Brand Visibility Analyzer: How AI Search Engines Rank Your Brand

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

Brand Visibility Analyzer: How AI Search Engines Rank Your Brand

Alex and Maya introduce the Brand Visibility Analyzer, now live on the Agentic AI Showcase. IN THIS EPISODE: * The new battleground: AI search engines recommend different brands in different order * Live demo: ABVS scoring, competitive ranking, citation gaps, keyword analysis * Architecture: Strands Graph on AgentCore Runtime with conditional back-edge * Who it is for: SaaS companies building AI search analytics, or enterprises doing it in-house Note: This episode features AI-generated voices. Alex and Maya are synthetic hosts produced using ElevenLabs text-to-speech. Content is written by Abhijeet Patil.

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]