Kansikuva näyttelystä Building Enterprise AI

Building Enterprise AI

Podcast by Agentic Coders

englanti

Teknologia & tieteet

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Lisää Building Enterprise AI

Building Enterprise AI is a podcast about domain-specific LLMs, fine-tuning, and deploying AI systems in the enterprise. Hosted by Agentic Coders, each episode covers hands-on experience with agentic AI, LoRA and QLoRA training, deployment architectures on AWS, and the business case for machine intelligence. Real practitioners share what broke and how they fixed it. No hype. No theory.

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3 jaksot

jakson Cross-Platform A2A: Salesforce AgentForce + AWS Bedrock AgentCore kansikuva

Cross-Platform A2A: Salesforce AgentForce + AWS Bedrock AgentCore

Alex and Maya break down cross-platform agent-to-agent collaboration: the Strategy Hub on AWS Bedrock AgentCore now coordinates with a Salesforce AgentForce agent using federated identity (Cognito JWT + OAuth2). Topics covered: - Cross-platform A2A: one orchestrator, multiple platforms - Live demo: Revenue Optimizer + Guest Experience + Group Sales agents working together - AgentCore Registry: governed discovery for agents, MCP servers, and skills - Federated identity: Cognito JWT for internal, OAuth2 for external - Full strategic assessment generated in 243 seconds Part of the Agentic AI Showcase series. https://agenticcoders.dev #CrossPlatformA2A #AgenticAI #AWSBedrock #SalesforceAgentForce #FederatedIdentity

12. touko 2026 - 7 min
jakson Why Your Enterprise Needs Its Own LLM — Fine-Tuning for $15 kansikuva

Why Your Enterprise Needs Its Own LLM — Fine-Tuning for $15

Alex and Maya break down Abhijeet Patil's hands-on account of building a domain-specific LLM for the enterprise — not the theory, not the hype, but the real, things-broke-and-I-fixed-them story. IN THIS EPISODE: * The agentic AI storm — 88% of organizations are using AI, but nearly two-thirds haven't scaled it (McKinsey 2025) * The Pluribus Syndrome — why foundation models are like a hive mind that sacrifices depth for breadth * The CIO's dilemma — data privacy is the #1 barrier to enterprise AI adoption (IBM, 57% of organizations) * RAG vs. fine-tuning — reference books vs. muscle memory * Why specialists win — DeepSeek's distilled 7B model scored 92.8% on MATH-500 where GPT-4o managed 74.6% * The overqualification trap — over-qualified personnel and overfitting are probably the same term * The experiment — four phases, from everything broken to 95% accuracy * The $15 moment — QLoRA fine-tuning on a single GPU with ~1,000 curated examples * The 77% gut punch — why data quality dominates data quantity, every time This is Part 1 of a multi-part series. Part 2 will reveal the specific industry use case. Read the full article: agenticcoders.dev [https://agenticcoders.dev/articles/domain-specific-llms-part1.html]

1. helmi 2026 - 9 min
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