Question 1

How do chained AI models create better workflows than simple generative AI tools?

Accepted Answer

_Building dynamic workflows by connecting AI models in sequence._

Chained AI models connect multiple models together so the output of one becomes the input for the next, creating dynamic workflows rather than fixed steps. For an email assistant, the system first analyzes the email and figures out needed steps, then checks the calendar, looks up past emails, and predicts likelihood of attending. It creates three draft replies with different options and presents them to the user. This approach enables system integration, context search through RAG, predictions using traditional AI, and user control through multiple options.

— [AI Agents In 2025: What Enterprise Leaders Need To Know](https://www.forbes.com/sites/lutzfinger/2025/01/05/ai-agents-in-2025-what-enterprise-leaders-need-to-know/) · _Forbes_

Question 2

How does jamming affect drone operations, and why does it make autonomy necessary?

Accepted Answer

_Communication denial forces drones to decide without waiting for human orders._

Jammers emit white noise across frequencies to drown out communication. GPS satellite signals go first because they are weak. Your connection to home base gets jammed second because you are far away and the jammer is closer. But drones close together, only hundreds of meters apart, can still communicate with each other because the nearest jammer is kilometers away. You have to assume information from home base arrives intermittently at best. This is why drones must make autonomous decisions. They cannot wait for human instructions that may never arrive.

— [AI on the Front Lines, with Alexander Fink](https://www.lutzfinger.com/transcript/2026-04-30-ai-on-the-front-lines-with-alexander-fink/) · _The Keynote on AI_

Question 3

What competitive challenges will OpenAI face in 2025?

Accepted Answer

_Tech giants with data and customers challenge OpenAI's technology bet._

OpenAI will face serious competition in 2025 from Salesforce, Microsoft, Google and Amazon, all of which would like to dominate the AI agent market. These competitors have access to closed datasets and customer bases. OpenAI is betting on its ability to outperform everyone else with superior technology. That's why Altman confidently claims we'll see AGI. OpenAI's strategy relies on technological superiority rather than the market advantages like proprietary data and established customer relationships that its competitors possess.

— [Sam Altman Says, 'We Know How To Build AGI' — Not So Fast](https://www.forbes.com/sites/lutzfinger/2025/01/07/sam-altman---agi--ai-workforce-in-2025-the-battle-of-tech-giants/) · _Forbes_

Question 4

What does the term agents actually mean in AI, and is the technology working?

Accepted Answer

_Agents has three definitions but the technology isn't quite there yet_

Agents is one of the most diluted terms, almost useless. There are three working definitions. First, a virtual agent representation that helps with support tickets. Second, the technical definition where you close the control loop with AI so output becomes input, doing multiple iterations without humans to solve higher level tasks, like booking a ticket. Those aren't really working yet. The tech isn't quite there, and these elements are very unpredictable for complex tasks. Third, any time an LLM calls out to a tool, like sending email. The term itself is almost useless, but the things it's talking about are very real trends.

— [The Future of AI, with Martin Casado](https://www.lutzfinger.com/transcript/2025-03-20-the-future-of-ai-with-martin-casado/) · _The Keynote on AI_

Question 5

What does true swarming mean, and how is it different from choreography?

Accepted Answer

_Real swarms have no leader; each drone decides independently._

Some companies have drones flying in formation or following a leader, self-healing formations that rearrange when members are shut down. I consider that choreography, not actual swarming. True swarming means every single member of the group knows everything about all the other members and makes its own independent decisions. There is no leader, no single point of failure. They all know the objectives and negotiate among themselves who does what to achieve it. It is a swarm where they are all equal partners, just with different capabilities.

— [AI on the Front Lines, with Alexander Fink](https://www.lutzfinger.com/transcript/2026-04-30-ai-on-the-front-lines-with-alexander-fink/) · _The Keynote on AI_

Question 6

What is a reward engineer and why does Richard predict this will be an important new job type?

Accepted Answer

_Defining AI success requires a new kind of manager and skill set._

A reward engineer is someone who defines what success looks like when delegating tasks to AI. Richard predicts this will be a main new job type because AI can be very smart but needs unambiguous instructions. For example, if you tell an AI to increase customer satisfaction scores, it might create bots to spam five-star ratings or give thousand-dollar gift certificates for ten-dollar problems. Reward engineers will need to think through all the ways to properly define success, refine rewards, and become better at delegation and managing AI with appropriate trust and verification.

— [The Future of AI, with Richard Socher](https://www.lutzfinger.com/transcript/2026-02-05-the-future-of-ai-with-richard-socher/) · _The Keynote on AI_

Question 7

What is the practical framework for implementing AI agents in enterprise settings?

Accepted Answer

_Five steps to deploy AI agents that create business value._

The framework I recommend has five essential steps. First, define the business objective to ensure you are solving a real problem. Second, collect and clean the data needed to power the workflow. Third, develop the agentic workflow by chaining AI models appropriately. Fourth, test with actual users to validate the solution works. Fifth, create a feedback loop for continuous improvement. Most importantly, the focus must remain on value creation throughout the entire process, not just on implementing technology for its own sake.

— [AI Agents In 2025: What Enterprise Leaders Need To Know](https://www.forbes.com/sites/lutzfinger/2025/01/05/ai-agents-in-2025-what-enterprise-leaders-need-to-know/) · _Forbes_

Question 8

What kind of AI agents will actually enter the workforce in 2025?

Accepted Answer

_Specialized workflow agents, not AGI, will join teams in 2025_

In 2025, we'll see more AI agents entering the workforce, transforming workflows by simplifying, enhancing, and automating tasks across industries. These won't be all encompassing AGI models but smaller, specialized models designed for dedicated workflows. AI will scale and improve processes one step at a time, combining traditional AI, context retrieval called RAG, and robust user design. Soon, there will be job websites with posts that only AI agents can apply for. 2025 will be the year AI colleagues enter our lives.

— [The Race For AI Agents. Who Will Supply Tomorrow's Workforce?](https://www.forbes.com/sites/lutzfinger/2025/01/07/the-race-for-ai-agents-who-will-supply-tomorrows-workforce/) · _Forbes_

Question 9

What level of autonomy does Swarmar allow in target engagement, and where do humans decide?

Accepted Answer

_Humans mark targets; drones autonomously track and engage them._

Humans decide what needs to be done. They set the mission, the targets, the rules of engagement, when to abort, and what to do in every fallback scenario. Once drones launch, they execute according to what humans decided. In our seek and hit missions, surveillance drones find targets and send video to the operator. The operator marks the target. Then terminal guidance and tracking while attack drones are incoming is done autonomously by the drones themselves. The human decision was the thing at these coordinates at this timestamp is your target.

— [AI on the Front Lines, with Alexander Fink](https://www.lutzfinger.com/transcript/2026-04-30-ai-on-the-front-lines-with-alexander-fink/) · _The Keynote on AI_

Question 10

What makes autonomous AI agents different from traditional automation tools like RPA?

Accepted Answer

_How AI agents differ from traditional automation like RPA_

Traditional RPA is basically scripting with rules the machine follows, without much deviation except for human input. It's a structural, human-made approach. Autonomous agents are different because they use large language models as an interface that understands human language instead of requiring rigid API calls or workflows. This means I don't need to design specific engineering interfaces. The language model can interact with tools like calculators, internet searches, or order engines using natural language, breaking down tasks iteratively rather than following predetermined scripts.

— [Connecting LLMs to the world: The possibilities of the feedback loop](https://www.lutzfinger.com/transcript/2023-05-02-connecting-llms-to-the-world-the-possibilities-of-the-feedback-loop/) · _The Edge_

Question 11

What should enterprise leaders expect from AI agents in 2025?

Accepted Answer

_Expect many workflow improvements, not one transformative application._

2025 will be the year of AI agents. Rather than expecting one killer app, leaders should anticipate AI simplifying, enhancing, or automating many smaller workflows across industries. We will see applications in areas like customer care, legal support, and sales. The key shift is moving from isolated generative AI experiments to properly integrated agentic workflows that chain multiple AI models together. These workflows will have system integration, context through RAG, appropriate guardrails, and user control, making them viable for production enterprise use.

— [AI Agents In 2025: What Enterprise Leaders Need To Know](https://www.forbes.com/sites/lutzfinger/2025/01/05/ai-agents-in-2025-what-enterprise-leaders-need-to-know/) · _Forbes_

Question 12

Why are coding applications seeing the biggest success with AI agents right now?

Accepted Answer

_Coding succeeds because compilers provide clear feedback loops_

We see the biggest success in coding because there's a clear feedback loop. When I ask an AI to write code, it can check if the code compiles. If it doesn't work, ChatGPT critiques that and the next step is to rewrite the code including the critique. This iterative loop continues till it actually works. The combination of knowing what was put in previously through memory, plus the ability to talk to a compiler, plus the iteration workflow creates a loop to generate good code. It's more difficult when the task isn't very clear or doesn't have such obvious success criteria.

— [Connecting LLMs to the world: The possibilities of the feedback loop](https://www.lutzfinger.com/transcript/2023-05-02-connecting-llms-to-the-world-the-possibilities-of-the-feedback-loop/) · _The Edge_

Question 13

Why do we need new AI tooling platforms for quality control and orchestration?

Accepted Answer

_Managing AI requires new platforms for quality control, security, and orchestration._

Whenever you get something new in the toolbox, you need to figure out how to maintain, manage, and set it up. AI is like a wild animal you have to tame, especially for enterprise applications where you want certain output and quality levels. You need observability to understand what direction the model is heading and quality gates so output doesn't do anything bad. There's also orchestration needed because you might chain multiple steps: taking a question, making five questions from it, doing retrieval, controlling with cosine similarity, merging results. These are different steps, partially non-large language models, that need orchestration across different agents and models.

— [Cherry's Investment Thesis on AI](https://www.lutzfinger.com/transcript/2024-07-19-cherrys-investment-thesis-on-ai/) · _The Edge_

Question 14

Why do you say the website is dead, and what does that mean for brands?

Accepted Answer

_Websites lose traffic but gain higher intent visitors via agents._

It's hyperbolic. The website is not dead. The newspaper is not dead, the homepage is not dead. It's only reduced in importance. An agent can go look up websites, read those websites, do the research, compare it, create a metric for themselves to say what is the most explosive or most fruitiest and then give me back an answer. Your traffic goes down, but your revenue probably doesn't because you should see more high intent consumers. Consumers that used an agent to be informed and then they come to your website.

— [From Google to Cornell: Rethinking AI Workflows | Lutz Finger | Build AI Podcast 17](https://www.lutzfinger.com/transcript/2026-04-16-from-google-to-cornell-rethinking-ai-workflows-lutz-finger-build-ai-podcast-17/) · _Build AI_

Question 15

Why does Salesforce believe it can compete in supplying AI agents to enterprises?

Accepted Answer

_Salesforce leverages its treasure trove of enterprise data for AI_

Salesforce holds an enormous trove of valuable data, including customer interactions, discussions, process documents, and marketing strategies. Access to enterprise data is one of two things companies need to become the main supplier of AI agents. Salesforce CEO Marc Benioff took a jab at Microsoft's Copilot, calling it disappointing and dubbing it Clippy 2.0, before introducing Salesforce's own AI solution, Agent Forces. This data advantage positions Salesforce alongside Microsoft, Amazon, and Google as a major player.

— [The Race For AI Agents. Who Will Supply Tomorrow's Workforce?](https://www.forbes.com/sites/lutzfinger/2025/01/07/the-race-for-ai-agents-who-will-supply-tomorrows-workforce/) · _Forbes_

Question 16

Why is memory the next AI battleground, not prompts or agentic systems?

Accepted Answer

_AI memory systems create new risks and control challenges._

Memory is essentially part of our context window. Models now start to keep information about our memory. I asked ChatGPT about using AI in student interviews and it scolded me, saying as a faculty member I should be extremely careful. But even worse, it noted down in my memory that I wanted to use that tool. That's pretty much a good idea for a big disaster. How do I as an organization ensure that my memory is kept in the correct form? What is the memory you want to capture?

— [From Google to Cornell: Rethinking AI Workflows | Lutz Finger | Build AI Podcast 17](https://www.lutzfinger.com/transcript/2026-04-16-from-google-to-cornell-rethinking-ai-workflows-lutz-finger-build-ai-podcast-17/) · _Build AI_

Question 17

Why is Microsoft considered to be in pole position in the race for AI agents?

Accepted Answer

_Why workforce tool dominance puts Microsoft ahead in AI agents_

Microsoft is in pole position because it has direct access to users through workforce tools most people already use. AI is becoming deeply integrated into these Microsoft products, with Copilots appearing everywhere. This market access is a big advantage. In 2023 and 2024, many startups launched impressive AI services, only to be quickly overshadowed by giants like Microsoft and Google, which have direct access to customers. For example, Jasper.ai saw similar features built directly into Google and Microsoft products.

— [The Race For AI Agents. Who Will Supply Tomorrow's Workforce?](https://www.forbes.com/sites/lutzfinger/2025/01/07/the-race-for-ai-agents-who-will-supply-tomorrows-workforce/) · _Forbes_

Question 18

Why will 2025 be the year of enterprise search for AI?

Accepted Answer

_Enterprise search becomes critical as AI agents need company data_

AI needs data to be effective. What we need are tools that can read and summarize company documents, tailored for enterprise use. 2025 will be the year of search, especially enterprise search. Companies that have access to this kind of data, like Microsoft, Amazon, Google, and Salesforce, are major players in the race to supply AI agents. Access to enterprise data is one of the two critical things companies need to become the main supplier of AI agents for tomorrow's workforce.

— [The Race For AI Agents. Who Will Supply Tomorrow's Workforce?](https://www.forbes.com/sites/lutzfinger/2025/01/07/the-race-for-ai-agents-who-will-supply-tomorrows-workforce/) · _Forbes_

Question 19

You've been building AI-driven products for over 20 years at companies like Google, LinkedIn, and Snap. How do you view the current hype around agentic AI compared to past technology revolutions?

Accepted Answer

_Agentic AI is fundamentally an interface innovation, not magic_

History is a good guide here. We've been through this before with data, then mobile. I was at LinkedIn during the mobile revolution when we essentially stopped the whole company to move everything to mobile. Now with agentic AI, it's important to understand what it actually is. It's essentially an interface. It's the moment of the mouse, but now you can use language to describe what you want. We live through a time when analytics were done with SQL. Startups tried semantic text mining and failed, going back to SQL. Now we have a better interface where you can have agents that search, shop, or analyze for you.

— [Deep Dive Into The Current And Future State Of Ai With Lutz Finger](https://www.lutzfinger.com/transcript/2025-04-24-deep-dive-into-the-current-and-future-state-of-ai-with-lutz-finger/) · _The Pulse of AI_

Q&A, agents

How do chained AI models create better workflows than simple generative AI tools?

How does jamming affect drone operations, and why does it make autonomy necessary?

What competitive challenges will OpenAI face in 2025?

What does the term agents actually mean in AI, and is the technology working?

What does true swarming mean, and how is it different from choreography?

What is a reward engineer and why does Richard predict this will be an important new job type?

What is the practical framework for implementing AI agents in enterprise settings?

What kind of AI agents will actually enter the workforce in 2025?

What level of autonomy does Swarmar allow in target engagement, and where do humans decide?

What makes autonomous AI agents different from traditional automation tools like RPA?

What should enterprise leaders expect from AI agents in 2025?

Why are coding applications seeing the biggest success with AI agents right now?

Why do we need new AI tooling platforms for quality control and orchestration?

Why do you say the website is dead, and what does that mean for brands?

Why does Salesforce believe it can compete in supplying AI agents to enterprises?

Why is memory the next AI battleground, not prompts or agentic systems?

Why is Microsoft considered to be in pole position in the race for AI agents?

Why will 2025 be the year of enterprise search for AI?

You've been building AI-driven products for over 20 years at companies like Google, LinkedIn, and Snap. How do you view the current hype around agentic AI compared to past technology revolutions?