Last week, I attended a fascinating event at the AGI House in San Francisco, where some of the brightest minds in AI gathered to discuss and demo the latest in autonomous agents. The timing couldn't have been better - we're at an inflection point where language models are becoming capable enough to reliably plan and execute complex tasks.

If you're new to agents, think of them as AI systems that can take actions in the real world (or at least, the digital parts of it). While a language model can tell you how to book a flight, an agent can actually go book it for you. While GPT-4 can explain how to analyze a dataset, an agent can fire up a Jupyter notebook and do the analysis itself.

The Agent Stack

Let's break down the key components that make up an agent:

1. A language model for reasoning and planning
2. Tools the agent can use (APIs, functions, etc.)
3. Memory to maintain context and learn from past actions
4. A feedback loop to improve performance

The language model acts as the "brain," deciding what actions to take. The tools are like hands and feet - ways for the agent to actually do things in the world. Memory helps the agent learn and adapt, while feedback helps it get better over time.

<figure> <img src="/img/agent-stack.webp" alt="The Agent Stack diagram showing components" /> <figcaption className="text-sm text-muted-foreground mt-2 text-center"> A simplified view of the agent stack </figcaption> </figure>

Why Now?

We've had chatbots and automation tools for years. What makes agents different? Three key developments:

1. Language models are getting smarter: GPT-4 and its peers can now reliably break down complex tasks into steps and reason about how to accomplish them.

2. Tool use is becoming standardized: OpenAI's function calling, Langchain's tools, and other frameworks make it easier for models to interact with external systems.

3. Memory systems are improving: Vector databases and other techniques let agents maintain context and learn from experience.

The Demo Day

The event showcased several impressive agent demos:

Personal Assistant Agents

• Calendar management
• Email triage
• Meeting scheduling
• Travel booking

Development Agents

• Code generation
• Bug fixing
• Documentation writing
• Testing

Research Agents

• Data analysis
• Literature review
• Report writing
• Citation management

<figure> <img src="/img/agent-demo.webp" alt="Screenshot of an agent performing a task" /> <figcaption className="text-sm text-muted-foreground mt-2 text-center"> An agent autonomously debugging code </figcaption> </figure>

Key Insights

Several themes emerged from the discussions:

1. Reliability is Critical

Agents need to be reliable to be useful. A personal assistant that occasionally books flights to the wrong city or a development agent that introduces subtle bugs is worse than no agent at all.

2. Control and Oversight Matter

Users need to be able to understand and control what agents are doing. This means:

• Clear logging of actions
• Ability to approve/reject actions
• Understanding of agent reasoning
• Easy ways to correct mistakes

3. Specialization vs. Generalization

There's an ongoing debate about whether to build specialized agents for specific tasks or more general-purpose agents. The consensus seems to be that specialized agents are more practical in the short term, but the long-term goal is more general capability.

Looking Forward

The agent ecosystem is evolving rapidly. Here are some trends to watch:

1. Better Tool Integration: More standardized ways for agents to interact with external systems

2. Improved Safety: Better ways to constrain and control agent behavior

3. Multi-Agent Systems: Agents that can work together to accomplish complex tasks

4. Learning from Feedback: More sophisticated ways for agents to improve from user interactions

Getting Started

If you're interested in building agents, here are some resources to check out:

1. LangChain - A popular framework for building agent applications
2. AutoGPT - An experimental open-source agent
3. BabyAGI - A simple but powerful agent architecture

Conclusion

Agents represent a significant step forward in AI capability. While language models gave us powerful tools for understanding and generating text, agents give us ways to take action in the world. The demos at AGI House showed that we're moving from proof-of-concept to practical applications.

The key challenge now is building agents that are reliable, controllable, and actually useful in real-world scenarios. It's an exciting time to be working in this space, and I'm looking forward to seeing how the technology evolves.

Thanks to the AGI House team for hosting, and to all the builders who shared their work. Special thanks to Harrison Chase, Yohei Nakajima, and others for their insights during the discussions.