Uploaded May 2026 | Updated September 2026, 2 weeks ago
Ever wondered how Salesforce built an end-to-end agentic AI platform that handles everything from creative writing to deterministic financial tasks in a single runtime? In this episode of How to Build Like AWS, we sit down with the developers behind Agentforce to unpack how they built agents that manage the full spectrum — from open-ended creative tasks to precise, rule-based workflows — and why fine-tuning open source models on AWS gives you the "free lunch" of faster, cheaper, and more accurate results. You'll see a live demo building a travel agent using Agentforce Labs, learn how SageMaker HyperPod and Bedrock power their training and inference pipeline, and discover why token explosion in agentic workloads demands a smarter approach to model hosting and cost management.
🔥 What you'll learn:
How Agentforce handles deterministic, blended, and fully agentic workflows in one runtime
Why fine-tuning smaller open source models beats frontier models on cost, latency, and performance
Real customer story: processing 10M resumes/year with only 4% human involvement
Live demo: building agents with Agentforce Labs and Agentforce Harness
How AWS SageMaker HyperPod and Bedrock fit into production agentic AI pipelines
Observability and trust layers for enterprise agent deployments
👥 This stream is for developers, ML engineers, and solution architects building agentic AI applications who want to understand how to balance creativity and determinism in their agent designs while optimizing for cost and performance at scale.
🔔 Subscribe and hit the bell so you never miss a live build session!
👉 Join the AWS community: linkedin.com/groups/13187185 👉 Follow the host: linkedin.com/in/awssteph 👉 Explore more episodes: builder.aws.com/content/32rxm1CXklSWkuM4ZgFwKkwh0Zo/how-to-build-like-aws-live-stream
#AgenticAI #Salesforce #Agentforce #AWS #FineTuning #SageMaker #Bedrock #LLM #AIAgents #MachineLearning #OpenSourceAI #HowToBuildLikeAWS #AWSLivestream #GenerativeAI #MLOps #AgentScript #HyperPod #AIEngineering #DeveloperContent #BuildOnAWS
Ever wondered how Salesforce built an end-to-end agentic AI platform that handles everything from creative writing to deterministic financial tasks in a single runtime? In this episode of How to Build Like AWS, we sit down with the developers behind Agentforce to unpack how they built agents that manage the full spectrum — from open-ended creative tasks to precise, rule-based workflows — and why fine-tuning open source models on AWS gives you the "free lunch" of faster, cheaper, and more accurate results. You'll see a live demo building a travel agent using Agentforce Labs, learn how SageMaker HyperPod and Bedrock power their training and inference pipeline, and discover why token explosion in agentic workloads demands a smarter approach to model hosting and cost management.
🔥 What you'll learn:
How Agentforce handles deterministic, blended, and fully agentic workflows in one runtime
Why fine-tuning smaller open source models beats frontier models on cost, latency, and performance
Real customer story: processing 10M resumes/year with only 4% human involvement
Live demo: building agents with Agentforce Labs and Agentforce Harness
How AWS SageMaker HyperPod and Bedrock fit into production agentic AI pipelines
Observability and trust layers for enterprise agent deployments
👥 This stream is for developers, ML engineers, and solution architects building agentic AI applications who want to understand how to balance creativity and determinism in their agent designs while optimizing for cost and performance at scale.
🔔 Subscribe and hit the bell so you never miss a live build session!
👉 Join the AWS community: linkedin.com/groups/13187185 👉 Follow the host: linkedin.com/in/awssteph 👉 Explore more episodes: builder.aws.com/content/32rxm1CXklSWkuM4ZgFwKkwh0Zo/how-to-build-like-aws-live-stream
#AgenticAI #Salesforce #Agentforce #AWS #FineTuning #SageMaker #Bedrock #LLM #AIAgents #MachineLearning #OpenSourceAI #HowToBuildLikeAWS #AWSLivestream #GenerativeAI #MLOps #AgentScript #HyperPod #AIEngineering #DeveloperContent #BuildOnAWS
