STP216IntermediateStartups Playbook 5 live updates

Building AI Agents: From Open-Source Frameworks to Production-Grade

What this session is about

AI agents are moving from demo to deployment. Startups across ANZ are building production-grade assistants using open-source orchestration frameworks, fine-tuned foundation models, and GPU-accelerated inference on AWS and NVIDIA infrastructure. This panel explores what it actually takes to ship agentic use casesfrom choosing the right models and frameworks to managing latency, cost, and reliability at scale. We'll hear from AirTree VC on where the investment thesis is heading, from NVIDIA on how accelerated compute is shaping the agent stack, and from Heidi Health building and scaling these systems in production. Whether it's vertical agents for healthcare, customer support, or code generation, we'll focus on what's working, what's hype, and where the real startup opportunities lie in the agent ecosystem.

Playbook

Editorial commentary · what to actually do about this on Monday

The concept
Production-grade agents using open-source orchestration, fine-tuned models, GPU acceleration on AWS + NVIDIA. Panel: AirTree VC + NVIDIA + Heidi Health.
Why it matters
Open-source agent stacks are maturing. Vendor-locked stacks now have credible alternatives.
The hard parts
Open-source = self-hosted ops. Most startups can't afford the overhead.
Playbook moves
(1) Use managed services until scale or differentiation justifies self-hosted. (2) Don't optimise prematurely. (3) Monitor your inference cost trajectory; the crossover point sneaks up.
The surprise
VC investment thesis is shifting from "agent capability" to "agent vertical depth." Generic agents are commoditising fast; domain-specific agents have moats. If you're raising for a generic agent platform in 2026, you're raising in a saturated market. ---

Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.

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