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How AI Agents Really Work - Daniel Vassilev

Listen to this episode from RevOps FM on Spotify. 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 platformLearn MoreVisit 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.



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How AI Agents Really Work - Daniel Vassilev

https://open.spotify.com/episode/60pT9ZwlwG8kLivyxa3b4p

Listen to this episode from RevOps FM on Spotify. 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 platformLearn MoreVisit 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.



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https://open.spotify.com/episode/60pT9ZwlwG8kLivyxa3b4p

How AI Agents Really Work - Daniel Vassilev

Listen to this episode from RevOps FM on Spotify. 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 platformLearn MoreVisit 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.

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