A sample eval matrix for financial-services voice AI agents

<p>Your agent tests pass. Your monitoring says "green."</p> <p>Meanwhile, your agent just hallucinated a refund policy, leaked a customer's SSN, and burned $2,847 in a token

<h2> Introduction </h2> <p>Most LLM tutorials assume you start from nothing. A blank project. A clean architecture. No constraints. No legacy code. No deployment history. No

<p>Your agent passed all the tests. It's been running in production for three weeks. And it has been quietly wrong the entire time.</p> <p>This is not a hypothetical. Galileo's

<p>Most developers who build AI agents make the same mistake: they spend weeks designing the orchestration layer, tuning the system prompt, and picking the right model — then hand

<!-- SC_OFF --><div class="md"><p>I have built a programmable governance layer for AI agents. I am considering to open source completely. Looking for feedback.</p> <p>Agent demos

<p><em>Originally published as a <a href="https://antengage.com/products/fonix/multilingual-voice-bot" rel="noopener noreferrer">Fonix.AI practical guide</a> by AntEngage. This

<h1> Google Remy and Meta Hatch: The Technical Architecture Behind 24/7 Personal AI Agents </h1> <p>Two big AI agent stories broke this week that every developer building on top

<p>I spent <strong>3-4 weeks</strong> building an AI voice agent that cold-calls HVAC owners for me. Real outbound. Real objection handling. Real appointments booked. When we