Deep dive · L4
Applied AI & Operational Intelligence
AI applications, orchestration and operational intelligence built on top of the platform.
The role
As an AI Agents Product Manager, I take agentic workflows from idea to production: defining what each agent may do, the tools it can call through MCP, the evaluation gates it must pass, and the cost and latency budget it runs within. I'm open to Head of Product and AI Agents Product Manager roles in Dubai, London or remote.
Shipped in production
- Automated digital worker for refunds and chargebacks: policy validation, ledger payout, CRM logging
- Fraud ML: -30% net fraud losses
- Recommendation engines: +8% average order value
- GCP streaming pipelines feeding live ML models
- MCP for secure tool orchestration
- Evaluation harnesses
- Enterprise RAG patterns
- Agentic workflows
Fig. 01 — Reference Architecture
AI Platform Stack
The layered system I design, evaluate, and lead across: from infrastructure and data foundations through orchestration to the applications customers actually use.
AI Applications
Where users meet intelligence
AI Orchestration
Context, retrieval, and control
Platform Layer
Interfaces teams build on
Infrastructure Layer
Runtime and scale
Data Layer
Signals, feedback, and truth
Evaluation & governance
No model ships without an evaluation gate, a cost and latency budget, and monitoring.
Tools I use daily
Claude, Cursor, Gemini, Lovable, n8n.
Case study
The User Pulse
8 specialist agents sharing one business-context layer.
92%
Answer accuracy
40%
Ticket reduction