Planning is a crucial aspect of agent-based systems, as it allows agents to reason about their actions, break down complex tasks into subtasks, and ensure a more reliable and coherent execution of their objectives. As Harrison Chase highlights, current language models are not yet capable of reliably performing this type of planning on their own, and developers often need to rely on external prompting strategies and cognitive architectures to enforce planning capabilities.

One of the key challenges is that language models tend to operate in a more reactive, "for-loop" manner, where they generate a response, execute an action, and then generate the next response. This can lead to suboptimal decision-making and a lack of long-term planning. Techniques like tree-of-thought, reflection, and subgoal decomposition aim to address this by giving the models the ability to reason about their actions, plan ahead, and break down complex tasks.

However, the long-term solution may require a fundamental shift in the underlying architecture of language models, moving beyond the current Transformer-based models to something that can inherently handle planning and reasoning more effectively. This is an area of active research, with projects like QAR (Question-Answering Reasoning) and models trained to "think slowly" showing promising results.

In the meantime, agent frameworks like Langchain play a crucial role in providing the necessary tools and infrastructure to enable planning capabilities, allowing developers to coordinate different models, give them access to various tools, and design consistent workflows. As the field of agents continues to evolve, the ability to plan and reason effectively will remain a key focus for both researchers and practitioners.

The user experience (UX) of agent applications is an area that Harrison is particularly excited about. He notes that the UX has not yet been "nailed" and that human-in-the-loop is still often necessary due to the unreliability of language models and the potential for hallucinations.

Harrison highlights the UX demonstrated in the Anthropic Delphi demo as a positive example, with the ability to see the various screens (browser, chat window, terminal, code) in one view. He also points to the value of having a "rewind and edit" capability, which allows users to go back to a previous state and make adjustments, improving the reliability and steering ability of the agent.

Additionally, Harrison discusses the importance of "flow engineering" - the explicit design of the workflow and state machine that the agent operates within. He suggests that this flow engineering can help offset some of the limitations of the language models themselves, by offloading the planning and decision-making to the human engineers upfront.

Overall, Harrison emphasizes that the UX of agent applications is a critical area that is still evolving, with a need to balance automation and human oversight to ensure consistency, reliability, and quality. Agent frameworks like Langchain can help provide the necessary tools and capabilities to develop effective agent-based applications.

Agents are powerful tools that go beyond just complex prompts. One of the key aspects that makes agents so capable is their ability to leverage memory, both short-term and long-term.

Short-term memory allows agents to learn and improve during a conversation or interaction, building on previous steps and adjusting their approach accordingly. This enables a more dynamic and adaptive interaction, where the agent can be steered and corrected by the user.

Long-term memory, on the other hand, is crucial for agents to maintain and utilize a company's knowledge base. This allows agents to have a deep understanding of the business, its processes, and relevant information, making them more effective in their tasks. However, managing long-term memory comes with its own challenges, such as determining what to store, when to forget, and how to evolve the memory as the business changes.

Integrating both short-term and long-term memory into agent frameworks is an active area of research and development. As these capabilities continue to improve, agents will become increasingly reliable, personalized, and valuable in enterprise settings, where consistency and quality are paramount.

The key points from Harrison Chase's talk on agents are:

1. Agents are more than just complex prompts - they have access to various tools, memory (short-term and long-term), and the ability to plan and take actions.

2. Planning is a crucial aspect of agents, as it allows them to reason about the steps needed to complete a task. However, current language models struggle with reliable planning, leading to the use of external prompting strategies. The future may require new architectures beyond just transformers to enable better planning capabilities.

3. The user experience (UX) of agent applications is an area of excitement. Techniques like allowing users to rewind and edit the agent's actions can improve reliability and give users more control. Balancing human-in-the-loop and automation is an ongoing challenge.

4. Memory, both short-term and long-term, is essential for agents to learn and personalize their interactions. Procedural memory (remembering how to do something) and personalized memory (remembering facts about the user) are important features being explored.

Overall, the talk highlights the current state and future potential of agents, emphasizing the need for advancements in planning, UX, and memory to make agents more reliable and useful in real-world applications.