Behind the Numbers: Next-Gen AI – From Assistants to Autonomous Agents and Beyond

Podcast: Behind the Numbers: an EMARKETER Podcast
Host: Marcus
Guests: Dan Van Dyke (VP of Gen AI, New York) and Jacob Bourne (Technology Analyst, California)
Release Date: April 18, 2025

Introduction

In the April 18, 2025 episode of Behind the Numbers, host Marcus engages with EMARKETER’s VP of Gen AI, Dan Van Dyke, and technology analyst Jacob Bourne to delve into the future of Artificial Intelligence (AI). The discussion centers around the evolving landscape of AI agents, their definitions, applications, challenges, and the anticipated trajectory of their adoption in various industries.

Fun Facts Segment

The episode kicks off with a light-hearted exchange of “facts of the day” between Dan and Jacob:

• Dan Van Dyke shares an intriguing fact about sloths:

  "[Dan, 01:25]"
  "Sloths will only use the bathroom once a week, excreting one-third of their body weight, and they risk their lives by coming down from trees to bury their waste."

• Jacob Bourne responds humorously:

  "[Jacob, 01:53]"
  "Interesting. Slow metabolism, I guess."

• Marcus adds his own fact about national flags, noting the striking similarities between various countries’ flags and the lack of creativity in their designs. This includes examples like Chad and Romania’s nearly identical flags and the similarities between Indonesia and Monaco’s flags.

The segment concludes with laughter over the competitive nature of sharing facts, highlighting the engaging and personable dynamic between the hosts and guests.

Main Discussion: The Dawn of AI Agents and AI Native Companies

Defining AI Agents

The conversation transitions to the core topic: AI agents. Marcus references Isabel Busquet's definition from the Wall Street Journal, highlighting that AI agents are systems capable of performing actions on behalf of humans, such as purchasing groceries or making reservations. However, the definition remains vague in some contexts.

• Dan Van Dyke elaborates on his understanding:

  "[Dan, 04:52]"
  "An AI agent is a tool that can take action based on a predefined task with autonomy and use tools. There's a spectrum from basic chatbots like ChatGPT to more robust, classical agents."

• Jacob Bourne adds to this by distinguishing between general AI chatbots and AI agents:

  "[Jacob, 05:51]"
  "It's about the level of autonomy. While chatbots require prompting for every task, AI agents can act independently towards a goal without step-by-step instructions."

Variations in Definitions

Marcus brings in additional perspectives to the discussion, citing Tom Kosho, senior analyst at Gartner, and Robert Blumoff, CTO at Akamai Technologies, who view AI agents as either assistive or autonomous based on their decision-making capabilities and the need for human direction.

• Dan Van Dyke acknowledges the evolving definitions:

  "[Dan, 07:25]"
  "The term 'agent' is murky and evolving. For instance, ChatGPT’s ability to search the web or generate images blurs the lines of what defines an agent."

• Jacob Bourne likens the situation to autonomous vehicles, suggesting that unlike the well-defined levels of autonomy in cars, AI agents lack a standardized classification system:

  "[Jacob, 08:47]"
  "There's a disconnect between the limitless vision of AI and the current technological capabilities. Terms like 'agent' are incremental steps towards more complex, human-like AI functionalities."

Examples of AI Agents

The guests discuss various existing AI agents across major tech companies:

• Amazon: Bedrock agents through the cloud.

• Google: Vertex AI Agent Builder and Agent Space with autonomous coding capabilities.

• OpenAI: Operator for browsing and booking tasks.

• Oracle: Clinical AI agent for healthcare.

• Nvidia: Agentic AI Blueprints for custom agent creation.

• Microsoft Salesforce: Agentforce, among others.

• Jacob Bourne emphasizes the diversity:

  "[Jacob, 13:49]"
  "From Amazon to Google, and beyond, numerous platforms are emerging. Industry-specific agents are also gaining traction."

Interoperability and Standards

Addressing the interoperability of these agents, the discussion highlights efforts to create standardized protocols:

• Dan Van Dyke introduces the Model Context Protocol (MCP) by Anthropic and the A2A Protocol by Google:

  "[Dan, 14:55]"
  "MCP allows agents to access tools like GitHub or Zapier, while the A2A Protocol enables agents to communicate in a common language. However, the ecosystem remains fragmented with ongoing consolidation anticipated."

• Jacob Bourne concurs:

  "[Jacob, 14:37]"
  "Interoperability is part of the vision, but it's not fully realized yet. The market is crowded and competitive."

Challenges in Building and Deploying AI Agents

The conversation shifts to the practical challenges of developing and deploying AI agents:

• Dan Van Dyke discusses the complexity beyond prototyping:

  "[Dan, 17:07]"
  "Building a proof of concept is easy, but deploying agents to fulfill real organizational needs requires rigorous evaluation. Achieving high accuracy and handling unforeseen tasks involves a protracted process of iteration and testing."

• Jacob Bourne highlights the heightened risks:

  "[Jacob, 19:16]"
  "AI agents that perform transactions carry higher stakes. Ensuring accuracy to prevent errors is critical, making deployment more challenging compared to simple chatbots."

• Marcus introduces Greg Shoemaker’s perspective on treating AI agents as digital workers:

  "[Marcus, 19:58]"
  "Companies should approach agents as digital workers that need onboarding and training rather than mere tech deployments."

• Dan Van Dyke reflects on the technical proficiency required:

  "[Dan, 20:52]"
  "Building and maintaining AI agents demands significant technical expertise. While the learning curve is decreasing with better tools, deploying reliable agents is still a challenging and time-consuming process."

• Jacob Bourne notes advancements like Google Cloud’s no-code Agent Designer:

  "[Jacob, 23:25]"
  "Tools are emerging to lower the technical barriers, allowing non-technical users to develop their own agents."

Adoption Rates and Future Outlook

The final segment assesses the current adoption of AI agents and forecasts future trends:

• Marcus cites a Gartner webinar statistic where only 6% of 3,400 participants reported deploying AI agents:

  "[Marcus, 23:49]"
  "This low adoption rate might reflect the nascent stage of true agent deployment."

• Dan Van Dyke predicts gradual increases:

  "[Dan, 24:48]"
  "By the end of 2025, adoption could rise to 10-20% as more platforms emerge and internal experts begin building agents."

• Jacob Bourne adds nuance to the statistics:

  "[Jacob, 25:46]"
  "True agent adoption is low, but AI assistants labeled as agents are more common. As technology improves, clearer distinctions and higher adoption rates are expected."

Conclusion

The episode concludes with Marcus thanking Dan and Jacob for their insights and referencing related episodes featuring Henry Powderly and Garcia Sevilla discussing AI at work. The guests express optimism about the growth and maturation of AI agents, anticipating increased adoption and technological advancements in the coming years.

Notable Quotes:

• Dan Van Dyke [01:25]:
  "Sloths will only use the bathroom once a week, excreting one-third of their body weight, and they risk their lives by coming down from trees to bury their waste."

• Dan Van Dyke [04:52]:
  "An AI agent is a tool that can take action based on a predefined task with autonomy and use tools."

• Jacob Bourne [05:51]:
  "It's about the level of autonomy. AI agents can act independently towards a goal without step-by-step instructions."

• Dan Van Dyke [07:25]:
  "The term 'agent' is murky and evolving. For instance, ChatGPT’s ability to search the web or generate images blurs the lines of what defines an agent."

• Jacob Bourne [08:47]:
  "There's a disconnect between the limitless vision of AI and the current technological capabilities."

• Dan Van Dyke [14:55]:
  "MCP allows agents to access tools like GitHub or Zapier, while the A2A Protocol enables agents to communicate in a common language."

• Dan Van Dyke [17:07]:
  "Deploying AI agents to fulfill real organizational needs requires rigorous evaluation and a protracted process of iteration and testing."

This comprehensive summary encapsulates the key discussions and insights from the episode, providing a clear understanding of the current state and future prospects of AI agents for those who have not listened to the podcast.