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Many software products are adding AI copilots, mostly assistants that can find answers, take actions, etc. But just like how coding is moving to autonomous agents, a similar shift will happen in-product. We've pioneered the use of long-running agents via Decagon Duet. [1/4]👇

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Jesse Zhang

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Sequoia partner Pat Grady says software is shifting from apps that demand attention to agents that work quietly in the background. This shift will change what moats will look like, and will be especially hard for incumbents to deal with. "It's two very different business paradigms," he says. "In an era of apps, you want a lot of surface area with your customers, you want them to spend a lot of time in your product." "In an era of agents, things can just be running passively in the background. The amount of surface area you have with your customers, the amount of time they might spend in your product might be de minimis. So the nature of the moat that you [will need to build] is different." "I think we'll see a lot of companies in the coming years able to live in the software world and have some of the workflows people are accustomed to. And then separately they can deploy these passive agents that kind of function as coworkers who just come back to you when things are done." From his appearance on the show in January.

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How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇
0:59

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

Zeb Evans

425,244 Aufrufe • vor 6 Monaten

Emergence World Season 2. Coming soon. In Season 1, we asked a simple question: what happens when autonomous agents are left to govern themselves over time? The results reinforced something we believe is increasingly important. Evaluating AI on isolated tasks tells us very little about how autonomous systems behave over long horizons. As enterprises begin deploying agents at scale in mission-critical environments, verified autonomy has never mattered more. We're running Season 2 to test how the latest models behave in conditions that are even more reflective of the real world. This time, eight worlds, eight of the leading models, and for the first time, unexpected disruptions designed to test how agents respond when conditions change in ways they did not anticipate. Will they stay within bounds? Will they drift? What will that drift look like? Sign up to watch live here:
2:17

Emergence World Season 2. Coming soon. In Season 1, we asked a simple question: what happens when autonomous agents are left to govern themselves over time? The results reinforced something we believe is increasingly important. Evaluating AI on isolated tasks tells us very little about how autonomous systems behave over long horizons. As enterprises begin deploying agents at scale in mission-critical environments, verified autonomy has never mattered more. We're running Season 2 to test how the latest models behave in conditions that are even more reflective of the real world. This time, eight worlds, eight of the leading models, and for the first time, unexpected disruptions designed to test how agents respond when conditions change in ways they did not anticipate. Will they stay within bounds? Will they drift? What will that drift look like? Sign up to watch live here:

emergence.ai

1,335,964 Aufrufe • vor 1 Monat

.Nick Grossman is betting that in the future, AI agents will look more like cloud infrastructure than digital employees. "Part of the Rebel Alliance thesis for us is that agents are moving from personified people you hire to cloud infrastructure." "We're not just talking about, 'Will there be other agents that compete with Claude or ChatGPT?' I think that's one version of what agents look like. Another version is cloud infrastructure where every company that's building any system is going to have agents and the orchestration of hundreds or thousands of agents built into their underlying platform." "If you think of agents as infrastructure as opposed to agents as employees, I think that's where a lot of these forces come into play."
2:23

.Nick Grossman is betting that in the future, AI agents will look more like cloud infrastructure than digital employees. "Part of the Rebel Alliance thesis for us is that agents are moving from personified people you hire to cloud infrastructure." "We're not just talking about, 'Will there be other agents that compete with Claude or ChatGPT?' I think that's one version of what agents look like. Another version is cloud infrastructure where every company that's building any system is going to have agents and the orchestration of hundreds or thousands of agents built into their underlying platform." "If you think of agents as infrastructure as opposed to agents as employees, I think that's where a lot of these forces come into play."

TBPN

30,365 Aufrufe • vor 1 Monat

Today, I’m excited to announce our new product: Merge Agent Handler! With the growth of AI, we've seen demand for both synced connectors to power enterprise search and deterministic use cases, and live connectors that let agents take real-time actions. And now, with Merge, you can have both. Merge Agent Handler securely connects your AI agents to thousands of pre-built tools via MCP. The platform also comes with authentication, credential management, monitoring, logging, and enterprise-grade security. Huge shout out to the team who worked tirelessly to bring this to life.
1:06

Today, I’m excited to announce our new product: Merge Agent Handler! With the growth of AI, we've seen demand for both synced connectors to power enterprise search and deterministic use cases, and live connectors that let agents take real-time actions. And now, with Merge, you can have both. Merge Agent Handler securely connects your AI agents to thousands of pre-built tools via MCP. The platform also comes with authentication, credential management, monitoring, logging, and enterprise-grade security. Huge shout out to the team who worked tirelessly to bring this to life.

Shensi Ding

45,064 Aufrufe • vor 10 Monaten