AI Agents — this is what’s coming next // in the next couple of years, maybe even sooner
There’s no secret formula to make it happen // just agentic datasets, just more HQ-datasets, datasets… and tools
>User: Is it time to go all in on AI-Agents?
>Agent: Definitely!
Google DeepMind CEO Demis Hassabis discussed the next steps in achieving Artificial General Intelligence (AGI), a hypothetical AI system that mimics human abilities. Hassabis stated that the next phase of AI development after chatbots will be “AI agents” that can:
1. Plan: Think ahead and make decisions.
2. Act: Take action in the real world.
3. Reason: Think through problems and apply logic.
4. Have better memory: Recall details and information.
5. Be personalized: Understand user preferences and characteristics.
6. Use tools: Utilize hardware, software, or other AI systems.
Hassabis emphasized that achieving AGI that can reason like humans is still about a decade away, as there is no secret formula to get there. He also pointed to DeepMind’s AI, AlphaGo, as an example of an AI that has demonstrated reasoning skills in a specific domain.
AI-agents frameworks: crewai, langgraph, autogen, swarm, etc.
Decagon, a leading innovator in AI customer support agents, has raised $100 million, quadrupling its valuation in a few months, through a Series B funding round led by Bain Capital Ventures. The funding will accelerate Decagon’s mission to transform enterprise customer support and unlock new levels of economic productivity.
Decagon’s AI agent sets itself apart from others by driving tangible productivity gains, capturing complex business logic, and innovating around the nuances of large companies’ support operations. Industry leaders such as Duolingo, Notion, and Eventbrite use Decagon’s AI agents to manage customer interactions, achieving significant efficiency improvements and 90% resolution rates in some cases.
Decagon’s technology automates repetitive tasks, resolves routine queries, and reduces response time pressure, freeing customer service representatives to focus on high-value positions requiring analytical and technical skills. The company believes that AI agents should be transparent, giving customer support teams full control to update, train, and build on the AI agents’ capabilities.
The funding will be used to expand Decagon’s engineering team, accelerate its go-to-market strategy, and expand into new verticals and modalities like voice. Decagon aims to lead the way in the future of work, where AI not only automates tasks but also helps businesses build more productive and resilient workforces.
