GeekWire Calendar
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AI Agents in the Wild: Building, Scaling & Deploying in Production

Join us for a high-signal evening with senior leaders from GitHub, Salesforce, and AWS who are at the forefront of shipping and scaling production-grade AI agents.
π
Date: August 12th
π Location: Bellevue City Hall
### ποΈ Agenda
4:30 PM β 5:30 PM
Check-in & Networking
5:30 PM β 6:00 PM
Matt Nigh β Program Manager Director, AI (GitHub)
Topic: TBA
6:00 PM β 6:30 PM
Ran Fu β Director of AI Agent Product Management, Salesforce
Topic: Post Day 1: How to Optimize Agents with Evals and Session Traces, After Building Them from 0 to 1
6:30 PM β 7:00 PM
Raj Ganesh Jayaraman β Senior GenAI Solutions Architect, AWS
Topic: AI Agents in Production and Its Challenges
7:00 PM β 8:00 PM
Networking
### Why Attend
- Most teams have built an agent demo. Very few have one running reliably in production. This event is for those closing that gap.
- Understand what evaluation frameworks actually look like in practice β not just theory, but the traces, metrics, and iteration loops that drive real agent improvement.
- Go beyond the initial build and learn why optimization, observability, and session tracing are the real differentiators once agents are live.
- Discover how leading teams at GitHub, Salesforce, and AWS are solving for scalability, reliability, and cost in production AI agent deployments.
- Learn how developer workflows are being reshaped by agentic systems β and what that means for teams building on or alongside platforms like GitHub.
- Get a clear view of where most agent projects stall β not at the model level, but at deployment, evaluation, and architectural decision points.
### Who Should Attend
- Professionals building, deploying, or scaling AI agents and agentic workflows
- Teams exploring production-ready AI systems across GitHub, Salesforce, and AWS ecosystems
- Individuals interested in observability, tracing, monitoring, and evaluation frameworks for AI applications
- Practitioners moving AI solutions from prototype to enterprise production environments
- Organizations looking to improve the reliability, scalability, and performance of AI-driven systems
- Builders and innovators exploring real-world use cases for autonomous and intelligent AI applications
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