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An AI agent testing checklist is the set of checks you run before, during, and after an agent goes live, covering whether it does the right thing, whether it keeps doing the right thing under real traffic, and whether anyone finds out when it stops. TLDR:

An outbound AI voice agent is software that places phone calls on a business's behalf, using speech recognition and generative AI to hold a real conversation instead of playing a fixed script. This guide walks through setting up an outbound AI voice agent

DronaHQ, an enterprise application and agent platform built by Deltecs Infotech Pvt Ltd, today announced the general availability of DronaHQ MCP, a Model Context Protocol server that gives AI coding agents secure, governed access to production infrastruct

Post-call analysis is the set of steps that run after a voice agent hangs up, capturing the transcript, pushing relevant data into business tools, extracting structured fields from the conversation, and feeding that data into a dashboard for analysis. TLD

Every demo sounds convincing. The agent speaks fluently, handles the sample objection, and signs off warmly. Then you try to picture it on your actual call volume, your actual customers, your actual compliance obligations, and the confidence starts to wob

Easily turn any data source or API you manage into a fully compliant MCP server to use with your AI tools, agents, and LLMs.

Enterprise interest in voice AI has moved quickly over the past two years, from early pilots to wider production use across customer service, banking, healthcare, and retail. Alongside that growth, the terminology has shifted too. Vendors, analysts, and e

A support agent answers a customer's question confidently. The answer is wrong. The right document was in the knowledge base. The retrieval system found it. But the chunk that reached the language model had been split mid-paragraph, cutting off the condit

What most articles do not cover is the part that matters most in enterprise environments: what happens after the demo. How do you govern the decisions an agent makes? Who approves its outputs? What happens when it is wrong? How do you produce a full audit

Enterprise AI is mired in "agent sprawl," with no answer to the CFO's core question: "What is our cost per outcome?" A DronaHQ co-founder argues that the solution requires a structural shift to a platform that exposes the true economics of every agentic j