AI Agents Explained: Insights from Dharmesh Shah and Andrew Ng for Enterprise Leaders
Hey there, C-suite superhero! 👋 If you've been anywhere near a tech conversation lately, you've probably heard "AI agents" thrown around more times than "synergy" at a management retreat. Google's shouting about Gemini 2.0 as the model for the agentic era, the World Economic Forum's writing fancy papers about it, and your LinkedIn feed is probably flooded with hot takes.
But let's cut through the noise and get to the good stuff, shall we? We're going to learn from two actual tech wizards - Dharmesh Shah (HubSpot's CTO) and Andrew Ng (the guy who's basically the Yoda of AI). No jargon, no hype, just the real deal.
What's an AI agent? (And why should you care?)
Think of traditional AI chatbots as one-hit wonders - they do their thing and... that's it. AI agents, on the other hand, are more like your most dedicated employees: they plan, execute, check their work, and improve it. They're the overachievers of the AI world! 🌟
Let's break it down with a simple example:
- Regular AI: "Here's your report, boss!" drops mic and leaves
- AI Agent: "Here's draft one... let me review that... hmm, could be better... let me fix these points... how about now?"
It's like having a team member who never gets tired of perfecting their work. (And never raids the office coffee supply! 😉)
Need some concrete numbers to compare regular AI vs AI agents? Check out their respective performance on HumanEval, a coding benchmark!
Making AI Agents even more performative
Here's where it gets fun. These digital workers can be supercharged with a few design patterns:
- Tool use: Like giving them a Swiss Army knife of digital tools (web search, calculators)
- Planning: They actually think before they act (unlike some humans we know...)
- Multi-agents: Multiple personalities, but not in a creepy way! Research has shown that if you prompt the same model to roleplay as different roles (writer, editor, proofreader), the sum of those parts is better than using a single role.
Real world magic- what will agents look like in the workforce?
We turn to Dharmesh Shah, CTO of Hubspot. At INBOUND 2024, Dharmesh highlights 2 existing use cases of agents that are already available on agent.ai.
- Conversion Rate Optimiser agent
Remember those endless hours tweaking website copy and CTAs? This agent does it faster than you can say "A/B testing." Simply input the site into the agent, and it will suggest areas for improvement. It's like having a marketing team working 24/7, minus the caffeine addiction.
- Company Research Agent
Imagine having someone who could read everything about a company - website, Crunchbase, Glassdoor, news - and give you the highlights in minutes. It's like having the world's fastest research assistant who never complains about information overload.
Your turn- the possibilities are endless!
Time to put on your innovation hat! What soul-crushing repetitive tasks are eating up your team's time? Those are prime candidates for your first AI agents.
Pro tip: Start small and internal. Think of it like introducing a new intern - you wouldn't put them directly in front of your biggest client on day one, right? Begin with internal processes where a few hiccups won't cause PR nightmares. E.g. use an agent for research on a prospect, but not to do cold outreach without supervision.
The Future is Here (Kind of)
Are AI agents perfect? Nope! They're like that smart high school valedictorian - incredibly capable but occasionally prone to face-palm moments. But they're learning fast, and the smart money is on figuring out how to use them now.
Ready to dip your toes in the future? Start small, think big, and remember: these agents are here to help your team work smarter, not replace them. They're more like R2-D2 - helpful sidekicks making everyone's job easier - than The Terminator!
What repetitive tasks would you love to delegate to your new digital workforce? Let's hear your ideas in the comments! 🚀
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