1. Create isolated GCP project
  2. Deploy IAM1 infrastructure via Terraform
  3. Upload client-specific knowledge to Cloud Storage
  4. Run data ingestion pipeline
  5. Deploy IAM1 (same codebase, client-specific grounding)
  6. Optional: Add IAM2 specialists based on needs

Technical Leadership Insights

1. Architecture Precedes Optimization

Don’t optimize a poorly-architected system. Design the right structure first, then refine.

2. Identity Drives Behavior

Clear identity (IAM1 Regional Manager) drives better decisions than generic prompts.

3. Standards Enable Scale

Standardized deliverables from IAM2s enable consistent quality across deployments.

4. Business Model Shapes Architecture

The revenue model (per-deployment + specialists) directly influenced the IAM1/IAM2 hierarchy design.

What This Demonstrates

For Employers/Clients:

Technical Capabilities:

Conclusion

Fine-tuning a multi-agent AI system isn’t just about better prompts. It’s about:

  1. Clear Architecture - Hierarchies, decision frameworks, standards
  2. Business Alignment - Product identity, deployment model, revenue strategy
  3. Professional Quality - Standardized deliverables, error handling, observability
  4. Systems Thinking - How components interact, scale, and deliver value

The result is a deployable business product, not just an impressive demo.

This is the kind of technical leadership work I bring to production AI systems.

Jeremy Longshore is a solutions architect specializing in production AI systems and multi-agent architectures. LinkedIn | X

#Technical-Leadership #Ai-Architecture #Vertex-Ai #Multi-Agent-Systems #Product-Development