What I've Learned Testing Dozens Of AI Agents In 2025

The rapid advancement of AI is driving businesses to integrate AI agents into their operations. A recent IBM and Morning Consult survey revealed that a vast majority (99%) of enterprise AI developers are actively exploring or developing AI agent technologies. My SaaS company is also actively engaged in this space.

My daily work involves extensive testing and interaction with various AI agents, a practice I encourage within my teams. Here's a summary of my key findings and top agent picks for 2025.

Voice Agents: A Powerful Entry Point

Voice agents offer an accessible and impactful introduction to AI's capabilities. As a16z highlights in their 2025 AI voice agent update, voice interaction is poised to become the primary interface for many consumers, potentially revolutionizing access to services like language learning.

A significant advantage of voice agents is their ability to handle customer calls around the clock, eliminating the need for voicemail during off-peak hours and ensuring proactive lead follow-up.

VoiceOS stands out for its remarkably natural-sounding voice, creating a truly human-like interaction. Conversations feel fluid and dynamic, adapting to interruptions and allowing for seamless topic shifts. It's a versatile assistant capable of handling a broad range of inquiries.

Knowledge-Based and Persona-Based AI Agents: Enhanced Research and Collaboration

Imagine a research assistant with access to a comprehensive library—that's the power of knowledge-based AI agents. Text.cortex excels in this area, allowing users to build custom knowledge bases by uploading and categorizing documents. The agent then synthesizes information from these documents to provide answers with clear source attribution.

Text.cortex also enables the creation of persona-based agents, each with a unique role (e.g., marketing strategist, legal assistant). This feature simulates collaboration with a cross-functional team, all leveraging a shared knowledge base. A key benefit is the persistent memory of past interactions and uploaded content, unlike tools like ChatGPT, making it ideal for ongoing workflows.

Deep Research Agents: Transparency and In-Depth Analysis

Deep research agents, such as Gemini, provide a level of transparency not found in typical generative AI. Instead of simply providing answers, Gemini demonstrates its research process, showing the websites referenced (often hundreds) and the steps taken in real time. This is particularly valuable for complex research tasks involving academic papers and scientific data.

For instance, when tasked with compiling a list of 100 AI agents in customer service and categorizing them, Gemini delivered a comprehensive report titled "The Rise of Intelligent Automation: An Examination of AI Agents in Customer Service", complete with a categorized list including agent names, providers, and functions.

Automation Agents: Streamlining Repetitive Tasks

Automation agents excel at handling repetitive tasks, freeing up human time and resources. By combining machine learning, predictive analytics, and rule-based logic, these agents automate processes without human intervention, boosting productivity and reducing errors.

Reworkd's AgentGPT is a prime example, operating directly within a web browser. Users define the agent's name and goal, and the agent autonomously breaks down the goal into tasks, executes them, and monitors progress. This eliminates the need for micromanagement in tasks like data scraping and report compilation.

Conclusion: Practical Utility Defines the Future of AI Agents

As Reuters has noted, autonomous agents are rapidly gaining prominence in the AI landscape. Their value lies in their practical utility, unlike earlier AI models that excelled in specific, limited tasks. Today's agents are intelligent, adaptable, and, most importantly, genuinely helpful, enhancing human productivity and enabling focus on higher-level work.

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