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How AI Agents Really Work - Daniel Vassilev

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Manage episode 472662546 series 3515115
Content provided by Justin Norris. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Justin Norris or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

AI agents are everywhere in conversation right now—but what actually makes them work? It’s not just slapping a large language model into a workflow and calling it a day. Under the hood, real agentic systems operate differently. They make decisions. They adapt. They break out of rigid if-this-then-that logic and enter something closer to human judgment.

In this episode, I talk with Daniel Vassilev, co-founder of Relevance AI, a platform purpose-built for building and deploying true agents. We dig deep into how agentic systems are structured—from core instructions to tool orchestration—and how that foundation changes what’s possible. Daniel explains the difference between automation and autonomy in clear, practical terms that any builder, founder, or operator can understand.

We also explore real-world use cases: where agents shine today, where they fall short, and how teams are already using them to 10x output without ballooning headcount. Whether you’re dabbling in LLM workflows or ready to rethink how your company works entirely, this conversation will level up your mental model.

If you’ve been wondering where the hype ends and the real architecture begins—this is the episode.

About Today's Guest

Daniel Vassilev is Co-Founder and Co-CEO of Relevance AI, a platform to develop commercial-grade multi-agent systems to power your business. With a background in software engineering, he previously created, grew and monetised two apps to a combined 7 million users, reaching #1 on the App Store top free.

Key Topics

  • [00:00] - Introduction
  • [01:31] - Defining agentic AI
  • [03:28] - AI in linear workflows vs. agentic systems
  • [08:19] - How agents work under the hood
  • [11:24] - Always-on agents
  • [13:43] - Selecting the right tasks for agentic AI
  • [17:42] - Copilot vs. Autopilot
  • [22:44] - Are there tasks we should never delegate to AI?
  • [25:03] - Coolest use cases
  • [34:30] - Agent memory and continual improvement
  • [37:55] - Compounding effect of agent teams
  • [41:39] - Relevance the company and platform

Learn More

Visit the RevOps FM Substack for our weekly newsletter:

Newsletter

Disclosure: I am using an affiliate link for Relevance AI, which means I earn a small bonus if you sign up through my content.

  continue reading

59 episodes

Artwork
iconShare
 
Manage episode 472662546 series 3515115
Content provided by Justin Norris. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Justin Norris or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

AI agents are everywhere in conversation right now—but what actually makes them work? It’s not just slapping a large language model into a workflow and calling it a day. Under the hood, real agentic systems operate differently. They make decisions. They adapt. They break out of rigid if-this-then-that logic and enter something closer to human judgment.

In this episode, I talk with Daniel Vassilev, co-founder of Relevance AI, a platform purpose-built for building and deploying true agents. We dig deep into how agentic systems are structured—from core instructions to tool orchestration—and how that foundation changes what’s possible. Daniel explains the difference between automation and autonomy in clear, practical terms that any builder, founder, or operator can understand.

We also explore real-world use cases: where agents shine today, where they fall short, and how teams are already using them to 10x output without ballooning headcount. Whether you’re dabbling in LLM workflows or ready to rethink how your company works entirely, this conversation will level up your mental model.

If you’ve been wondering where the hype ends and the real architecture begins—this is the episode.

About Today's Guest

Daniel Vassilev is Co-Founder and Co-CEO of Relevance AI, a platform to develop commercial-grade multi-agent systems to power your business. With a background in software engineering, he previously created, grew and monetised two apps to a combined 7 million users, reaching #1 on the App Store top free.

Key Topics

  • [00:00] - Introduction
  • [01:31] - Defining agentic AI
  • [03:28] - AI in linear workflows vs. agentic systems
  • [08:19] - How agents work under the hood
  • [11:24] - Always-on agents
  • [13:43] - Selecting the right tasks for agentic AI
  • [17:42] - Copilot vs. Autopilot
  • [22:44] - Are there tasks we should never delegate to AI?
  • [25:03] - Coolest use cases
  • [34:30] - Agent memory and continual improvement
  • [37:55] - Compounding effect of agent teams
  • [41:39] - Relevance the company and platform

Learn More

Visit the RevOps FM Substack for our weekly newsletter:

Newsletter

Disclosure: I am using an affiliate link for Relevance AI, which means I earn a small bonus if you sign up through my content.

  continue reading

59 episodes

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