What’s an Al agent?

It's like having a robot butler that doesn't judge you for binge-watching reality TV.

What Are AI Agents?

• Why Read:

  • This article provides a nuanced overview of the current state of AI agents, highlighting their potential and challenges in AI agent development.

• Featuring:

  • Ron Miller (@ron_miller),Enterprise Reporter at TechCrunch

• Link:

  • “What exactly is an AI agent?”

Key Concepts and Tactics:

• Understanding the Lack of Consensus on AI Agents:

  • Point: Recognize that there is no universal definition of AI agents, which can lead to confusion in the industry.

  • "AI agents are supposed to be the next big thing in AI, but there isn't an exact definition of what they are. To this point, people can't agree on what exactly constitutes an AI agent."

• Grasping the Basic Concept of AI Agents:

  • Point: Understand the fundamental idea behind AI agents as AI-powered software that performs tasks traditionally done by humans.

  • "At its simplest, an AI agent is best described as AI-fueled software that does a series of jobs for you that a human customer service agent, HR person or IT help desk employee might have done in the past, although it could ultimately involve any task."

• Recognizing the Potential of AI Agents:

  • Point: Understand that AI agents have the potential to become more capable over time, performing increasingly complex tasks.

  • "Aaron Levie, co-founder and CEO at Box, says that over time, as AI becomes more capable, AI agents will be able to do much more on behalf of humans, and there are already dynamics at play that will drive that evolution."

• Considering the Challenges in AI Agent Development:

  • Point: Be aware of the difficulties in creating truly autonomous AI agents, especially in handling contingencies and cross-system operations.

  • "The problem is that crossing systems is hard, and this is complicated by the fact that some legacy systems lack basic API access. While we are seeing steady improvements that Levie alluded to, getting software to access multiple systems while solving problems it may encounter along the way could prove more challenging than many think."

• Understanding the Need for AI Agent Infrastructure:

  • Point: Recognize the importance of developing a specific tech stack for creating and supporting AI agents.

  • "Jon Turow, a partner at Madrona Ventures, says this is going to require the creation of an AI agent infrastructure, a tech stack designed specifically for creating the agents (however you define them)."

• Recognizing the Current State of AI Agents:

  • Point: Understand that while AI agents show promise, they are still in a transitional phase and require further advancements.

  • "While what we've seen so far is clearly a promising step in the right direction, we still need some advances and breakthroughs for AI agents to operate as they are being envisioned today. And it's important to understand that we aren't there yet."

How AI Agents May Change Software Pricing

Why Read:

• This article explores how the rise of AI agents will challenge traditional SaaS pricing models, offering founders insights on innovative pricing strategies to gain a competitive advantage

Featuring:

• Tomasz Tunguz (@ttunguz) , General Partner at Theory Ventures

• Link:

• “No SaaS! How AI Agents Will Change Software Pricing”

Key Concepts and Tactics:

• Understanding the Impact of AI Agents on Software Pricing:

  • Point: Recognize that AI agents' productivity will challenge traditional SaaS pricing models.

  • "In a world where AI agents are 2.5-3x as productive as humans, which would parallel mechanical robots, how does a software company price?"

• Considering Price Increases:

  • Point: Evaluate the possibility of increasing per-seat prices to reflect AI agents' higher productivity.

  • "Triple the per seat price : If the AI agent is 3x as productive as a human, the software company could charge 3x as much per seat. This would be a significant increase in price, but the value of the software would be much higher."

• Exploring Usage-Based Pricing:

  • Point: Consider transitioning to usage-based pricing models similar to database pricing.

  • "Move to usage-based pricing : Jamin makes the case that AI software will be priced like databases since the AI is using the database directly. Just as databases charge for compute, AI agents will charge for compute."

• Implementing Performance-Based Pricing:

  • Point: Explore charging based on outcomes achieved by AI agents.

  • "Pay for performance : Some AI companies are exploring charging for outcomes. If an AI agent replaces an SDR who is compensated for meetings, then why not charge this way?"

• Recognizing the Strategic Opportunity:

  • Point: Understand that new pricing models could provide a competitive advantage for startups.

  • "But for the first time since Slack started offering billing on active seats, new pricing models provide a strategic option to startups looking to compete with incumbents."

• Anticipating a Potential "No-SaaS" Movement:

  • Point: Be prepared for potential disruption of the traditional SaaS model.

  • "Maybe we'll see a No-SaaS rebel replicate Marc Benioff's playbook."

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